1. health and fitness
2. disease

Heart Failure with Preserved Ejection Fraction-Determinants and
Predictors of Mortality, Hospitalization and Quality of Life (Analysis
from a Large Heart Failure Registry)
LIU, Ming
A Thesis Submitted in Partial Fulfillment
of the Requirements for the Degree of
Doctor of Philosophy
in
Medical Sciences
The Chinese University of Hong Kong
August 2012
DECLARATION OF ORGINALITY
I hereby declare that all studies that are contained in this thesis are original research
carried out by the author in the Division of Cardiology, Department of Medicine and
Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong. No
part of the thesis has been submitted to other universities or institutions for a degree or
diploma.
i
ACKNOWLEDGEMENT
I wish to express my deepest gratitude to my supervisor, Professor Cheuk-Man YU,
for his giving me the precious opportunity to study in the Chinese University of Hong
Kong; for his guidance, encouragement, patience, understanding and support over the
years. His profound knowledge, dedicated attitude about research, creative and
constructive advice, and patient guidance helped greatly in the completion of this
thesis.
I also appreciate my co-supervisor, Prof. Bryan P Yan for his invaluable suggestions
and guidance through my study as well as revision of my thesis. I am also filled with
gratitude to Prof. John E. Sanderson, for his great help with the study design and the
comments and revision of the manuscripts. My project would not be possible without
his support.
I am deeply indebted to all professors in the cardiology team for their support and
advice in my study: Prof. Alex PW Lee, Prof. YY Lam, Prof. Jing Ping Sun, Prof.
Andrew Coats, Prof. Gabriel WK Yip, Prof. Qing Zhang, Prof. Fang Fang and Ms.
Joey Kwong. Thanks to Dr. CP Chan from the bottom of my heart for originating and
supplying patients’ data for the study.
I appreciate Ms. Mang Zhang, Dr. Rui-Jie Li and Dr. Ming Dong for collecting some
of patients’ data and Ms. Kidius Tam for the work of follow-up in the study. I also
ii
appreciate the work of Ms. Yong-Na Wen and the research team for screening patients.
In addition, thanks for Ms. Xue-Ting Wang and Joyce Wong for entering patients’
data for the study. Thanks for Ms. Ka-Wai Chan and Ms Abbie Yip for doing
questionnaires.
I also wish to take the opportunity to thank the colleagues and friends in the cardiac
team for their substantial support and warmly friendship: Dr. Jun-Min Xie, Dr. Qing
Shang, Dr. Friendy Xiu-Xia Luo, Dr. Shang Wang, Ms. Leata Yeung, Ms. Skiva Chan,
Ms. Soey Ou Yang, Ms. Olivia To, Ms. Mei-Ling Yip, Ms. Jenny Yip, Ms. Tracy Lam,
Ms Miho Yu, Ms. Sheung Poon, Dr. Nancy Huang and Dr. Jing Wang. Thanks Ms.
Wenmy Poon, Ms. Angel Ho, Ms. Amelia Iu n the cardiac admin team and Ms.
Wing-Man Wong, Ms. Carmen Chiu, Ms. Wendy Lau and Ms. Shirley Wong in the
Department of Medicine & Therapeutics, the Chinese University of Hong Kong for
dealing with submitting abstracts and paper, dealing with issues related to my Ph.D.
program.
Last but not least, I would like express my deepest gratitude to my parents, for their
endless patience, understanding, support and love in all my life. I would also express
my deepest thanks to my girl friend - Dr. Song Li, for her consistent understanding,
tolerance, help and care over the years.
Finally, thanks to all patients who participated in our study to make all this possible.
iii
LIST OF ABBREVIATIONS
A = transmitral flow velocity with atrial contraction;
a′ = velocity of mitral annulus motion with atrial systole;
ACEI,=angiotensin-converting enzyme inhibitor;
ADHERE=Acute Decompensated Heart Failure National Registry;
Adur = duration of A;
ALB=serum albumin;
ALT=alanine aminotransferase;
AR = flow from left atrium to pulmonary veins during atrial contraction;
AUROC=area under ROC
ARB=angiotensin II receptor blockers;
ARdur = duration of AR;
BMI=body mass index
BNP=brain natriuretic peptide
BP=blood pressure
BUN=blood urea nitrogen;
CAD=coronary artery disease;
CCB=Calcium channel blockers.
CI= confidence interval
Cr=creatinine;
CVD=primary cerebrovascular disease;
iv
CVP=central venous pressure
D = diastolic;
DT = deceleration time;
DBP=diastolic blood pressure
DM=diabetes mellitus;
E = early diastolic transmitral flow velocity;
e′ = velocity of early diastolic mitral annular motion;
ECM=extracellular matrix
EF=ejection fraction
eGFR=estimated glomerular filtration rate;
Hb=hemoglobin;
HF=heart failure
HFREF=heart failure with reduced ejection fraction
HFPEF=heart failure with preserved ejection fraction
HHD=hypertensive heart disease
HR=hazard ratios
HRQoL=Health related quality of life
ICD-9-CM=International Classification of Diseases, Ninth Revision, Clinical
Modification
IQR=inter-quartile ranges
LV=left ventricular
LA=left atrium
v
LVEF=left ventricular ejection fraction
LVH=left ventricular hypertrophy
MDRD=Modification of Diet in Renal Disease formula
MMPs=matrix metalloproteinases
NT-proBNP=N-terminal pro-BNP
NYHA= New York Heart Association;
PH=Pulmonary hypertension
RAS= renin–angiotensin system
ROC=receiver operating characteristics
RV=right ventricle
S = systolic
SBP=systolic blood pressure;
TDI=Tissue Doppler Imaging
TIMPs=Tissue inhibitors of matrix metalloproteinases
VVC=ventricular–arterial stiffening
vi
PUBLICATIONS
Full Paper:
1. Liu M, Chan CP, Yan BP, Zhang Q, Lam YY, Li RJ, Sanderson JE, Coats AJ, Sun
JP, Yip GW, Yu CM. Albumin levels predict survival in patients with heart failure and
preserved ejection fraction. Eur J Heart Fail. 2012;14(1):39-44.
vii
Abstracts
1. M. Liu, Alex PW Lee, CM. Yu, et al. Risk stratification for 1 year mortality in
patients with heart failure and normal ejection fraction. ESC Congress 2012 (Oral
Presentation)
2. Liu, M; Zhang, Q; Yan, BP; Lam, YY; Li, RJ; Sanderson, JE; Sun, JP; Chan,CP;
Yip, GWK; Chan, JYS; Wu, EB; Chan, A; Chan, K; Lee, APW; Yu, CM. Increase in
Prevalence but Improved Outcome for Heart Failure with Preserved Ejection Fraction
in the Last Decade. CIRCULATION Volume: 124 Issue: 21
Supplement:
S Meeting Abstract: A12293 Published: NOV 22 2011
3. M. Liu, GWK. Yip, CM. Yu, et al. Albumin levels predict survival in patients with
heart failure and normal ejection fraction. ESC Congress 2011 (Poster Presentation)
4. Liu M, Yip GWK, Chan CP, Yan BP, Zhang Q, Lam YY, Li RJ, Sanderson JE, Yu
CM. Effectiveness of a disease management program for heart failure patients with
preserved ejection fraction. European Heart Journal Volume: 31 Supplement: 1 Pages:
728-728 Published: Sep 2010
viii
ABSTRACT
Recently, many studies have found that many patients presenting with clinical heart
failure (HF) had a left ventricular ejection fraction in the normal range. This entity has
been termed “heart failure with preserved ejection fraction (HFPEF).” Previous
studies have indicated that patients who have HFPEF tend to be older, female, and to
have a history of hypertension.
However, little was known about the clinical outcome and related predictors of
HFPEF patients in Chinese population. Long term quality of life (QOL) after
treatment in HFPEF patients have not been well studied, especially in very elderly
HFPEF. Furthermore, there has been no a risk score used HFPEF patients.
We studied 847 HFPEF patients who were prospectively enrolled into a HF Registry
from 2006 to 2010 at a teaching hospital. In addition, a historical cohort of patients
admitted in our hospital from 2001 to 2005 was retrospectively retrieved and data
searched using the ICD-9-CM code 428. Among this, 170 with HFPEF were selected
for study. To adjust for the impact of baseline differences between the 2 cohorts on
clinical outcomes, we calculated a propensity score. To establish a risk score, HFPEF
patients were randomly divided into derivation group and validation group. We got a
risk score from the derivation group and then checked in the validation one. QOL was
assessed by the Minnesota Living with Heart Failure Questionnaire (MLHFQ)
ix
instruments.
Main findings of our study included:
1. 1-year survival rates improved (65.5% vs. 76.9%, p=0.001) and HF
re-hospitalization rates decreased (50.6% vs. 33.3%, p<0.001 in HFPEF patients
admitted between 2001-2005 and 2006-2010, respectively). The improvement in
1-year survival (68.1% vs. 78.9%, p=0.02) and HF re-hospitalization (51.2% vs.
34.3%, p=0.002) remained significant after propensity score matching.
2. Baseline (30±16 vs. 28±16 vs. 29±16, p=0.87) and 12-months (15±14 vs. 16±14 vs.
15±12, p=0.92) MLHFQ score showed no significant differences with advancing age.
Proportion of patients who experienced improvement in QOL at 12-months were
similar among age groups (84.0% vs. 80.2% vs. 87.5%, p=0.68).
3. Six independent prognostic factors were identified, and each was assigned a number
of points proportional to its regression coefficient: hypoalbuminemia (5 points), not
use of CCB (3 points), history of HF (2.5 points), history of CVD (2.5 points),
BUN>10mmol/L (2.5 points), age>78 years (2 points). We calculated risk scores for
each patient and defined three risk groups: low risk (0 to 5.5 points), intermediate risk
(6 to 10.5 points) and high risk (11 to 17.5 points). In the derivation cohort, the 1-year
mortality rates for these three groups were 10.5%, 22.3%, and 48.7% respectively. In
the validation cohort, the corresponding mortality rates were 15.4%, 25.3% and 39%.
4. Hypoalbuminemia was the most powerful predictor of 1 year mortality for HFPEF
patients.
x
In summary, we found that the mortality of HFPEF patients in the first year decreased
over time. Elderly HFPEF patients experienced similar improvements in QOL
compared to younger ones. The clinical based risk score can be used to predict
mortality of HFPEF patients. Hypoalbuminemia was the most powerful predictor of 1
year mortality for HFPEF patients.
xi
中文摘要
近年，研究發現許多心臟衰竭患者的左室射血分數在正常範圍內。這種類型的
心臟衰竭，已被稱為“射血分數保持的心臟衰竭（HFPEF）”。研究還發現，HFPEF
患者往往是老年女性，有高血壓病史，其預後比射血分數降低的心衰更好。
然而，很少人研究過中國人中HFPEF患者的死亡率。同時，經治療后HFPEF患
者長期的生活質量是否改善沒有得到很好的研究，特別是在老年HFPEF患者中。
此外，到目前為止，一直沒有一個風險評分系統用於預測HFPEF患者的預後。
我們從2006年至2010年在一所大學附屬醫院建立的心臟衰竭注冊研究中，前瞻
性納入了847 名HFPEF的患者進行研究。此外，我們通過國際疾病分類第九版
臨床修正（ICD-9- CM）代碼428進行數據檢索，回顧性分析了2001年至2005年
入住我院的心臟衰竭的患者。其中170名射血分數超過50％的患者納入本研究。
爲了消除兩組病人基線差異對臨床終點的影響，我們計算出傾向性得分。在建
立風險評分方面，所有HFPEF患者隨機分為推導組和驗證組。從推導組中，我
們得到了風險評分，然後我們再在驗證組中測試評分系統是否可行。本研究中，
生活質量是通過明尼蘇達州心力衰竭問卷（MLHFQ）進行評估。
我們研究的主要發現包括：
1、與2001-2005年納入的HFPEF患者比，2006-2010年納入的HFPEF患者，一年
生存率有顯著提高（76.9％比65.5％，P = 0.001），心臟衰竭的再次住院率也顯
xii
著下降（33.3％比50.6％，P <0.001）。傾向得分匹配調整後1年生存率提高（78.9
％比68.1％，P = 0.02）和心衰再次住院率降低（34.3比％51.2％，P = 0.002）仍
然顯著。
2、各個年齡組基線（32±16比30±15比34±11，P = 0.12）和12個月（16±14比16±12
比19±13，P = 0.62）的MLHFQ得分均沒有顯著。HFPEF患者12個月時生活質量
得到改善的比例在年齡組之間相似（84.0％比80.2％比87.5％，P = 0.68）。
3、我們通過Cox多因素回歸分析得到了了6個獨立的預測HFPEF患者1年死亡率
的預後因素。每個因素根據其回歸系統獲得一個分數：低蛋白血症（5分），不
使用鈣通道阻滯劑（3分），充血性心臟衰竭病史（2.5分），腦血管疾病病史（2.5
分），尿素氮> 10mmol / L（2.5分），年齡> 78歲（2分）。每一個患者根據風
險分數而被分為三個危險人群：低風險（0至5.5分），中等風險（10.5分）和高
風險（11至17.5分）。在推導隊列，這三組的1年死亡率分別為10.5％，22.3％和
48.7％分別。在驗證隊列，相應的死亡率分別為15.4％，25.3％和39％。
4、低蛋白血症為HFPEF患者1年死亡率的最有力的預測指標。
綜上所述，我們發現，近年來，HFPEF患者一年的死亡率和心臟衰竭再次住院
率有所下降。與相對年輕的HFPEF患者相比，老年HFPEF患者經歷了類似的生
活質量的改善。從臨床常用的變量得到的風險評分可用於預測HFPEF患者1年死
亡率。低蛋白血症為HFPEF患者1年死亡率的最有力的預測指標。
xiii
Table of Contents
Declaration of originality..............................................................................................i
Acknowledgement....................................................................................................... ii
List of abbreviations………………………………………………...………………..iv
Publications………………………………………………………………………….vii
Full paper…………………………………………………………………….......vii
Abstracts………………………………………………………………………..viii
Abstract………………………………………………………………………………ix
中文摘要…………………………………………………………………………xii
Table of Contents…………………………………………………………………xiv
List of Tables………………………………………………………………………xx
List of Figures……………………………………………………………………xxi
SECTION I LITERATURE REVIEW………………………………………………1
CHAPTER 1 DEFINITION, PATHOPHYSIOLOGY AND DIAGNOSIS OF
HFPEF……………………………………………………………………………......1
1.1 Definition of HFPEF……………………………………………………………2
1.2 Pathophysiology of HFPEF……………………………………………………..3
1.2.1 Structure abnormality in HFPEF…………………………………………...3
1.2.2 Diastolic dysfunction in HFPEF……………………………….………......10
1.2.3 Systolic function in HFPEF……………………………………………......12
1.2.4 Left atrial dysfunction in HFPEF…………………………………………14
1.2.5 Peripheral factors in HFPEF……………………………………………....15
xiv
1.3 Diagnosis of HFPEF…………………………………………………………….16
1.3.1 Clinical features…………………………………………………….…..….17
1.3.2 Echocardiographic features of HFPEF patients…………………………..18
1.3.3 BNP AND N-pro BNP assays……………………………...……………..18
CHAPTER 2 EPIDEMIOLOGY OF HFPEF……………………………………...28
2.1 Prevalence of HFPEF among HF patients……………………………………..28
2.2 Demographic features and comorbid conditions………………………………29
2.2.1 Age………………………………………………………..………………30
2.2.2 Gender…………………………………………………………..………...31
2.2.3 Hypertension……………………………………………………..……….31
2.2.4 Coronary artery disease……………………………………………..…….32
2.2.5 Atrial fibrillation………………………………………………………..…33
2.2.6 Diabetes Mellitus…………………………………………………………34
2.2.7 Renal Dysfunction…………………………………………………….......34
2.2.8 Body Mass Index…………………………………………………………..35
2.2.9 Anemia……………………………………………………………………..35
2.2.10 Chronic Obstructive Pulmonary Disease……………………………...…..35
2.3 Mortality of HFPEF patients……………………………………………………36
2.3.1 Mortality rates…………………………………………………………….36
2.3.2 Pattern of death……………………………………………………………37
2.4 Prognostic predictors……………………………………………………………38
2.5 Health related quality of life in HFPEF patients………………………………..40
xv
CHAPTER 3 TREATMENT OF HFPEF PATIENTS……………………………..42
3.1 Non-pharmacologic Therapy……………………………………………………42
3.2 Medical and Surgical Therapy…………………………………………………..43
3.2.1 Clinical Studies……………………………………………………………43
3.2.2 Randomized Controlled Clinical Trials…………………………………...43
3.2.3 Current Therapeutic Recommendations…………………………………..45
Conclusions.............................................................................................................46
SECTIONS II STUDIES ABOUT HFPEF…………………………………………47
CHAPTER 4 OBJECTIVES AND HYPOTHESIS………………………………...47
4.1 Objectives of the study…………………………………………………………..47
4.2. Hypothesis……………………………………………………………………....48
CHAPTER 5 METHODOLOGY…………………………………………………...49
5.1 Patient population……………………………………………………………….49
5.2 Definition of HFPEF patients…………………………………………………...49
5.3 Baseline patient data…………………………………………………………….50
5.4 Echocardiogram…………………………………………………………………50
5.5 Health related quality of life assessment……………………………………..…51
5.6 Follow-up and clinical outcome……………………….………………………..51
5.7 Statistical analysis……………………………………………………………….52
CHAPTER 6 IMPROVED 12 MONTH SURVIVAL OF PATIENTS ADMITTED
WITH HFPEF OVER THE LAST DECADE………………………………………54
6.1 Introduction………………………………………………………………….….54
xvi
6.2 Methods…………………………………………………………………………54
6.21 Patient population………………………………………………………….54
6.2.2 Baseline patient data…………………..……………………………………55
6.2.3 Study endpoints……………………….……………………………………56
6.2.4 Statistical analysis…………………….……………………………………56
6.3 Results………………………………………………………………………...…57
6.3.1 Baseline patient characteristics…………..…………………………………57
6.3.2 Unadjusted clinical outcomes……………..………………………………..57
6.3.3 Propensity score adjusted clinical outcomes………………………………..58
6.4 Discussion……………………………………………………………………….58
6.5 Conclusions…………………………………………………………………...61
CHAPTER 7 QUALITY OF LIFE IN ELDERLY PATIENTS WITH HFPEF…....67
7.1 Introduction…………………………………………………………………….67
7.2 Methods……………………………………………………………………..….68
7.2.1 Patient population…………………………………………………………68
7.2.2 Health related quality of life assessment…………………………………69
7.2.3 Follow-up…………………………………………………………………69
7.2.4 Statistical analysis………………………………………………………...69
7.3 Results………………………………………………………………………….70
7.3.1 Baseline patient characteristics……………………………………………70
7.3.2 Mortality…………………………………………………………………71
7.3.3 Health-related quality of life………………………………………………71
xvii
7.3.4 Therapy……………………………………………………………………71
7.3.5 Predictors of HRQoL improvement in HFPEF patients…………………...72
7.4 Discussions……………………………………………………………………..72
7.5 Conclusions…………………………………………………………………….75
CHAPTER 8 A RISK SCORE TO PREDICT 1 YEAR MORATALITY IN
PATIENTS WITH HFPEF…………………………………………………………83
8.1 Introduction…………………………………………………………………….83
8.2 Methods………………………………………………………………………...84
8.2.1 Patient population……………………………………………………........84
8.2.2 Candidate Predictor Variables………………………………………….....84
8.2.3 Statistical analysis………………………………………………………...85
8.3 Results………………………………………………………………………….86
8.3.1 Patient Characteristics and Outcomes……………………………………86
8.3.2 Predictors of Mortality…………………………………………………..87
8.3.3 Generation of the Risk score……………………………………………..87
8.3.4 Validation of the risk score……………………...………………………...88
8.4 Discussions……………………………………………………………………..88
8.5 Conclusions…………………………………………………………………….91
CHAPTER 9 ALBUMIN LEVELS PREDICT SURVIVAL IN PATIENTS WITH
HFPEF………………………………………………………………………………97
9.1 Introduction……………………………………………………………………..97
9.2 Methods………………………………………………………………………....97
xviii
9.2.1 Patient population…………………………………………………………97
9.2.2 Baseline measurement…………………………………………………….98
9.2.3 End points…………………………………………………………………99
9.2.4 Statistical analysis………………………………………………………99
9.3 Results………………………………………………………………………..100
9.3.1 Baseline patient characteristics……………………………………………100
9.3.2 Hypoalbuminemia and Cardiac Events………………………………….101
9.3.3 Albumin and body mass index (BMI)……………………………………..102
9.3.4 Causes of hypoaluminemia in HFPEF patients…………………………...102
9.4 Discussion…………………………………………………………………….103
9.4.1 Liver dysfunction………………………………………………………….104
9.4.2 Hemodilution……………………………………………………………...105
9.4.3 BMI and hypoalbuminemia……………………………………………….105
9.4.4 Renal failure…………………………………………………………….106
9.4.5 B-type Natriuretic Peptides and albumin……….…………….……......…107
9.5. Conclusions………………………………………………………………….109
CHAPTER 10 GENERAL SUMMARY…………………………………...………117
10.1 Main findings of our study…………………………………………………….117
10.2 Clinical implications………………………………………………………….119
10.3 Potential for final development of research…………………………………...120
CHAPTER 11 CONCLUSIONS…………………………………………………...123
References………………………………………………………………………….124
xix
List of Tables
Table 6-1 Baseline Characteristics of HFPEF patients enrolled in Cohort-1 and
Cohort-2…………………………………………………………………...………...62
Table 7-1 Baseline Characteristics of HFPEF patients in different age subgroups…76
Table 7-2 Comparison of MLHFQ score among three age groups of HFPEF
patients………………………………………………………………………............78
Table 7-3 Multivariate-regression of HRQOL improvement within 1 year for patients
with heart failure and preserved ejection fraction (HFPEF) patients……...................79
Table 8-1 Baseline Characteristics of HFPEF patients in derivation and validation
groups…………………………………………………………………………...…...92
Table 8-2 Predictors of 1 year mortality of HFPEF patients……………………….94
Table 9-1 Baseline characteristics by the status of albumin in patients with
HFPEF.…………………………………………………………………..…….…..110
Table 9-2 Cox-regression of all cause mortality within 1 year for patients HFPEF
patients……………………………………………………………………………..111
xx
List of Figures
Figure 1-1 A comprehensive Doppler assessment of diastolic function………........21
Figure 6-1 Kaplan-Meier Survival Curve for 1 year mortality of HFPEF patients in
Cohort-1 (2006-2010) and Cohort-1 (2001-2005)………………………………….65
Figure 6-2 Kaplan-Meier Survival Curve for 1 year mortality of HFPEF patients in
Cohort-3 and Cohort-4 using propensity score matching…………………………..66
Figure 7-1 Change of MLHFQ scores (Total) between baseline and follow-up in
different age groups………………………………………………………………..80
Figure 7-2 Change of MLHFQ scores (Physical scale) between baseline and
follow-up in different age groups……………………………………………….….81
Figure 7-3 Change of MLHFQ scores (Emotional scale) between baseline and
follow-up in different age groups………………………………………………….82
Figure 8-1 Kaplan–Meier Survival Curves for the Derivation Cohort and the
Validation Cohort, According to the Prognostic Classification…...........................…95
Figure 8-2 ROC curve of prediction of risk score in 1 year mortality for HFPEF
patients…………………………………………………………………………......96
Figure 9-1 Kaplan-Meier survival analysis showing HFPEF patients with
hypoalbuminemia (Serum albumin≤34g/L) had significantly worse survival than
patients without hypoalbuminemia……………………………………………….114
Figure 9-2 Kaplan-Meier survival analysis showing HFPEF patients with
hypoalbuminemia (Serum albumin≤34g/L) had significantly higher cardiovascular
death rate in 1y than patients without hypoalbuminemia…………………………...115
xxi
Figure 9-3 ROC curve of prediction of serum albumin in 1 year’s survival for HFPEF
patients…………………………………………………………………………......116
xxii
SECTION I. LITERATURE REVIEW
CHAPTER 1 DEFINITION, PATHOPHYSIOLOGY AND DIAGNOSIS OF
HFPEF
Heart failure (HF) affects about 2% of the western population, with the prevalence
increasing sharply from 1% in 40-year-old individuals to 10% above the age of 75
years. It is the most common cause of hospitalization in patients over 65 years of age.1,
2
For so many years, HF has been defined as a syndrome characterized by an impaired
ability of the heart to fill with and/or to eject blood commensurate with the metabolic
needs of the body, resulting in a classic constellation of signs or symptoms of
pulmonary and systemic venous congestion. 1
While traditionally associated with the concept of ‘pump failure’ or reduced Left
ventricular (LV) ejection fraction (EF), it has become widely recognized that HF can
occur even when EF is preserved, constituting the syndrome of HF with preserved
ejection fraction (HFPEF). 3
Until the last two decades, the possibility that large numbers of HF patients might have
HFPEF was not considered. As numerous studies have now demonstrated, HFPEF is
common. The emergence of this ‘new’ form of HF engendered considerable early
skepticism, despite growing epidemiologic evidence of its importance. Controversy
1
about the significance of HFPEF has largely but not completely abated.
1.1 Definition of HFPEF
As recognition of the importance of HFPEF as a public health problem increased,
controversy arose over the proper term to use for HFPEF. Most early studies referred
to HFPEF as diastolic HF, a term implying that diastolic dysfunction is the key
pathophysiologic mechanism responsible for hemodynamic perturbations and
symptoms in these patients. Similarly, systolic HF was commonly used to refer to
patients with HF with reduced or depressed EF (HFREF). Many consider HFPEF to be
a disorder of diastolic function, while others believe that it may be due to a
combination of diastolic abnormalities with subtle disturbances of systolic function
that are insufficient to reduce LVEF.
4-7
In addition, studies using tissue Doppler
imaging (TDI) have demonstrated the presence of diastolic and/or systolic
dyssynchrony in patients with HFPEF.4,8 Since the term ‘diastolic heart failure’
implies that the functional abnormalities lie entirely in diastole which is now
manifestly not so the descriptive term heart failure with a preserved （HFPEF） or
normal ejection fraction (HFNEF）is preferred.9
EF is a continuous variable with a fairly normal distribution within the population,10
and the threshold value to define normal versus reduced EF is arbitrary. Although
consensus seems to be building toward use of an EF higher than 50% to designate
HFPEF, the approach to patients with borderline reduction in EF (EF of 40% to 50%)
2
adds to the complexity of the classification. 11
1.2. Pathophysiology of HFPEF
Understanding of the pathophysiologic mechanisms in HFPEF needs a clear
understanding of LV diastolic and systolic function and the manner in which LV
function is influenced by volume status, which together with LV geometry determines
preload, and of the arterial system, which together with LV geometry affects afterload.
Although abnormal diastolic function has long been hypothesized to be the primary
factor responsible for hemodynamic perturbations and symptoms in HFPEF, only
recently have studies proved this hypothesis by studying diastolic function in patients
with HFPEF and relevant control populations. Because LV structure and function are
altered by age, gender, and cardiovascular disease in the absence of HF, it is important
to understand how LV structure and function differ between persons with HFPEF and
elderly persons with cardiovascular disease but no HF. Whereas abnormal diastolic
function plays a key role in HFPEF, other mechanisms also contribute to the
pathophysiologic process in many patients.
1.2.1 Structure abnormality in HFPEF
1.2.1.1 Chamber remodeling
Many, but not all, patients with HFPEF exhibit a concentric pattern of LV remodeling
and a hypertrophic process that is characterized by the following features
12-14
: a
normal or near-normal end-diastolic volume; increased wall thickness and/or LV mass;
3
an increased ratio of myocardial mass to cavity volume; an increased relative wall
thickness.
The main physiological difference between HFREF and HFPEF is the increase in
ventricular volume and change in shape due to ventricular remodeling. A myocardial
infarction (or rarely viral myocarditis) appears to be a potent stimulant for the
remodeling process, which leads to increased ventricular volumes and reduced
ejection fraction.15 In hypertensive heart disease remodeling is a slower process.
Initially LVH by itself leads to reduced systolic and diastolic function particularly in
the long axis.16 Compensatory increased radial contraction normalizes the ejection
fraction. However, at later stages further remodeling will occur, the LV volumes will
increase and the patient will slip from HFPEF to more obvious HFREF. Thus, from a
physiological point it is more sensible to categorise patients with HF according to
whether remodeling has taken place.
Studies have demonstrated that over 95% of patients with HFPEF have a normal LV
end diastolic volume; 50 -66% have increased wall thicknesses, mass and relative wall
thickness.1, 17 HFPEF is frequently associated with LV hypertrophy (LVH).18 These
patients commonly have hypertensive heart disease which leads to concentric
remodeling, cardiomyocyte hypertrophy and increased extracellular matrix that result
in diastolic dysfunction and increased chamber stiffness. However, hypertensive heart
disease and concentric remodeling are not the only causes of these abnormalities. For
4
example, diabetic heart disease, coronary artery disease, and advanced age even in the
absence of LVH, can result in diastolic dysfunction.
LVH is often considered an adaptive response to volume and pressure overload in
order to normalize elevated wall stress.
4, 19
However, concentric remodeling is not
adaptive.20 Prolonged LVH may contribute to cardiac dysfunction and significant
increase in the risk of cardiovascular disease.21 Severe LVH is thought to be
deleterious because it reduces subendocardial coronary flow reserve which in turn
impairs diastolic function.22 By contrast, patients with HFREF exhibit a pattern of
eccentric remodeling with an increase in end-diastolic volume, an increase in LV mass
but little increase in wall thickness, and a substantial decrease in the ratio of mass to
volume and thickness to radius. 22. 23
The molecular mechanism of LVH is still not clear. A review published recently
summarized the possible mechanism of LVH in HFPEF.4 As we know, the calcineurin
transcriptional pathways as well as the renin–angiotensin system (RAS) place a
dominant role in the development of pressure-overload hypertrophy and HF.24,25
Interestingly, while blockade of calcineurin activity and RAS prevented the excessive
LVH. Blockade of calcineurin activity did not prevent the development of fibrosis
whereas the blockade of RAS reduced fibrosis.
26, 27
Abnormal diastolic filling is
found in patients with hypertension even before any obvious LVH or change in
systolic function 4, Mechanisms contributing to abnormal LV diastolic properties
5
include large artery stiffness, hypertension, ischaemia, diabetes, and intrinsic
myocardial changes can occur with or without hypertrophy.27,
28
This might be
explained by the suggestion that there are two types of hypertrophy in hypertensive
hearts. Early LVH occurs to compensate for pressure overload (independent of the
RAS) but the later stage of LVH is an excessive and unnecessary type driven by the
activation of RAS.28 It is possible that only patients who develop the latter stages of
LVH consisting of excessive hypertrophy and fibrosis (dependent on the RAS)
progress to HFPEF
In summary, remodeling is a very important therapeutic target and reversing
remodeling is probably a powerful predictor of improvement. Nearly all treatments
that are proven to reduce mortality and improve symptoms in heart failure have also
induced reverse remodeling-for example, beta-blockers and cardiac resynchronisation
therapy.29, 30
1.2.1.2 Cardiomyocyte and extracellular matrix remodeling
Alterations in organ morphology and geometry are generally paralleled by
differences at the microscopic level. In HFPEF, the cardiomyocyte exhibits an
increased diameter with little or no change in cardiomyocyte length, corresponding
to the increase in LV wall thickness with no change in LV volume. 4 By contrast, in
HFREF the cardiomyocytes are elongated with little or no change in diameter,
corresponding to the increase in LV volume with no change in LV wall thickness. 4
6
In HFPEF, there is an increase in the amount of collagen with a corresponding
increment in the width and continuity of the fibrillar components of the extracellular
matrix.12, 22, 23 There is also serologic evidence of an active fibrotic process in the
myocardium of patients with HFPEF31. In HFREF, there is degradation and
disruption of the fibrillar collagen, at least early in the development of HFREF.
22 ,23
12,
In end-stage HFREF, replacement fibrosis and regional ischemic scarring may
result in an overall increase in fibrillar collagen within the extracellular matrix.
Increased myocyte growth is usually accompanied by increased connective tissue, the
extracellular matrix (ECM), which is predominantly composed of collagen and to a
lesser degree elastin, laminin, and fibronectin. There are collagen types I, III, and V
within the myocardium, type I being most the abundant (85%).
4,28
Myocardial
stiffness is determined by the composition and turnover rate of the extracellular matrix
32
. There is some evidence in animalmodels to indicate that the progression to overt
HFPEF is associated with progression of myocardial stiffening rather than in LV
relaxation abnormalities.33,
34
Therefore myocardial stiffness appears to play a
significant role in HFPEF.
Both collagen accumulations within the myocardium and LVH contribute to
myocardial stiffening. There is evidence to suggest that myocardial stiffening is
probably more due to the progression of collagen accumulation, collagen phenotype
7
shift and enhanced collagen cross-linking, than to hypertrophy 34,35. Experimental and
human studies have demonstrated serological and morphometric evidence of increased
myocardial fibrosis in patients with hypertensive heart disease (HHD). 36, 37 There is
increased fibrillar collagen content, altered fibrillar collagen geometry, and an
increased collagen I to III isotype ratio.38, 39
Collagen turnover is regulated by enzymes such as matrix metalloproteinases (MMPs),
a family of zinc-dependent interstitial enzymes, and their tissue inhibitors (TIMPs). In
HHD, if TIMPs are increased and MMPs are decreased, this would favour decreased
collagen degradation and increased collagen accumulation. Studies have found that in
HHD, alteration in MMP profiles that favour decreased ECM degradation were
associated with LVH and diastolic dysfunction. Furthermore, increased TIMP-1 level
predicted the presence of heart failure, and higher levels of TIMP-1 were present in
patients with HFPEF compared to those without.37 Recent studies in HFPEF have
demonstrated the presence of an active fibrotic process, which is more marked with
increasing
severity
of
diastolic
dysfunction.
Elevated
levels
of
serum
carboxy-terminal telopeptide of procollagen type I, TIMP-1, amino-terminal
propeptide of procollagen type III, carboxy-terminal telopeptide of procollagen type I,
and MMP-2 were greater in more severe phases of diastolic dysfunction.38
Titin, a giant sarcomere protein that acts like a molecular spring, may play two roles in
diastolic function: a recoil spring mechanism and a desensitizer of the myofilaments
8
to calcium. Titin isoform shifting may have an impact on diastolic function. In
idiopathic dilated cardiomyopathy, Nagueh et al40 have recently shown an increase in
the N2BA:N2B isoform ratio compared with controls. This shift to a larger isoform
would predict a substantial decrease in passive myocardial stiffness, which was found
in myocardial strips, but also affects the restoring forces and elastic recoil of the
cardiac myocyte and hence ventricular suction.
Titin is compressed when the myocyte shortens during systole. At the beginning of
cell relaxation, when the actin-myosin crossbridges detach and active shortening
tension begins to dissipate, the compressed titin forcefully expands and generates an
intracellular "restoring force" that relengthens the sarcomere and myocyte 41. This
restoring force creates substantial early diastolic ventricular elastic recoil that
generates a negative LV pressure that sucks blood across the mitral valve and
facilitates LV filling at low LV and left atrial diastolic (filling) pressures 42.
The early diastolic elastic recoil is amplified during exercise when greater systolic
myocyte shortening causes greater titin compression; this is a critical mechanism that
enhances LV diastolic filling during exercise without an increase in left atrial and
pulmonary capillary pressure 43.
In addition to acting as a recoil spring, titin, in its compressed state, also interacts
with the actin-myosin filament to desensitize it to calcium. Thus, the marked titin
9
compression at end-systole facilitates both the detachment of the actin-myosin
crossbridge and cell relengthening by the two additive actions of desensitization and
elastic recoil 41, 42. Mutations in the titin gene have been associated with a familial
dilated cardiomyopathy 44
1.2.2 Diastolic dysfunction in HFPEF
In HFPEF patients, abnormalities in diastolic function form the dominant
pathophysiologic basis for the development of the clinical syndrome of HF. The major
abnormalities in LV diastolic function includes: slowed, delayed and incomplete
myocardial relaxation; impaired rate and extent of LV filling; shift of filling from early
to late diastole; increased dependence on LV filling from atrial contraction; decreased
early diastolic suction/recoil; increased LA pressure during the early filling; increased
passive stiffness and decreased distensibility of the LV; impaired ability to augment
cardiac output during exercise; reduced ability to augment relaxation during exercise;
limited ability to utilize the Frank-Starling mechanism during exercise; increased
diastolic LV, LA, pulmonary venous pressure at rest and/or during exercise. 12, 45-46
Impaired myocardial relaxation was the major abnormality in diastolic dysfunction of
HFPEF patients. There were many evidences which supported this. Data from studies
of humans with HFPEF,47,48,49 relevant animal models,50 and mathematical modeling
systems51 indicate that impaired relaxation is present in HFPEF and may contribute to
elevated mean LV diastolic pressures in HFPEF when the heart rate is increased and
10
particularly when marked increases in blood pressure occur along with tachycardia
during exercise. Furthermore, any other factor that further shortens the diastolic filling
period (prolonged contraction or long PR interval) will enhance the effect of impaired
relaxation on LV diastolic pressures during filling and thus affect the mean LA
pressure needed to fill the LV. Studies in patients with HFPEF have reported average
resting values of tau of approximately 60 milliseconds (heart rate, approximately 70
beats/min), with values increasing to approximately 86 milliseconds during
exercise.47,48 Whether therapies to enhance relaxation directly and specifically can be
developed, and whether such therapies will relieve symptoms, remains an area of
active investigation.
Stiffness or elastance is defined as the relationship between the change in stress and
the resulting strain. On the chamber level, the elastance of the LV varies over the
cardiac cycle (time-varying elastance), and end-systolic and end-diastolic elastance
are defined by the changes in systolic or diastolic pressure associated with a change
in end-systolic or end-diastolic volume. Increases in LV diastolic stiffness will
mandate higher LA pressures to maintain filling and thus promote elevated
pulmonary venous pressures and pulmonary congestion when LA pressures are
elevated or reduced cardiac output when LA pressures are not elevated.
The difficulties inherent in characterizing LV diastolic stiffness in patients have
limited studies characterizing ventricular diastolic stiffness in patients with HFPEF.
11
However, several studies using both invasive and noninvasive estimates of LV
stiffness have shown increased LV stiffness in HFPEF22,53-54 compared with
age-matched (but not always disease-matched) control cohorts without HF.
1.2.3 Systolic function in HFPEF
By definition, LVEF and most indices of contractile function is normal or nearly
normal in patients with HFPEF. Some have argued that in HFPEF systolic function is
completely normal, and that the clinical condition is due entirely to diastolic
dysfunction alone, and HFREF and HFPEF are distinctly different.13 These studies are
based on global measurements derived from pressure–volume relationships.
However, these studies take no account of regional dysfunction or abnormalities of
long axis function, which are compensated for initially by increased radial function.9,55
Even measures such as tau and LV end diastolic pressure-volume relationships have
considerable theoretical and practical drawbacks: neither accurately measures
‘relaxation’ or ‘stiffness’ as popularly supposed.55 Global pressure-volume loops can
be remain normal despite significant changes in myocardial architecture and shape,
which perhaps are reflected better by the long axis measurements.
Numerous studies have shown that at rest, myocardial systolic function is impaired in
many patients with HFPEF. Some of these studies have used load-dependent
12
measures, such as Doppler-derived systolic strain56, 57; some found that systolic peak
early diastolic velocities decreased. In an early study Yip et al7 showed that both
peak annular systolic and peak early diastolic velocities and the respective excursions
that are measures of ventricular long axis function were lower in patients with
HFPEF than in age-matched controls. These findings have now been confirmed in
other studies.58–61 Thus, despite a normal ejection fraction, systolic function in the
long axis is not normal in HFPEF. This should come as no surprise, as both LVH and
fibrosis clearly affect systole as much as diastole. Shan et al62 showed that both peak
annular systolic velocity and early diastolic velocity are equally affected by
interstitial fibrosis within the myocardium. Moreover, the subendocardial fibres,
which are mainly responsible for long axis contraction, may be more susceptible to
the effects of fibrosis, hypertrophy and ischaemia because of their position, and thus
explain why this measurement is a good early marker of disease. In addition,
hypertension, LVH, ageing and diabetes all alter global myocardial architecture and
fibre orientation, which would probably have important effects on ventricular torsion
and recoil during relaxation. Reduced ventricular twist and long axis motion during
systole also affect ventricular suction.63 There had been studies which showed
systolic dysfunction in HFPEF using load-independent measures of contractility,
such as stress-corrected midwall fiber shortening52,53 or measures of LV torsion or
“twist.”64
Systolic dyssynchrony was also found in HFPEF patients. The prevalence of systolic
13
and diastolic dyssynchrony in patients with HFPEF was assessed in two observational
series 8, 60. Using TDI, systolic and diastolic dyssynchrony were noted in 33-39% and
36-58% of HFPEF patients, respectively8. This prevalence is similar to that observed
in patients with systolic HF. However, whether or not dyssynchrony is an important
contributor to the pathophysiology of HFPEF remains uncertain
In summary, myocardial systolic dysfunction can occur in the setting of a normal EF
because concentric remodeling and cross-fiber shortening preserve the extent of
endocardial motion relative to the diastolic cavity despite myofiber shortening.66,67
Although subtle at rest, these abnormalities are associated with impaired prognosis.56
Perhaps more important, the ability to enhance systolic function with exercise is
dramatically impaired,68,69 contributing to impaired reserve function in HFPEF.
1.2.4 Left atrial dysfunction in HFPEF
Although most discussion has centered on ventricular function in HFPEF, atrial
function may also play an important role in the pathophysiologic process of HFPEF.
Early- and mid-diastolic LV (and thus LA) pressures as well as systolic atrial pressures
(atrial V wave) are important contributors to mean LA pressure, which is therefore the
resistance to filling that the pulmonary venous system faces. Whereas LA systolic
function compensates for reduced early filling in the earlier stages of HFPEF,70 atrial
failure eventually occurs. Indeed, an often forgotten hemodynamic hallmark of
restrictive cardiomyopathy is the presence of large V waves in the LA pressure
14
waveform in the absence of mitral regurgitation, reflecting reduced LA compliance.
Reduced LA compliance has been shown to potently influence the development of
pulmonary arterial hypertension in mitral valve disease and may play a similar role in
HFPEF. Reduced LA systolic function limits LV filling in the setting of impaired
relaxation and necessitates higher mean LA pressures to augment early diastolic filling.
Thus, enlarged and dysfunctional atria may contribute to the pathophysiologic process
of HFPEF.
1.2.5 Peripheral factors in HFPEF
In a recent experimental study of HFPEF the time to complete relaxation was
significantly longer than in controls, which worsened with increased arterial
pressure.50 Furthermore end systolic elastance was increased in this experimental heart
failure model and was closely linked to collagen volume fraction. Afterload affects
both systolic and diastolic LV performance, prolonging contraction and relaxation.
This effect is seen early in the progression of systolic dysfunction and leads to a
shortening of the diastolic filling period. This action of an increased afterload would
be particularly troublesome with faster heart rates such as with exercise or AF.
Kawaguchi et al48 found in humans that end systolic elastance (stiffness) was higher in
patients with HFPEF as was effective arterial elastance due to reduced total arterial
compliance, and these were higher than that associated with ageing or hypertension.
This ventricular–arterial stiffening (VVC), presumably due to abnormal myocardial
15
and arterial collagen, amplifies stress-induced hypertension, thus worsening diastolic
dysfunction. 4VVC is important in the context of HFPEF because of its important
effects on diastolic filling 4, 71. In patients with HFPEF, resting VVC is lower than in
younger individuals
4,72
but similar to asymptomatic hypertensive elderly patients
49
and falls within a range where cardiac work and efficiency are not compromised.
Indeed, large artery stiffness has been shown to have a strong inverse relationship
with exercise capacity (peak VO2) in patients with HFPEF
73
. Borlaug et al4 found
subtle systolic dysfunction in a large group of HFPEF patients compared to
hypertensive controls but ventricular–vascular coupling ratio was similar. Plan TT et
al4 found that during exercise despite similar VVC at rest in HFPEF patients
compared
to
matched
controls
there
was
a
marked
disturbance
of
ventricular–vascular coupling in HFPEF patients due to a combination of greater
increase in arterial stiffness and reduced LV end-systolic elastance response during
exercise compared to age matched controls, the latter indicating a failure of
contractile reserve.68 Impaired renal function and renal arterial atherosclerosis in the
elderly may also be involved in causing rapid rises in blood pressure and excessive
fluid retention.
1.3 Diagnosis of HFPEF
Several criteria have been proposed to define the syndrome of HFPEF,3, 11, 74 the most
comprehensive of which are the guidelines by the Echocardiography and Heart Failure
Associations of the European Society of Cardiology.3 In general, these diagnostic
16
criteria share three features in common: (i) clinical signs or symptoms of HF; (ii)
evidence of normal LV systolic function; and (iii) evidence of abnormal LV diastolic
dysfunction.
1.3.1 Clinical features
Patients with HFPEF were shown to have pathophysiologic characteristics similar to
those of HFREF, including severely reduced exercise capacity, neuroendocrine
activation, and impaired quality of life.27 As a result, the clinical manifestations of
HFPEF are identical to those of HFREF. Clinical manifestations of HFPEF include
dyspnea (including dyspnea on exertion, paroxysmal nocturnal dyspnea, and
orthopnea), elevated jugular venous pressure, pulmonary rales, lower extremity edema,
elevated brain natriuretic peptide (BNP) or N-terminal pro-BNP (NT-proBNP) levels,
and radiographic evidence of pulmonary edema.3 No clinical features (symptoms,
signs, or chest radiography) can be used to reliably distinguish between the two. This
was illustrated in a report in which clinical data from 59 patients aged at least 60 years
with symptoms of HF and an HFPEF were compared to data from 60 patients of the
same age with an LVEF ≤35 percent and data from 28 age-matched healthy controls43.
The patients with HFPEF had similar clinical manifestations (including peak VO2 and
neurohumoral activation) as those with HFREF, although some parameters were less
severe (natriuretic peptide levels, some quality of life measures). In another series,
cardiopulmonary exercise parameters in patients with HFPEF and HFREF were
indistinguishable75. Thus, assessment of EF with cardiac imaging is required in all
17
patients with new-onset HF.
1.3.2 Echocardiographic features of HFPEF patients
1.3.2.1 Doppler Echocardiographic Assessment of Diastolic Function and Filling
Pressures
In the most recent set of diagnostic criteria proposed by the European Society of
Cardiology,3 echocardiographic and haemodynamic features are key components for
the diagnosis of HFPEF. After first establishing the presence of signs or symptoms of
HF, the presence of an EF>50% and a LV end-diastolic volume index <97 mL/m2 is
the second essential criterion for the diagnosis.3 The third criterion is the presence of
LV diastolic dysfunction, which can be demonstrated by Doppler echocardiography,
cardiac catheterization, or BNP measurements. Assessment of diastolic dysfunction
begins with the transmitral flow velocity profile. Decreases in the ratio of early to late
diastolic filling (E/A), increases in the deceleration time, or increases in the
isovolumic relaxation time indicate impaired relaxation. However, in the presence of
impaired relaxation, increases in filling pressure progressively modify the transmitral
gradient and mitral inflow pattern. As a result, diastolic function should be classified
by using a combination of Doppler criteria including mitral inflow, tissue Doppler
mitral annular motion, Color M-mode flow propagation velocity and pulmonary
venous velocities. Using Doppler echocardiography, a ratio of mitral early diastolic
inflow velocity to mitral early annular lengthening velocity (E/e′) exceeding 15
provides evidence for raised LV filling pressures. If the E/e′ ratio is ≤8, then LV filling
18
pressures are probably ‘normal’. If the E/e′ ratio is intermediate (>8 to<15), it may be
necessary to consider a multi-parametric approach using ‘second line’ indices: the left
atrial volume (>40 mL/m2), LV mass index (>122 g/m2 in women and >149 g/m2 in
men), mitral inflow Doppler (ratio of early to late mitral inflow velocity <0.5 and
deceleration time >280 ms), pulmonary venous flow velocity patterns (duration of
pulmonary venous A-wave reversal <30 ms longer than duration of mitral A-wave), or
the presence of AF. The utility of these ‘second line’ indices was evaluated in a
retrospective study of patients referred to a tertiary echocardiography laboratory,30
where left atrial enlargement was shown to distinguish patients with E/e′ >15 from
those with E/e′<8 with better diagnostic accuracy than LV mass index or Doppler
measurements. However, prospective evaluation is still needed in patients with
confirmed clinical HF and E/e′ in the intermediate range of 8–15, 31
Based on these features, diastolic function can be divided into 4 grades3 (Figure 1-1):
( 1) Normal diastolic function; (2) .Mild diastolic dysfunction (impaired relaxation): In
patients with mild dysfunction the following findings are expected: mitral E/A ratio
is <0.8, predominant systolic flow in the pulmonary venous flow (S>D), annular e'
<8 cm/s (septal and lateral), and E/e' ratio <8 (septal and lateral). A reduced mitral
E/A ratio in the presence of normal annular TD velocities can occur in normal old
individuals and should not be used to diagnose diastolic dysfunction. (3) Moderate
diastolic dysfunction (Pseudonormal) In patients with grade II diastolic dysfunction,
mitral E/A ratio is ≥1, and average E/e' ratio (septal and lateral) is >10. In some
19
patients with moderate diastolic dysfunction, LV end diastolic pressure is the only
pressure that is increased and recognized by Ar-A duration ≥30 ms. (4) Severe
diastolic dysfunction (Restrictive filling pattern): With severe diastolic dysfunction
(grade III) restrictive LV filling occurs with an E/A ratio ≥2, DT <160 ms, IVRT≤60
ms, systolic filling fraction ≤40 percent, and average E/e' ratio ≥13. LV filling may
revert to one of impaired relaxation with successful therapy in some patients,
whereas in others LV filling remains restrictive. The latter response predicts
increased morbidity and mortality.
20
Figure 1-1 A comprehensive Doppler assessment of diastolic function. A =
transmitral flow velocity with atrial contraction; a′ = velocity of mitral annulus motion
with atrial systole; Adur = duration of A; AR = flow from left atrium to pulmonary
veins during atrial contraction; ARdur = duration of AR; D = diastolic; DT =
deceleration time; E = early diastolic transmitral flow velocity; e′=velocity of early
diastolic mitral annular motion; S = systolic. (From Redfield MM, Jacobsen SJ,
21
Burnett JC Jr, et al: Burden of systolic and diastolic ventricular dysfunction in the
community: Appreciating the scope of the heart failure epidemic. JAMA 2003;
289:194.)
1.3.2.2 Left Atrial Enlargement
Increases in left atrial (LA) dimension or volume are commonly present in patients
with HFPEF.76.77 A left atrial volume indexed to body surface area (LA volume index)
>32 mL/m2 was first recognized in the elderly as a strong predictor (P<0.003) of a
cardiovascular event with a higher predictive value than other echocardiographically
derived indices such as LV mass index (P=0.014) or LV diastolic dysfunction
(P=0.029).78 In a population-based study, LA volume index was also strongly
associated with the severity and duration of diastolic LV dysfunction: the left atrial
volume index progressively increased from a value of 23±6 mL/m2 in normals to 25±8
mL/m2 in mild diastolic LV dysfunction, to 31±8 mL/m2 in moderate diastolic LV
dysfunction, and finally to 48±12 mL/m2 in severe diastolic LV dysfunction.79 LA
volume index was therefore proposed as a biomarker of both diastolic LV dysfunction
and cardiovascular risk.80,81 A raised LA volume index (>26 mL/m2) has recently been
recognized as a relatively load-independent marker of LV filling pressures and of LV
diastolic dysfunction in patients with suspected heart failure and normal LVEF.82 In
these patients, LA volume index is a more robust marker than left atrial area or left
atrial diameter.83 For these reasons, the present consensus document considers a left
22
atrial volume index >40 mL/m2 to provide sufficient evidence of diastolic LV
dysfunction when the E/e' ratio is non-conclusive (i.e., 8< E/e' <15) or when plasma
levels of natriuretic peptides are elevated (Similarly, a left atrial volume index<29
mL/m2 is proposed as a prerequisite to exclude HFPEF. The conduit, reservoir, and
pump functions of the LA in normal and pathophysiological conditions are further
explained in the appendix.
1.3.2.3 Mildly abnormal systolic left ventricular function in HFPEF
The presence of mildly abnormal systolic LV function constitutes the second criterion
for the diagnosis of HFPEF. Since LVEF of heart failure patients presents as a
unimodal distribution, the choice of a specific cut-off value remains arbitrary.11 The
National Heart, Lung, and Blood Institute’s Framingham Heart Study3 used an LVEF
<50% as cut-off for normal or mildly abnormal systolic LV function and this cut-off
value has meanwhile been used or proposed by other investigators.11, 84 LVEF needs to
be assessed in accordance to the recent recommendations for cardiac chamber
quantification of the American Society of Echocardiography and the European
Association of Echocardiography.85 It is of importance to note that in HFPEF reduced
long-axis shortening is frequently compensated for by increased short-axis shortening.
1.3.2.4 Pulmonary Hypertension
Just as chronic pulmonary venous hypertension leads to pulmonary arterial
hypertension in HFREF, the same can occur in HFPEF, and an elevated tricuspid
23
regurgitant velocity indicative of pulmonary hypertension is extremely common in
HFPEF86,87 . Pulmonary hypertension (PHT) in HFPEF is highly prevalent and often
severe, and, as in LV systolic dysfunction,86 is a predictor of morbidity and mortality.
88,89
Because of the thin wall and distensibility, the right ventricle (RV) is more
vulnerable by an excessive afterload than by preload. The pulmonary circulation is a
central determinant of RV afterload, and an increase in RV ejection impedance can
easily result in RV failure, tricuspid regurgitation, and central venous pressure (CVP)
rise.
1.3.2.5 Other Doppler Echocardiographic Findings
Regional wall motion abnormalities (with preserved EF) and right ventricular dilation,
either from ischemic disease or secondary to chronic pressure overload from chronic
pulmonary venous hypertension, can also be present at echocardiography in patients
with HFPEF. Additional important negative findings to be considered at
echocardiography include the absence of valvular disease important enough to cause
the HF symptoms, pericardial tamponade, or pericardial constriction and the presence
of congenital heart diseases such as atrial septal defect or other more extensive
structural abnormalities .11
1.3.3 BNP AND N-pro BNP assays
Brain natriuretic peptide (BNP) is produced by ventricular myocardium in response to
an increase of ventricular diastolic stretch and their secretion results in natriuresis,
24
vasodilation, and improved LV relaxation. Cardiac myocytes produce pro-BNP,
which is subsequently cleaved in the blood into NT-proBNP and BNP.11
In patients with HFPEF, NT-proBNP values correlate with early diastolic LV
relaxation indices, such as the time constant of LV relaxation, late diastolic LV
relaxation indices, such as LV end-diastolic pressure, and the LV stiffness modulus.
11
BNP and NT-proBNP values also vary with the degree of LV diastolic dysfunction:
progressively higher values were observed in patients with a mitral valve flow velocity
pattern of impaired LV relaxation, pseudonormalization, or restriction.11,90 The area
under the receiver operating characteristics (ROC) curve of NT-proBNP (0.83)
equalled the area observed for LV end-diastolic pressure (0.84) and exceeded the area
observed for an abnormal TD e'/a' ratio (0.81).11 Combining NT-proBNP with the E/e'
ratio increased the area under the ROC curve from 83 to 95%.11 In contrast to its
usefulness in symptomatic isolated diastolic LV dysfunction, natriuretic peptides were
a suboptimal screening test for preclinical diastolic LV dysfunction.91
Numerous studies have now shown that on average, brain natriuretic peptide (BNP)
and N-terminal pro-BNP (NT-proBNP) assay results are elevated in patients with
HFPEF compared with results in persons without HF but are lower than levels in
patients with reduced EF. As a result, it can be used in the diagnosis of HFPEF. For
the diagnosis of HFPEF, a high positive predictive value was aimed for when choosing
the cut-off values of NT-proBNP (220 pg/mL; Roche Diagnostics) and of BNP (200
25
pg/mL; Triage Biosite). For the exclusion of HFPEF, a high negative predictive value
was aimed for and the respective cut-off values of NT-proBNP (120 pg/mL) and of
BNP (100 pg/mL) were adjusted accordingly. NT-proBNP values of 120 and 220
pg/mL yielded, respectively, a negative predictive value of 93% and a positive
predictive value of 80%.146 BNP values of 100 and 200 pg/mL yielded, respectively,
a negative predictive value of 96% and a positive predictive value of 83%.11 Cut-off
values of NT-proBNP were derived from ROC analysis performed in HFPEF patients
presenting with exertional dyspnoea. 11 An ROC analysis for BNP in HFPEF patients
presenting with exertional dyspnoea has not been reported.
Cut-off values of BNP were therefore derived from ROC analysis performed in
HFPEF patients presenting in the emergency room with acute heart failure.11 As
cut-off values of NT-proBNP and BNP were derived from different HFPEF subgroups,
their respective magnitudes and ranges cannot be compared. To achieve satisfactory
positive predictive values, the diagnostic cut-offs of NT-proBNP and BNP had to be
raised to a level, at which sensitivity drops below 80%. These results from the overlap
of NT-proBNP and BNP values between controls and HFPEF patients, especially
when the HFPEF patients present with exertional dyspnoea.92 Natriuretic peptides are
therefore recommended mainly for exclusion of HFPEF and not for diagnosis of
HFPEF. Furthermore, when used for diagnostic purposes, natriuretic peptides do not
provide diagnostic stand-alone evidence of HFPEF and always need to be
implemented with other non-invasive investigations.
26
There were some limitations in using the BNP in diagnosis of HFPEF. In normal
individuals, the concentration of NT-proBNP rises with age and is higher in women
than in men.11 BNP and NT-proBNP levels can be influenced by comorbidities such as
sepsis, liver failure, or kidney failure. Plasma levels of BNP rise independently of LV
filling pressures once glomerular filtration rate falls below 60 mL/min. BNP levels are
lower in obese persons, and HFPEF patients are often obese. More important,
increased transmural wall stress is the stimulus for enhanced BNP production. As
HFPEF patients have dramatically different LV geometry (smaller LV cavity and
thicker LV walls), their wall stress is much lower than in HF with reduced EF, even in
the setting of high systolic and diastolic pressures.93 Thus, for diagnosis, BNP is less
sensitive for detection of HFPEF, particularly in its early or milder stages. Similarly,
BNP is less specific for the detection of HFPEF. Normal plasma BNP concentrations
increase with age and are higher in women. Because patients with HFPEF are older
and more often female than are patients with HF and a reduced EF, the standard
partition value of 100 pg/mL suggested for the diagnosis of HF may not be appropriate
in HFPEF. However, the prognostic value of NT-proBNP was shown to be robust in a
large clinical trial of HFPEF patients.11
27
CHAPTER 2 EPIDEMIOLOGY OF HFPEF
2.1 Prevalence of HFPEF among HF patients
The reported prevalence of preserved LVEF among patients with HF varied widely
from 13 to 74% in early studies.3 There were many population-based studies with
echocardiographic investigations performed in large community-based samples all
over the world. 1Together, these recent studies provided a more refined estimate of the
prevalence of HFPEF among patients with HF, which averaged 54%, with a range
from 40 to 71%.94 Inherent difficulties in making an accurate diagnosis of HFPEF, the
lack of standardization of diagnostic criteria, and the potential for misdiagnosis in
these often elderly, overweight, or deconditioned patients limit the precision of these
estimates.17 Nonetheless, the ‘true’ overall prevalence of HFPEF in the community
has been estimated at 1.1-5.5% of the general population.95,96Of note, the prevalence
of HFPEF in the community increased with advancing age, and was higher in
women.1Further, the relative prevalence of HFPEF among all HF patients increased
over time in a large hospital-based study in Olmsted County, MN, rising from 38 to
54% (of all HF cases) between 1987 and 2001.97 This temporal trend for increasing
HFPEF occurred in association with increases in the prevalence of hypertension,
diabetes, and atrial fibrillation (AF), but without a corresponding increase in the
relative prevalence of HFREF. In the same time frame, survival was noted to improve
in patients with HFREF, but not in those with HFPEF. These secular trends underscore
the importance of HFPEF as a major and growing public health problem.
28
Until now, there has been little study about the prevalence of HFPEF patients in Asian
population. Yip et al found that 66% of patients with a clinical diagnosis of heart
failure had a normal LVEF in a Hong Kong study.98 TSUCHIHASHI-MAKAYA M et
al studied 1,692 patients registered for the JCARE-CARD registry done in Japan. In
this analysis, 429 patients (26%) had EF ≥50% and 985 patients (58%) had EF <40%
(HF with reduced EF). 72
2.2 Demographic features and comorbid conditions
Recent large epidemiological studies characterizing more than 57,000 HF patients
have helped to confirm observations from previous smaller studies of selected
patients,1 and more clearly define the demographic features of patients with HFPEF .
In general, these patients are older women with a history of hypertension. The
prevalence of other cardiovascular risk factors varies depending on the study setting
and the diagnostic criteria for the condition. Although not uniformly reported,
cardiovascular risk factors are highly prevalent in HFPEF in population-based studies
and registries, and include obesity in 41-46%, coronary artery disease in 20-76%,
diabetes mellitus in 13-70%, atrial fibrillation (AF) in 15-41%, and hyperlipidaemia in
16–77%. In studies that included both HFPEF and HFREF,1 patients with HFPEF
were consistently found to be older, more often female, more predominantly
hypertensive, and have a higher prevalence of AF but a lower prevalence of coronary
artery disease compared with those with HFREF. Notably, non-cardiovascular
co-morbidities also appear to be highly prevalent in HFPEF, consistent with an elderly
29
population, and include renal impairment, chronic lung diseases, anemia, cancer, liver
disease, peptic ulcer disease, and hypothyroidism. Controlled clinical trials have, to
date, included more than 10,000 HFPEF patients; the demographic characteristics and
risk factor profiles of these individuals more closely resemble that of population-based
studies in the more recently completed trials (I-PRESERVE, SENIORS, HKDHF,
PEP-CHF) .1
2.2.1 Age
Patients with HFPEF are generally older than 65 years, with many older than 80 years.
Although cardiovascular disease may contribute to diastolic dysfunction in older
people, studies have also suggested that diastolic function deteriorates with normal
aging.72 The speed of LV relaxation declines with age in men and women, even in the
absence of cardiovascular disease. Vascular, LV systolic, and LV diastolic stiffness
increase with aging.72 Increases in vascular stiffness have been shown to be related to
effort intolerance in patients with HFPEF. Structural cardiac changes with aging and
functional changes at the cellular level involving blunted beta-adrenergic
responsiveness, excitation-contraction coupling, and altered calcium-handling
proteins may contribute to diastolic dysfunction with normal aging.
2.2.2 Gender
Along with age, female gender is a potent risk factor for HFPEF. Indeed, there appear
to be important age-gender interactions, such that the prevalence of HFPEF increases
30
more sharply with age in women than the prevalence of HFREF. Women accounts for
a large amounts of HFPEF patients. In a large multicenter study with 37,699 HFPEF
patients, 65% of HFPEF patients were women. 109 The reasons for the female
prominence in HFPEF are not entirely clear, but women have higher vascular and LV
systolic and diastolic stiffness than men do, and vascular and ventricular stiffness
increases more dramatically with age in women.1 Women also demonstrate more
concentric left ventricular remodeling and less ventricular dilatation in response to
arterial hypertension.
100,101
Unique coronary vascular functional changes in women
may also play a role in the pathophysiologic process of HFPEF.
2.2.3 Hypertension
Hypertension (HT) is the most commonly associated cardiac condition in patients with
HFPEF. Approximately 90% of patients with HFPEF have HT.102 Severe HT is
common in acute exacerbations of HF with a preserved ejection fraction, and treating
HT reduces the incidence of HF in older adults. Chronically increased blood pressure
is an important stimulus for cardiac structural remodeling and functional changes. The
resultant hypertensive heart disease is characterized by LVH (LVH), increasing
vascular and ventricular systolic stiffness, impaired relaxation, and increased diastolic
stiffness, all factors linked to the pathogenesis of HFPEF.101 In the presence of
hypertensive heart disease, ischemia produces exaggerated increases in filling
pressures, and hypertensive heart disease and ischemic heart disease are often present
in combination in patients with HFPEF. Elucidating which factors mediate transition
31
to HFPEF in persons with hypertensive heart disease is an area of active investigation.
A history of hypertension with LV (LV) hypertrophy is commonly associated with
HFPEF.1 Also, new-onset atrial fibrillation (AF) is common, and the loss of the atrial
contribution and reduced filling time may combine to precipitate pulmonary oedema.1
HT predisposes towards the development of AF especially if LV filling pressures are
high and LA size is increased. Thus, the onset of AF in a patient with hypertension
may be the precipitating factor for the symptoms of heart failure to develop and the
subsequent hospital admission. Ischemia and diabetes are also important. In a study
from Hong Kong it was clear that hypertension, ischemic heart disease and diabetes
overlapped and all were common in patients with HFPEF.103 All of these etiological
factors can impair both systolic and diastolic function, particularly ventricular long
axis function, even in the presence of a preserved LVEF.1
2.2.4 Coronary artery disease
The reported prevalence of coronary artery disease or myocardial ischemia in patients
with HFPEF varies widely.104 Although acute ischemia is known to cause diastolic
dysfunction (see later), the role of coronary artery disease and ischemia in contributing
to chronic diastolic dysfunction and symptoms in patients with HFPEF remains
speculative. Despite uncertainty about the role of ischemia in the pathophysiologic
process of HFPEF and a lack of data documenting that revascularization improves
outcomes in patients with HFPEF, HF management guidelines recommend
revascularization in those HFPEF patients in whom 'ischemia is felt to contribute to
32
diastolic dysfunction.105,106 Whether unique features (e.g., diffuse disease, more
endothelial dysfunction) play a role in the pathophysiologic process of HFPEF in
women remains to be determined. Even in the absence of epicardial coronary disease,
aging, hypertension, and diabetes are associated with vascular rarefaction and reduced
coronary microvascular density, which can lead to impaired coronary flow reserve and
diastolic dysfunction during stress.107,108
2.2.5 Atrial fibrillation
Atrial fibrillation (AF) is recognized as a frequent precipitant of acute decompensation
in patients with HFPEF. The Euro Heart Failure Survey reported the presence of
chronic AF was 25% in HF patients with LVEF ≥40%.109 Potential mechanisms
responsible for this frequent presentation are discussed more fully later. Diastolic
dysfunction, AF and HFPEF are common and related conditions that probably share
common pathogenic mechanisms in the elderly.
2.2.6 Diabetes Mellitus
Diabetes Mellitus (DM) is a potent risk factor for HF. A study showed that and the
prevalence of diabetes HFPEF was 29%110, which was similar to that of HFREF
patients, suggesting that DM contributes to the pathophysiologic process of both forms
of HF. The morphologic changes in the diabetic heart include myocyte hypertrophy,
increased extracellular matrix (fibrosis), and intramyocardial microangiopathy.
Functional changes, which may represent a continuum, include impaired
33
endothelium-dependent and endothelium-independent vasodilation, impaired LV
relaxation, increased passive diastolic stiffness, and contractile dysfunction.
Mechanisms contributing to structural and functional coronary vascular and
myocardial changes are diverse and include metabolic disturbances, activation of
proinflammatory and profibrotic mediators, cardiac autonomic neuropathy, and
increases in advanced glycation end products, which promote increased collagen
accumulation and increased collagen stiffness. Accumulation of advanced glycation
end products may play a role in age-related cardiac and vascular stiffening.
2.2.7 Renal Dysfunction
The critical impact of renal function on morbidity and mortality in HF is well
established.110 Studies have shown no difference in the severity of renal dysfunction in
patients with reduced or preserved EF.96 D. Rusinaru et al. analyzed 358 HFPEF
patients and found that 53% of patients had eGFR <60 ml/min/1.73 m2.112
Furthermore, the incidence of worsening renal function during HF therapy is similar in
patients with preserved or reduced EF.96Although the prevalence of renal vascular
disease in HF has been poorly delineated, it is probably high, and bilateral renal artery
stenosis with rapid-onset pulmonary edema is a well-recognized cause of HFPEF.
Evaluation of the renal arteries should be considered in patients presenting with the
triad of hypertension, renal dysfunction, and HFPEF.
2.2.8 Body Mass Index
34
Obesity is associated with an increased risk for HF. In general, patients with HFPEF
are more often obese than are patients with HFREF, and the prevalence of diastolic
dysfunction is increased in obese persons. Increased adiposity not only imposes an
adverse hemodynamic load on the heart but also is a source of a large number of
biologically active peptide and nonpeptide mediators, many linked to chronic
inflammation. Increased body mass index is a risk factor for hypertension, diabetes
mellitus, coronary artery disease, and AF, all of which are associated with HFPEF.
Studies using TDI or invasive LV pressure measurement have reported an association
between diastolic dysfunction, elevated filling pressures, and obesity, even in the
absence of a diagnosis of HF.111
2.2.9 Anemia
Anemia was also a common comorbidity of HFPEF patients. SENIORS study112 found
that anemia was similarly common in patients with LVEF≤35% and those with
LVEF>35% (19.0 vs. 18.7%, P =0.89). They also found that anemia is an independent
predictor of death or hospitalization for cardiovascular reasons among elderly patients
with chronic HF and reduced or preserved/mildly reduced LVEF.
2.2.10 Chronic Obstructive Pulmonary Disease
There have been several studies which aimed to illustrate the relationship of Chronic
Obstructive Pulmonary Disease (COPD) and the prognosis of HFPEF patients. Kwon
et al113 found that In HF patients with coexisting COPD, cardiovascular and
35
pulmonary event-free survival of HFPEF was found to be similar to that of HFREF
over 3 years follow-up. Rusinaru et al114found that COPD was an independent
predictor of mortality in patients with preserved LV ejection fraction and in patients
with reduced ejection fraction.
2.3 Mortality of HFPEF patients
2.3.1 Mortality rates
Several studies have evaluated the short- and long-term mortality of HFPEF,
compared these mortality patterns with that of HFREF, and assessed the prognostic
factors that determine mortality risk in patients with HFPEF.
HFPEF is associated with high in-hospital, short-term, and long-term mortality rates.
In studies that have evaluated mortality during the peri-hospitalization period, the
in-hospital mortality rates have ranged from 3-6.5% during the index
hospitalization.115 Short-term (30-90 day) mortality also is high, ranging typically
between 5 and 9.5%.115 The long-term mortality rates seem more variable in the
reported literature. Annualized mortality rates ranged from about 3.5 to 6% in 3 of the
large randomized clinical trials.77,
116
The lower mortality of HFPEF patients in
clinical trials likely reflects a selection bias favouring relatively younger, more
compliant individuals with less comorbidities. A meta-analysis of 7688 patients with
HFPEF followed for about 4 years found an overall mortality of 32% (about an 8%
annual mortality rate).117The longer term (5 years) mortality rates across observational
36
studies and registries evaluating prevalence cohorts of HFPEF are consistently high,
although absolute rates have varied considerably from about 55 to 74%.118
There were many studies which tried to compare the mortality of HFPEF and that of
HFREF. More recently, Somaratne et al.117 published the largest systematic
meta-analytic comparison of death rates in the two kinds of HF; the investigators
compared mortality in 7688 HFPEF patients with 16,831 HFREF patients from 17
studies, and noted a 50% lower hazard for mortality in HFPEF compared with HFREF.
Another meta-analysis119 published recently studied 31 studies included 41,972
patients: 10,347 with HF-PEF and 31 625 with HF-REF. They found that patients with
HFPEF had a lower risk of death than patients with HF-REF, and this difference is
seen regardless of age, gender, and etiology of HF. However, absolute mortality is still
high in patients with HFPEF highlighting the need for a treatment to improve
prognosis.
When regard to hospitalization rates, large prospective national registries have
consistently demonstrated that 46–51% of hospitalized acute HF patients have a
HFPEF.1 These patients are also just as likely to be re-admitted following discharge as
patients with HFREF, with a re-hospitalization rate of 29% within 60-90 days,1 and a
median time to re-hospitalization of 29 days.1
2.3.2 Pattern of death
37
There were still controversies on the reason of death for HFPEF patients. The
proportion of deaths attributed to cardiovascular vs. non-cardiovascular causes in
HFPEF varies with study design, mode of death ascertainment, and time period of the
studies.116-118 As noted above, there is a general consensus that patients with HFPEF
have high co-morbidity burden due to their elderly nature. Thus, a recent report from
the Mayo Clinic120 (that was community-based, and in which the cause of death was
adjudicated by a coroner) underscored that nearly half of HFPEF patients succumbed
to non-cardiovascular diseases, and there has been a temporal trend for higher
non-cardiovascular mortality in HFPEF in the most recent decade (late 1990s-early
2000). However, Lim et al studied 461 HFPEF patients and found that cardiovascular
diseases was still the main cause of death for HFPEF patients after 3-year's follow-up.
121
Overall, community-based studies120,122 demonstrate a higher proportion of
non-cardiovascular deaths, and clinical trials77,116 report a higher per cent of
cardiovascular deaths. This pattern may reflect the enrolment of healthier patients with
fewer co-morbidities in controlled clinical trials. Cardiovascular causes of death in
HFPEF patients include sudden death, refractory HF (pump failure), myocardial
infarction, and other cardiovascular disease (stroke or coronary disease).77,117,120,121
When cause-specific mortality patterns are compared between HFPEF and HFREF,
the latter has a higher burden of cardiovascular-related death compared with the
former.120
2.4 Prognostic predictors
38
Several studies have examined the factors influencing mortality risk in HFPEF. Thus,
in one of the largest series from Canada115 that systematically investigated the impact
of prognostic factors, the following factors increased mortality risk: older age,
associated co-morbidities (presence of peripheral vascular disease, dementia, or
cancer each doubled mortality risk), worse clinical profile at presentation as reflected
by anemia (Hemoglobin <10g/dL), higher serum creatinine (>150 mmol/L),
hyponatraemia (<136 mmol/L), each of which increased mortality risk by 50%, and a
lower systolic BP. Some other studies have emphasized a worse prognosis in men with
HFPEF (compared with women),100 those with DM,109COPD,113,114 and AF.123
Some recent investigations have evaluated if the paradigm of reverse epidemiology
observed in HFREF is also evident in HFPEF. The impact of etiology of HFPEF on
mortality risk is less clear, with conflicting reports in the literature; a recent report
noted similar mortality risk in HFPEF due to valve disease, hypertension or ischemic
heart disease,118 whereas another study22 highlighted a worse prognosis in those with
coronary disease as the basis of HFPEF. In summary, HFPEF has a high mortality risk,
on an average lower than HFREF, a higher likelihood of non-cardiovascular death, and
a range of prognostic factors that are generally similar to those noted for HFREF.
These studies have reported that lower BMI, lower SBP, and lower total cholesterol
are all markers of increased mortality risk in HFPEF, thereby extending the reverse
epidemiology concept beyond HFREF. Kapoor studied 1, 236 consecutive patients
39
with a prior diagnosis of heart failure and a preserved EF (≥50%). They found that
Obesity (BMI>30) was noted in 542 patients (44%). Furthermore, they found that Low
BMI is associated with increased mortality in patients with heart failure and preserved
systolic function. However, with a BMI of >45, mortality increased, raising the
possibility of a U-shaped relationship between BMI and survival.
Risk score is very useful in predicting outcome for HFREF patients. Several
predictive models have been reported for long term outcomes in HF with reduced
ejection fraction.124, 125 However, these models have not included HFPEF patients.
Furthermore, existing studies focus on clinical trial populations, which may not
represent the general HF population, or the use of non-contemporary cohorts, which
may have limited applicability to current practice.
2.5 Health related quality of life in HFPEF patients
Health related quality of life (HRQoL) is very useful in the epidemiology of HF
patients. There was little study about the HRQoL in HFPEF patients. Lewis studied
2709 HF patients and found that in symptomatic HF patients, HRQoL was equally
impaired in both preserved and low LVEF populations in baseline.126 Horeska et al did
a similar study. HRQoL between patients with HF-PEF and HF-REF did not differ
significantly. When adjusting the HRQoL scores for BNP, an association between
HRQoL and LVEF was not found, i.e. patients with HF-PEF and HF-REF with similar
BNP levels had the same impairment in QoL.127 However, there has not been study
40
which compared the QoL change after a long-term’s follow-up.
41
CHAPTER3. TREATMENT OF HFPEF PATIENTS
In general, the management of HFPEF has two objectives. The first is to treat the
presenting syndrome of HF-relieve resting or exercise-associated venous congestion
and eliminate precipitating factors. The second is to reverse the factors responsible for
diastolic dysfunction or other perturbations that lead to HFPEF. Both
nonpharmacologic and pharmacologic strategies may be used to achieve these
objectives. Present treatment strategies for HFPEF are largely based on assumptions of
its pathophysiologic mechanisms and on extrapolations from proven strategies used in
HF with a reduced EF.
3.1 Non-pharmacologic Therapy
General measures that may be used in the management of patients with chronic
HFPEF are not different from those pursued in patients with HF with a reduced EF.
They include daily monitoring of weight, attention to diet and lifestyle, patient
education, and close medical follow-up. In patients with HFPEF, aggressive control of
hypertension, tachycardia, and other potential precipitants of HF decompensation
should be emphasized. Although there are no adequate clinical trials with appropriate
outcome endpoints, such as increased longevity, decreased symptoms, or improved
quality of life, to prove the benefits of exercise training in patients with HFPEF
definitively, several clinical and experimental studies have suggested that exercise
training would be beneficial for such patients.110
42
3.2 Medical and Surgical Therapy
In contrast to the treatment of HFREF, information to guide the pharmacologic
therapy for patients with HFPEF is lacking. There were Limited available data from
clinical studies and randomized controlled clinical trials for the therapy of HFPEF
patients. However, no drug was recommended to use for HFPEF patients until now.
3.2.1 Clinical Studies
Small controlled studies have been performed with various standard HF drugs in
patients with HFPEF. The drugs used have included ACEI, angiotensin receptor
antagonists, beta blockers, and calcium channel blockers. These trials have, however,
been small or have produced inconclusive results.103
3.2.2 Randomized Controlled Clinical Trials
The Digitalis Investigation Group (DIG) trial included a small subgroup of patients
with HFPEF. In the HFPEF group, digoxin did not alter the primary endpoint of HF
hospitalization or cardiovascular mortality but did reduce HF hospitalizations.128
Unfortunately, total cardiovascular hospitalizations were not reduced because of an
increased rate of admissions for unstable angina, which completely negated the
beneficial effect of reduced HF hospitalizations.
In the CHARM-Preserved Trial,77 HF patients with an EF higher than 40% were
randomized to candesartan (an angiotensin receptor antagonist) or placebo in addition
43
to standard therapy. Fewer patients in the candesartan group than in the placebo group
reached the primary endpoint of cardiovascular death or HF hospitalization, a finding
that reached statistical significance only after adjustment for nonsignificant
differences in baseline characteristics.
The I-PRESERVE trial116 tested the angiotensin receptor blocker irbesartan in 4128
patients who were at least 60 years of age and had New York Heart Association Class
II, III, or IV HF and an EF of at least 45%. The primary composite outcome was death
from any cause or hospitalization for a cardiovascular cause (HF, myocardial
infarction, unstable angina, arrhythmia, or stroke). Secondary outcomes included
death from HF or hospitalization for HF, death from any cause and from
cardiovascular causes, and quality of life. Irbesartan had no effect on any of the
prespecified outcome measures.
In the PEP-CHF trial, patients older than 70 years with chronic HF and normal or
near-normal EF were randomized to perindopril (an ACEI) or placebo.129 The primary
endpoint was a composite of all-cause mortality or unplanned HF-related
hospitalization. Both enrollment and event rates were lower than anticipated, and there
was a high rate of cessation of blinded therapy, with crossover to open-label ACE
inhibitor use in both groups. These factors limited the strength of the study, which did
not show significant reduction in the primary endpoint. Some trends toward benefit,
primarily driven by reduction in HF-related hospitalizations, were observed at 1 year,
44
when crossover therapy rates were lower.
The SENIORS trial tested the effect of the beta1-selective blocker nebivolol in patients
with HF.130 Nebivolol also has vasodilator properties thought to be related to its effects
on nitric oxide release. This trial was not restricted to those with normal EF. There was
a modest but significant reduction in the primary endpoint of all-cause mortality or
cardiovascular hospitalizations, which was driven primarily by the effect on
hospitalizations. Prespecified subgroup analysis in patients with EF less than versus
more than 35% did not detect any trends toward reduced benefit in those with higher
EF. Unfortunately, there were very few patients with EF higher than 50% in this trial.
The Hong Kong diastolic heart failure study103 randomized 150 patients with HFPEF
(EF > 45%) to diuretics alone, diuretics plus irbesartan, or diuretics plus ramipril.
Quality of life assessment, 6-minute walk test, and Doppler echocardiography were
performed at baseline and at 12, 24, and 52 weeks. The quality of life score and
6-minute walk test increased similarly, and hospitalizations were similar in all three
groups. Modest improvements in Doppler systolic and diastolic indices and
NT-proBNP levels were seen only in the irbesartan and ramipril groups.
3.2.3 Current Therapeutic Recommendations
In addition, it is important to treat other contributing comorbidities and risk factors
aggressively, such as diabetes, hyperlipidemia, renal dysfunction, and renal vascular
45
disease. One retrospective study has shown that statin use, but not the use of beta
blockers, ACEI, or calcium channel blockers, is associated with improved survival in
patients with HFPEF. Until more clinical trials are performed in patients with HFPEF,
the empiric nature of therapeutic recommendations and their uncertain benefit must be
recognized.
CONCLUSIONS
In summary, HFPEF currently accounts for more than 50% of all HF patients and as
the prevalence of HFPEF in the HF population rises by 1% per year. Diastolic
dysfunction is the main pathophysiology in HFPEF patients. Diagnosis of HFPEF was
based on the symptoms or signs of HF, mildly abnormal systolic function and diastolic
dysfunciton. The prevalence of HFPEF patients among HF patients was about 50%.
They were more likely to be older, more likely to be female and more likely to have
co-morbidities such as DM, AF, renal failure, COPD when compared with HFREF
patients. The mortality rate of HFPEF was as high as that of HFREF patients. Main
reason of death was still not clear. There was no recommended medical treatment for
HFPEF until now.
46
SECTIONS II STUDIES ABOUT HFPEF
CHAPTER4. OBJECTIVES AND HYPOTHESIS
4.1 Objectives of the study
There have been many studies about the epidemiology of HFPEF patients in the past
ten years. However, there were still some issues which were not clear for HFPEF
patients. For example, little was known about the clinical outcome including mortality
and hospitalization of HFPEF patients in Chinese population. Whether the prognostic
factors of HFPEF patients in Chinese population were the same as those of other races
was still unknown. Furthermore, there has been no a risk score for predicting outcome
of HFPEF patients until now, which is important for decision making in clinical
practice. Finally, long term health related quality of life (HRQoL) after treatment in
HFPEF patients and determinants of QOL improvement have not been well studied,
especially in very elderly HFPEF patients. As a result, we tried to do an epidemiology
study of HFPEF patients from a large HF registry.
Main objectives of our studies included:
1. To investigate the clinical outcome of HFPEF patients admitted to hospital over a 10
year (2001-2010)’s period.
2. To find the predictors of QOL improvement for HFPEF patients.
3. To find the effect of age on quality of life in HFPEF patients
4. To find the determinants and predictors of mortality, hospitalization for HFPEF
patients.
47
5. To develop an assessment Model that predicts 1 year mortality of HFPEF patients.
4.2 Hypothesis
The hypothesis of our studies included:
1. Clinical outcome of HFPEF patients admitted to hospital in recent five years was
better than those admitted earlier due to more recognition of the disease.
2. QOL in elderly HFPEF patients improves similarly as that of younger HFPEF
patients after treatment.
3. There would be some difference in the determinants and predictors of mortality,
hospitalization for HFPEF patients in Chinese population compared to other countries
due to the race difference.
4. A risk assessment model could be built with simple parameters to predict 1 year
mortality of HFPEF patients.
48
CHAPTER 5 METHODOLOGY
5.1 Patient population
Consecutive heart failure (HF) patients were prospectively enrolled into a HF Registry
from June 2006 to December 2010 at a university affiliated teaching hospital.
Baseline demographics, investigations (e.g. transthoracic echocardiography, routine
blood testing) and therapy were obtained during index hospital. HF patients enrolled in
HF registry received 3 month and 12 month’s follow-up by research nurses except for
routine cardiac clinic visit at our hospital. Diagnosis of HFPEF was based on the
clinical diagnosis and standard transthoracic echocardiography Heart failure with
preserved ejection fraction was defined as ejection fraction of more than 50%.
A historical cohort of HF patients admitted in our hospital from January 2001 to June
2005 were retrospectively retrieved from the Hong Kong Hospital Authority Clinical
Management System and data searched using the International Classification of
Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 428 (Heart
Failure).131
Among
these
cohort,
HF
patients
who
have
transthoracic
echocardiography done within hospitalization and LVEF more than 50% by standard
transthoracic echocardiography were selected for study.
5.2 Definition of HFPEF patients
HFPEF in our study was defined using the criteria of the Echocardiography and Heart
Failure Associations of the European Society of Cardiology.11 In general, these
49
diagnostic criteria share three features in common: (i) clinical signs or symptoms of
HF; (ii) evidence of normal LV systolic function; and (iii) evidence of abnormal LV
diastolic dysfunction. Preserved ejection fraction was defined as ejection fraction of
more than 50% in our study.
5.3 Baseline patient data
The following baseline characteristics of HFPEF patients were analyzed: gender, age,
atrial fibrillation (AF), coronary artery disease (CAD), chronic obstructive pulmonary
disease (COPD), renal failure, diabetes mellitus (DM), primary cerebrovascular
disease (CVD), hyperlipidemia, current smokers, systolic blood pressure (SBP) at
admission, diastolic blood pressure (DBP) at admission, heart rate at admission,
hemoglobin (Hb), sodium, blood urea, Cr, ALB, ALT, and medications at discharge
[diuretics, aldosterone antagonists, angiotensin-converting enzyme inhibitor or
angiotensin II receptor blockers (ACEI/ARB), beta-blocker, calcium channel blockers
(CCB), digoxin, warfarin, statins].
5.4 Echocardiogram
Comprehensive 2-dimensional with Doppler transthoracic echocardiography was
performed in all participants (Vivid Five or Seven, General Electric, Milwaukee, WI,
USA) using a 2.5-MHz probe. All images were digitally stored with at least 3 cardiac
cycles for off-line analysis. The LV volumes and ejection fraction were assessed by
bi-plane Simpson’s method.
The presence of LV diastolic dysfunction was
50
confirmed by Doppler echocardiography by interrogation of transmitral flow pattern,
pulmonary venous inflow pattern, and pulse-wave tissue Doppler imaging assessment
of peak myocardial early diastolic velocity.7,8 At least 3 consecutive beats in sinus
rhythm were measured and averaged.
5.5 Health related quality of life assessment
Health-related quality of life (HRQoL) was assessed by the Minnesota Living with
Heart Failure Questionnaire (MLHFQ) instruments. The MLHFQ was designed to
measure the effects of heart failure and its treatments on an individual’s HRQoL.103
The MLHFQ asks the individuals to indicate, using a scale ranging from 0 to 5, how
much each of 21 symptoms prevented them from living as they desired during the past
30 days. Total score may be derived by summing the responses. The MLHFQ has 2
subscales. The physical scale is composed of 8 questions, with scores ranging from 0
to 40. The emotional scale is composed of 5 questions, with scores ranging from 0 to
25 points. Higher scores reflect a poorer HRQoL.103
5.6 Follow-up and clinical outcome
3-month and 12-month follow-up was conducted by clinical interview at our hospital
to assess HRQoL. Cardiac events were retrieved from review of patient medical
records though the dedicated electronic system which recorded patient events,
hospitalizations and details of clinic follow up.
51
5.7 Statistical Analysis
All continuous variables were expressed as mean ± SD and categorical parameters
were presented as frequency (percentage) as appropriate. The difference between
continuous variables was analyzed by student t-test. Chi-square or Fisher exact test
was used for categorical data.
Kaplan-Meier survival curves were constructed to demonstrate 1 year survival in
different group of patients. The log-rank test was used to determine if actuarial
survival was significantly different. Hazard ratios (HR) were calculated using
Cox-proportional hazards models. We put 27 variables into univariate analysis for
screening the predictors of 1 year mortality in our study. They were: gender, age, atrial
fibrillation (AF), coronary artery disease (CAD), chronic obstructive pulmonary
disease (COPD), renal failure, diabetes mellitus (DM), primary cerebrovascular
disease(CVD), hyperlipidemia, current smokers, systolic blood pressure (SBP) at
admission, diastolic blood pressure (DBP) at admission, heart rate at admission,
hemoglobin (Hb), sodium, BUN, Cr, ALB, ALT, and medications at discharge
[diuretics, aldosterone antagonists, angiotensin-converting enzyme inhibitor or
angiotensin II receptor blockers (ACEI/ARB), beta-blocker, calcium channel blockers
(CCB), digoxin, warfarin, statins].
To adjust for the impact of baseline differences between the 2 cohorts on clinical
outcomes, we calculated a focused propensity score132-134. This method uses measured
52
variables which were different between the two cohorts to predict cohort assignment.
To establish a risk score, all HFPEF patients were randomly divided into derivation
group and validation group. We got a risk score from the derivation group and then
we validated the score in the rest of HFPEF patients.
A p≤0.05 (2-tailed) was considered as statistically significant. Data were analyzed
using SPSS 17.0 for Windows (SPSS, Inc, Chicago, IL).
53
CHAPTER 6 IMPROVED 12 MONTH SURVIVAL OF PATIENTS
ADMITTED WITH HFPEF OVER THE LAST DECADE
6.1 Introduction
Recent studies have shown that survival of patients with heart failure and preserved
ejection fraction (HFPEF) is improving but the reasons are not well established or
studied1. There may be changes in patient demographics, pattern of disease, pattern of
co-morbidities, management, awareness of this disease and so on. However, little was
known about the change of outcome for HFPEF patients in resent years among
Chinese population.
We performed a study to investigate the change of outcome of HFPEF patients at a
single institution over a 10-year period. We aim to assess the pattern, treatment and
outcomes of patients with HFPEF. We also try to determine independent predictors of
mortality and change of predictors for HFPEF patients over 10-year’s period.
6.2 Methods
6.2.1 Patient population
Consecutive heart failure (HF) patients were prospectively enrolled into a HF Registry
from June 2006 to December 2010 at a university affiliated teaching hospital.
Baseline demographics, investigations (e.g. transthoracic echocardiography, routine
blood testing) and therapy were obtained during index hospital. HF patients enrolled in
54
HF registry received 3 month and 12 month’s follow-up by research nurses except for
routine cardiac clinic visit at our hospital. Diagnosis of HFPEF was based on the
clinical diagnosis and standard transthoracic echocardiography Heart failure with
preserved ejection fraction was defined as ejection fraction of more than 50%.
A historical cohort of HF patients admitted in our hospital from January 2001 to June
2005 were retrospectively retrieved from the Hong Kong Hospital Authority Clinical
Management System and data searched using the International Classification of
Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 428 (Heart
Failure).131
Among
these
cohort,
HF
patients
who
have
transthoracic
echocardiography done within hospitalization and LVEF ejection fraction of more
than 50% by standard transthoracic echocardiography were selected for study.
6.2.2 Baseline patient data
HFPEF patients prospectively enrolled from 2006 through 2010(Cohort-1) and those
enrolled in 2001-2005 (Cohort-2) were compared. The following baseline
characteristics were used for comparison: gender, age, atrial fibrillation (AF),
coronary artery disease (CAD), chronic obstructive pulmonary disease (COPD), renal
failure, diabetes mellitus (DM), primary cerebrovascular disease (CVD),
hyperlipidemia, current smokers, systolic blood pressure (SBP) at admission, diastolic
blood pressure (DBP) at admission, heart rate at admission, hemoglobin (Hb), sodium,
blood urea, serum creatinine level (Cr), serum albumin levels (ALB) and medications
55
at discharge [diuretics, aldosterone antagonists, angiotensin-converting enzyme
inhibitor or angiotensin II receptor blockers (ACEI/ARB), beta-blocker, calcium
channel blockers (CCB), digoxin, warfarin, statins].
6.2.3 Study end points
The primary outcome for the study was time to death within one year. Secondary
outcomes included time to heart failure re-hospitalization.
6.2.4 Statistical analysis
Categorical data were expressed as percentages, and continuous variables expressed as
mean ±SD and/or median with inter-quartile ranges (IQR). Continuous variables were
compared using Student's t-tests. Categorical variables were compared using Pearson
chi-square or Fisher exact tests as appropriate. Survival was estimated by the
Kaplan–Meier method, and differences in survival between two cohorts were
assessed by the log-rank test. To adjust for the impact of baseline differences
between the 2 cohorts on clinical outcomes, we calculated a focused propensity
score132-134. This method uses measured variables which were different between the
two cohorts to predict cohort assignment. Using the propensity score, we adjusted both
Cohort-1 and Cohort-2 which made the two cohorts similar in baseline characteristics.
Statistical analyses were performed using SPSS version 17.0 for Windows (SPSS Inc.,
Chicago, Illinois). All calculated p values were two-sided and p value <0.05 were
considered statistically significant.
56
6.3 Results
6.3.1 Baseline patient characteristics
847 HFPEF patients prospectively enrolled from 2006 through 2010 (Cohort-1) and
170 HFPEF patients enrolled between from 2001 through 2005 (Cohort-2) were
studied. Baseline characteristics of HF patients in Cohort-1 and Cohort-2 were showed
in Table 7-1. HFPEF patients in Cohort-1 were older than those in Cohort -2. (77±11
vs. 74 ±11, p<0.001). The prevalence of hypertension was also much higher in
Cohort-1 than that of Cohort-2 (66% in Cohort-1 vs. 57% in Cohort-2, p=0.03).
HFPEF patients in Cohort-2 were more likely to be current smokers, have history of
CHF, history of IHD, history of hyperlipidemia than those in Cohort-1. HFPEF
patients in Cohort-2 also had higher heart rate, systolic blood pressure, blood urea, Cr
level as well as lower sodium, Hb and albumin level compared with those in Cohort -1.
Incubation rates and rates of receiving cardiac angiogram were also higher in Cohort-2.
In medication at discharge, patients in Cohort -1 were less likely to be prescribed with
regular nitrates. Other than this, there was no significant difference in prescription
rates of ACEI/ARBs and Beta-blocker between the two cohorts.
6.3.2 Unadjusted Clinical Outcomes
During the mean follow-up of 12 months, 175 HFPEF patients died in Corhort-1 and
54 HFPEF patients in Cohort-1. Kaplan-Meier analysis showed that one year survival
of HFPEF improved over time (78.8% in Cohort-1 vs. 68.2% in Cohort-2, Log-rank
57
χ2=9.5, p=0.002) (Figure 1), while heart failure re-hospitalization rates also decreased
over time (33.4% in Cohort-1 vs. 50.6% in Cohort-2, p<0.001).
6.3.3 Propensity Score Adjusted Clinical Outcomes
After matching for potential confounders, including all significant differences, we
selected 125 HFPEF patients in Cohort-1 (Cohort-3) and 125 HFPEF patients
(Cohort-4) in Cohort-2 for comparison of the 1 year’s survival. During the follow-up
of 12 months, 35 HFPEF patients in Cohort-3 and 53 HFPEF patients in Cohort-4 died.
Kaplan-Meier analysis showed that one year survival of HFPEF still improved over
time (78.9% in Cohort-3 vs. 68.1% in Cohort-4, Log-rank χ2=6.03, p=0.01) (Figure 2),
while heart failure re-hospitalization rate also decreased over time (34.3% in Cohort-3
vs. 51.2% in Cohort-4, p=0.002).
6.4 Discussion
Major findings of our study were that survival of patients admitted with HFPEF
improved over a 10 year period from 2001 to 2010. Even after matching the baseline
characteristics of recent and old cohorts, the survival still improved.
Community-based studies suggest that overall survival among patients with HF is
improving.135More and more studies have been focus on the outcome of HFPEF
patients recently. There were many studies which studied the mortality of HFPEF
patients by comparing with patients with heart failure and reduced ejection fraction
58
(HFREF). A meta-analysis found that patients with HFPEF have a lower risk of death
than patients with HFREF, and this difference is seen regardless of age, gender, and
etiology of HF. Absolute mortality is still high in patients with HFPEF highlighting the
need for a treatment to improve prognosis.119 However, few studies were focus on the
change of mortality in HFPEF patients over time. Ovan et al found among patients
with reduced ejection fraction, survival improved significantly over time, whereas
there was no trend toward improvement among patients with preserved ejection
fraction.97 These observations suggest that improvement over time in the survival of
broader populations of patients with heart failure may be due primarily to
improvement among those with reduced ejection fraction. Although several
interventions known to improve survival among patients with reduced ejection
fraction were introduced into clinical practice during the study period, no agents have
been proven to improve survival among patients with preserved ejection
fraction.128-130 Thus they thought that it was not unexpected that survival among
patients with preserved ejection fraction did not change significantly over the study
period.
The results of our study were opposite to Owan’s study. We found that the
mortality of HFPEF patients improved in the recent five years when compared to
previous HFPEF cohorts in our hospital. We confirmed our results by matching
baseline characteristics using propensity score. There may be three reasons for this
finding. First hypothesis was that recent HFPEF patients who enrolled in our HF
59
registry received more follow-up by our research nurses. However, we found that there
was no difference in the outcome of patients who came to follow-up and those who
didn’t come to follow-up. It did not support our hypothesis. Second, improved
outcome of HFPEF would be due to more awareness of this disease. HFPEF as a
possible diagnosis was more recognized in the past 5 years. However, there has not
been standard therapy for HFPEF patients until now. So we have no evidence to
support that improved outcome was related to more awareness of the disease. Third,
improved treatment may be a factor. However, there was no significant change in the
use of medication between the two cohorts (except a lower rate of nitrate usage in the
later cohort) which is somewhat surprising. The apparent improvement in outcome
therefore must due to other reasons other than more frequent use of standard anti-heart
failure therapies such as ACEI/ARBs etc. In part this observation also attests to the
lack of any medication proven to improve mortality in HFPEF patients. But increased
recognition of the condition will lead to earlier treatment of risk factors such as HT and
diabetes and this may be a factor in the improved outcome. Renal function was better
in the later cohort which was consistent with this. Finally, dose of medications at
discharged, actual drugs used and difference in the drugs prescribed in follow-up
would be reasons. However we have no related data to support. Further studies were
needed.
Limitations
There are some limitations in our study. First, there was selection bias because we used
60
different methods to select patients hospitalized in 2001-2005 and those in 2006-2010.
Patients enrolled in 2001-2005 were historical cohorts and they were more severe than
the current cohort. They were not consecutive recruited that they couldn’t represent
the real HFPEF cohort enrolled in our hospital between 2001 and 2005. Second, the
sample size was unmatched in HFPEF patients hospitalized in 2001-2005 and those in
2006-2010. However, we used propensity score to match the patient number and
characteristics and compared the true mortality rates between the two groups to reduce
the selection bias. Finally, only HF patients with echocardiography data were enrolled
in our study. Those patients without echo done would have effect on the outcome of
HFPEF patients.
6.5 Conclusions
In our population of Chinese patients with HFPEF, 1 year mortality and HF
re-hospitalization decreased over the last decade. Further study such as investigating
the impact of dose and actual type of medication, the role of HF registry on the
outcome of HFPEF patients are needed for investigation of the improvement.
61
Table 6-1 Baseline Characteristics of HFPEF patients enrolled in Cohort-1 and
Cohort-2
Parameters
HFPEF patients
HFPEF patients
Before propensity score matched
After propensity score matched
(N=1017)
(N=250)
Cohort-1
Cohort-2
p value Cohort-3
Cohort-4
p value
(2006-2010) (2001-2005)
(2006-2010) (2001-2005)
(N=847)
(N=170)
(n=125)
Age, years
77±11
74±11
<0.001 74±11
75±10
0.51
Female, n (%)
547 (63)
109 (64)
0.81
81 (65)
77 (62)
0.60
Smokers, n (%)
229 (28)
26 (15)
0.001
22 (18)
21 (17)
0.87
SBP, mmHg
153±31
174±38
<0.001 174±34
168±37
0.26
LVEF, %
62±8
65±11
0.002
65±11
0.99
Cr, umol/L
147±121
214±212
<0.001 163±147
181±165
0.36
Hb, g/dl
11.4±2.2
11.2±2.5
0.27
11.5±2.3
11.4±2.4
0.63
Blood urea, mmol/L 11±9
13±8
0.007
11±7
12±7
0.68
Albumin, g/L
36±5
35±8
0.03
35±6
35±8
0.77
Na, mmol/L
139±5
138±5
0.006
138±5
138±5
0.84
Medical
65±9
(n=125)
history-no.
(%)
HT
561 (66)
97 (57)
0.03
84 (68)
76 (61)
0.25
DM
346 (41)
77 (46)
0.26
66 (53)
62 (50)
0.61
62
CAD
212(25)
59(35)
0.01
32 (26)
41 (33)
0.21
AF
231(28)
50(30)
0.58
34(27)
37 (30)
0.67
COPD
90 (12)
18 (11)
0.83
8 (7)
14 (11)
0.24
CVA
128 (15)
20(12)
0.26
16 (13)
14 (11)
0.70
Hyperlipidemia
145(17)
12 (7)
0.001
10 (8)
11 (9)
0.82
CRF
144 (17)
52 (31)
<0.001 25 (20)
32 (26)
0.29
HF
318 (38)
48 (28)
0.02
42 (34)
24 (27)
0.25
Incubation
33 (4)
40 (24)
<0.001 14 (11)
14 (11)
1.00
9 (5)
<0.001 2 (2)
4 (3)
0.41
Coronary angiogram 4 (0.5)
Medication-no. (%)
ACEI/ARB
393 (47)
91 (54)
0.09
58 (46)
71 (57)
0.10
Beta-blockers
367 (44)
75 (44)
0.86
53 (42)
51 (41)
0.78
Aldosterone
35 (4)
5(3)
0.46
9 (7.2)
4 (3.2)
0.15
Regular nitrates
184 (22)
83(49)
<0.001 31 (25)
65 (52)
<0.001
Digoxin
113 (13)
21 (12)
0.72
13 (10)
17 (14)
0.44
CCB
250 (30)
44 (26)
0.34
35 (28)
34 (27)
0.89
Diuretics
516 (61)
117 (69)
0.05
63 (50)
87 (70)
0.002
Antagonists
SBP, systolic blood pressure at admission; LVEF, LV ejection fraction; NYHA, New
York Heart Association; BMI, body mass index; DM, diabetes mellitus; CAD,
coronary artery disease; AF, atrial fibrillation; COPD, chronic obstructive pulmonary
disease; CVD, primary cerebrovascular disease; HF, heart failure; Cr, creatinine;
63
eGFR, estimated glomerular filtration rate; ALT, alanine aminotransferase; Hb,
hemoglobin; ALB, serum albumin; QOL, quality of life ACEI, angiotensin-converting
enzyme inhibitor; ARB, angiotensin II receptor blockers; CCB, Calcium channel
blockers.
64
Figure 6-1 Kaplan-Meier Survival Curve for 1 year mortality of HFPEF patients in
Cohort-1 (2006-2010) and Cohort-2 (2001-2005)
65
Figure 6-2 Kaplan-Meier Survival Curve for 1 year mortality of HFPEF patients in
Cohort-3 and Cohort-4 using propensity score matching
66
CHAPTER 7 QUALITY OF LIFE IN ELDERLY PATIENTS WITH HFPEF
7.1 Introduction
The prevalence of elderly patients presenting with heart failure (HF) is increasing with
an ageing population and their management remains a major therapeutic challenge.
Many elderly patients with HF have relatively preserved ejection fraction (HFPEF)
and consequently in large population studies most HFPEF patients were older than 60
years. In addition, advanced age is an important determinant of outcomes for patients
with HFPEF.115
However, community practice surveys reveal that there is a disproportionately lower
use of cardiovascular medications among elderly patients with HF who may stand to
benefit.136 Reasons include limited trial data to guide the care of the elderly and
uncertainty about benefits and risks of newer medications. This population usually
receives less medication such as ACEI or Beta-blockers because of concerns related to
the frequently associated co-morbidities. 137
Health related quality of life (HRQoL) is increasingly more relevant in the
management of heart failure patients as survival increases and the population ages.
Studies have shown that health status in terms of symptoms and emotions strongly
predicts long term clinical outcomes in heart failure patients 127. One study found that
there was no difference in the unadjusted mean summary Minnesota Living with Heart
Failure Questionnaire (MLHFQ) score of HFPEF and Heart Failure-Low EF
67
patients.127
However, little is known about the long term health status after treatment in HFPEF
patients, especially the very elderly. There are also limited studies about the effect of
medication such as ACEI/ARB or Beta-blockers on HRQoL of elderly HFPEF
patients.
Thus, the aim of this study was to investigate the effect of age on HRQoL in HFPEF
patients. We also tried to determine if the effect of age on HRQoL in HFPEF patients
was influenced by the medications prescribed such as ACEI/ARB or Beta-blockers.
7.2 Methods
7.2.1 Patient population
We enrolled 847 consecutive HFPEF patients [LVEF ≥50%, including all causes of
heart failure] in our hospital between 01 June 2006 and 31 December 2010. Patients
were excluded if they died within 12-month’s follow-up (n= 94) or they did not come
for follow-up at 3 months or 12 months after discharge (n=87). Finally, there were a
total 495 HFPEF patients with HRQoL assessment recorded at baseline and 271
patients with measurements at baseline and follow-up. Medical records review for this
study was approved by the institutional clinical ethical review board.
68
7.2.2 Health related quality of life assessment
Health-related quality of life was assessed by the MLHFQ instruments. The MLHFQ
was designed to measure the effects of heart failure and its treatments on an
individual’s HRQoL.127 The MLHFQ asks the individuals to indicate, using a scale
ranging from 0 to 5, how much each of 21 symptoms prevented them from living as
they desired during the past 30 days. Total score may be derived by summing the
responses. The MLHFQ has 2 subscales. The physical scale is composed of 8
questions, with scores ranging from 0 to 40. The emotional scale is composed of 5
questions, with scores ranging from 0 to 25 points. Higher scores reflect a poorer
HRQoL.126
7.2.3 Follow-up
3-month and 12-month follow-up was conducted by clinical interview at our hospital
to assess HRQoL. Cardiac events were retrieved from review of patient medical
records though the dedicated electronic system which recorded patient events,
hospitalizations and details of clinic follow up.
7.2.4 Statistical analysis
Categorical data were expressed as percentages, and continuous variables expressed as
mean ±SD and/or median with inter-quartile ranges (IQR). Continuous variables were
compared using Student's t-tests. Categorical variables were compared using Fisher
exact or Pearson chi-square tests as appropriate. HRQoL was analyzed using a
69
repeated measures analysis of variance procedure that allowed for imputation of any
missing data through a general linear model. The general linear model allowed us to
look at different age subgroups, time, and interaction effects. Odd ratios (OR) were
calculated using logistic regression models. We entered 9 variables into the univariate
analysis for screening the predictors of HRQoL improvement in our study which were:
gender, age, MLHFQ scores at baseline, LVEF at baseline, history of primary
cerebrovascular diseases(CVD), New York Heart Association (NYHA) Class and
medications at discharge [angiotensin-converting enzyme inhibitor or angiotensin II
receptor blockers (ACEI/ARB), beta-blockers, diurectics]. A p≤0.05 (2-tailed) was
considered as statistically significant. Statistical analyses were performed using
SPSS version 17.0 for Windows (SPSS Inc., Chicago, Illinois).
7.3 Results
7.3.1 Baseline patient characteristics
After 12 months’ follow-up, 94 HFPEF patients died within one year while full
follow-up. 271 HFPEF patients had HRQoL measurements done at baseline and at 3
and 12 months follow up and constitute the cohort for analysis. Patients aged <66,
66-85 and ≥85 years accounted for 12.1%, 67.4% and 20.3% of these 271 HFPEF
patients, respectively. HFPEF patients aged ≥85 years were more likely to have history
of hypertension and less likely to be treated with beta-blockers than younger patients
at baseline, but there was no other difference in baseline characteristics among three
age subgroups. Baseline characteristics of the study cohort are shown in Table 7-1.
70
7.3.2 Mortality
Cardiovascular mortality over one year (9.6%) was higher in elderly HFPEF patients
(age≥80years) than younger ones (age<80 years) (14.8% vs. 8%, p<0.001). Total 1
year mortality was 12.8%, 19.8% and 29.6% in the three age groups respectively.
HRQoL at baseline was related to 1 year mortality in the whole cohort [HR 1.02, 95%
CI 1.01-1.03, p=0.004]
7.3.3 Health-related quality of life
The mean MLHFQ total scores across 3 time points were similar in three groups and
improved from 30 to 15 in patients with age<66 years, from 28 to 15 in patients with
age 66-85 years and from 29 to 15 in patients with age≥85years after 12 months’
follow-up (Table 7-2). Overall 71-84% of patients felt that their HRQoL had improved
and in this respect there was no difference among the three age groups (p=0.28)
(Figure 7-1). When compared the Physical Scale and the Emotional Scale, there was
still no differences among the three groups (Figure 7-2 and Figure 7-3). We found no
interaction on the effect of age group in HRQoL score, although the time effect was
significant in three groups.
7.3.4 Therapy
At baseline there were no significant differences in the use of ACEI/ARB therapy in
the different age groups. (46.9% vs. 46.6% vs. 49.1%, p=0.95). Over the follow up
period overall 54.9% of HFPEF patients were taking ACEI/ARBs (53.2% vs. 56.3%
71
vs. 52.6% in three age groups, respectively, p=0.86), a slight but non-significant
increase. However, the percentage of very elderly patients taking beta-blockers was
significantly lower (Table 7-1). At follow up this did not change significantly.
7.3.5 Predictors of HRQoL improvement in HFPEF patients
Neither age nor prescribing ACEI/ARB can predict HRQOL improvement in our
cohort in univariate analysis. After adjusting for all potential confounders in the
multivariable analysis, only baseline MLHFQ score (OR, 1.08; 95% CI, 1.05-1.11;
p<0.001) was found to be the most powerful predictor of HRQoL improvement in
HFPEF patients at 1 year (Table 7-3). Despite the lower use of BB in the very elderly it
was not a negative predictor for HRQoL.
7.4 Discussions
The main finding of this study is that HRQoL in very elderly HFPEF patients can
improve to the same extent as younger HFPEF patients after 1 year. In addition, in this
study there was no difference in the medication prescribed in 1 year between elderly
HFPEF patients and younger ones which may be connected to the response. This is the
first study to demonstrate that the very elderly have as much to gain from modern
therapy as younger patients in terms of HRQoL.
Our results are particularly relevant to contemporary medical practice as the
prevalence of HF increases from a few percentage only at age 50 to about 10% in
72
people over 80 years of age. Approximately 80% of patients hospitalize with HF are
older than 85 years. Many studies showed long-term prognosis in HF patients older
than 80 years was poor138.
However, patient-centered outcomes such as HRQoL, have gained greater importance,
particularly because life expectances for HF patients have increased, and HF patients
have to adjust to living with a chronic condition and for many (elderly) patients
HRQoL appears to be more important than longer survival. There has been very few
previous work on this important aspect of life for the elderly and none on the effect of
therapy Hoekstra et al127 found that HRQOL was similarly impaired in patients with
HFPEF as in HFREF and Jaarsma et al140 also showed that baseline HRQOL
assessment was similar in patients older than 50 years old with HFPEF or HFREF.
Our study also found that the percentage usage of ACEI/ARB or Beta-blockers
increased similarly between very elderly HFPEF patients and younger ones after 1
year follow-up. However, cardiovascular mortality was higher in elderly HFPEF
patients than younger ones, which was similar with the results of the Mahjoub et al
study138 (Cardiovascular causes were recorded in over 60% of deaths of HF patients
with age >80 years). Although it is well established that ACEI and Beta-blocker can
reduce mortality in HFREF patients there is no proof of significant benefit from these
drugs in patients with HFPEF. However, many studies found that use of ACEI/ARB
and Beta-blocker was associated with better survival in HFPEF patients age> 65yr141.
73
Inherent difficulties in therapy management and perhaps a higher prevalence of renal
insufficiency at advanced ages may account for the lower prescription rates of
ACE-inhibitors in elderly HF patients found in many studies. However, we found that
Beta-blocker usage was significantly lower in the very elderly at baseline. But
Beta-blocker treatment was recently reported to be well tolerated and not associated
with an increased risk of adverse events in elderly HF patients142. It is not clear why
the usage of Beta Blockers was so much lower at baseline in the very elderly in our
cohort. Probably, concerns about possible side-effects deterred prescribing but this
may be unfounded. It seems likely that the similar HRQoL improvement demonstrated
in the very elderly patients may be related to a high rate ACEI/ARB prescriptions used
our study. Possibly the improvement in the elderly could be greater if Beta-blockers
were used more frequent.
Overviews of trials with ACE-inhibitors and beta-blockers have demonstrated that in
the HFREF population the benefits on mortality apply to both young and old patients.
However, despite this in may reports standard treatment with ACEIs etc in these very
elderly HF patients is not as frequent as in younger patients. Fonarow G136 et al found
that elderly (Age>75 years) HFREF patients received less ACEI/ARB treatment than
younger ones (78.8% vs.84.3%). In the HFPEF group, which is mainly elderly, lower
prescription rates of ACEI/ARB and Beta-blockers have also been found. In the
study by Mofgensen et al
137
only 38.3% of elderly HF patients (52.6% with
LVEF>45%) received ACEI/ARB compared with 54.1% in younger HF patients.
74
Limitations
There are a few limitations in our study. First, the sample sizes of elderly HFPEF
patients were relatively small. Second, many patients were lost to follow-up within 1
year. This may reflect the local health care organization.
Despite the lack of
difference between the age groups in the use of medication the overall usage of
standard heart failure therapy was low, especially aldosterone blockade which may be
more beneficial in the HFPEF population. It is possible that the HRQOL
improvements in the whole population could have been greater if the usage of standard
medications for HF had been higher. Also, although suggestive we are unable to
demonstrate conclusively or precisely from our data the degree to which the
improvement of HRQoL in elderly HFPEF patients was due to the use of medications
such as ACEI/ARB and beta-blockers.
7.5 Conclusions
Very elderly HFPEF patients can experience similar improvement in quality of life
compared to younger patients with appropriate treatment. Age per se should not deter
against treatment such as ACEI/ARBs and Beta-blocker because of potential benefits
in quality of life.
75
Table 7-1 Baseline Characteristics of HFPEF patients in different age subgroups
Parameters
Age<66 yr
Age 66-85 yr
Age> 85yr
p value
(n=49)
(n=164)
(n=58)
Age, years
57±8
77±4
88±3
<0.001
Female, n (%)
34 (69)
103 (63)
42 (72)
0.35
Smokers, n (%)
13(27)
48(30)
14(24)
0.72
SBP, mmHg
153±35
153±34
157±21
0.83
LVEF, %
62±8
64±9
62±7
0.08
NYHA III-IV, n(%)
31(66)
119(77)
48(84)
0.09
BMI, kg/m2
24±4
25±4
24±5
0.70
Cr, umol/L
127±70
136±98
115±44
0.30
Hb, g/dl
12.2±2.4
11.6±2.3
11.9±2.3
0.09
Blood urea, mg/dl
10±6
10±5
9±5
0.48
Na, mmol/L
138±5
139±4
138±5
0.12
HT
22(45)
116 (72)
42 ((71)
<0.001
DM
24 (49)
64 (40)
20 (35)
0.33
CAD
8(17)
46(29)
12 (21)
0.21
AF
15(32)
48(30)
15(32)
0.41
COPD
4(9)
17(11)
10(20)
0.19
CVA
5(10)
21(13)
10(18)
0.51
Hyperlipidemia
11(22)
31(19)
6(10)
0.24
Medical history-no. (%)
76
CRF
5(10)
26(16)
5(9)
0.32
HF
18(37)
52(32)
21(37)
0.73
Total scores
30±16
28±16
29±16
0.87
Physical Subscale Scores
16±8
17±9
16±9
0.94
5±5
7±5
0.48
MLHFQ scores
Emotional Subscale Scores 6±5
Medication-no. (%)
ACEI/ARB
23(47)
76(47)
27(49)
0.95
Beta-blockers
29(59)
82(50)
16(29)
0.005
Aldosterone antagonists
1(4)
9(9)
2(8)
0.67
Digoxin
9(18)
20(12)
8(15)
0.55
CCB
17(35)
47(29)
18(33)
0.69
Diuretics
32(65)
100(61)
29(53)
0.37
SBP, systolic blood pressure at admission; LVEF, LV ejection fraction; NYHA, New
York Heart Association; BMI, body mass index; DM, diabetes mellitus; CAD,
coronary artery disease; AF, atrial fibrillation; COPD, chronic obstructive pulmonary
disease; CVD, primary cerebrovascular disease; HF, heart failure; Cr, creatinine; ALT,
alanine aminotransferase; Hb, hemoglobin; ALB, serum albumin; HRQOL, quality of
life ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor
blockers; CCB, Calcium channel blockers.
77
Table 7-2 Comparison of MLHFQ score among three age groups of HFPEF patients
Parameters
Age<66y
Age 66-85
Age>85y
P value
Baseline
30±16
28±16
29±16
0.87
3 month
20±16*#
20±15 *#
21±16 *#
0.95
1 year
15±14†
16±14 †
15±12†
0.92
Baseline
16±8
17±9
16±9
0.94
3 month
10±9 *#
10±8 *#
12±10 *#
0.52
1 year
8±8†
9±8†
8±8†
0.61
MLHFQ Scores (Mean+ SD)
MLHFQ
Scores
(Physical
subscale)
MLHFQ
Scores
(Emotional
subscale)
Baseline
6±5
5±5
7±5
0.48
3 month
5±5 #
5±4 #
4±4 *#
0.49
1 year
4±4 †
3±4 †
3±3 †
0.64
*
p<0.05 when comparing the baseline and 3 month MLHFQ scores
#
p<0.05 when comparing the baseline and 1 year MLHFQ scores
†
p<0.05 when comparing the 3month and 1 year MLHFQ scores
78
Table 7-3 Multivariate-regression of HRQOL improvement within 1 year for patients
with heart failure and preserved ejection fraction (HFPEF) patients
Univariate analysis
Multivariate analysis
Variable
OR
95%CI
p value
OR
95%CI
p value
Age, per year increase
0.97
0.95-1.01
0.06
-
-
-
Female
1.39
0.77-2.48
0.27
-
-
-
LVEF at baseline
1.06
1.02-1.10
0.007
-
-
-
Baseline MLHFQ scores
1.08
1.05-1.11
<0.001
1.08
1.05-1.11
<0.001
NYHA class III-IV
1.87
0.99-3.53
0.05
History of CVD
0.37
0.18-0.77
0.01
-
-
-
Diuretics at discharge
1.53
0.87-2.71
0.14
-
-
-
Beta-blocker at discharge
1.52
0.86-2.70
0.15
-
-
-
ACEI/ARBs at discharge
1.06
0.60-1.87
0.83
-
-
-
OR, Odds ratios; 95% CI, 95% confidence intervals; NYHA, New York Heart
Association; DM, diabetes mellitus; CVD, primary cerebrovascular disease; ACEI,
angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blockers;
79
Figure 7-1 Change of MLHFQ scores (Total) between baseline and follow-up in
different age groups
80
Figure 7-2 Change of MLHFQ scores (Physical scale) between baseline and
follow-up in different age groups
81
Figure 7-3 Change of MLHFQ scores (Emotional scale) between baseline and
follow-up in different age groups
82
CHAPTER 8 A RISK SCORE TO PREDICT 1 YEAR MORATALITY IN
PATIENTS WITH HFPEF
8.1 Introduction
In clinical practice, risk models may be useful to inform patient triage and treatment
decisions. Patients hospitalized with heart failure (HF) provide a unique setting for
such prognostic tools. HF is prevalent and an increasingly common reason for
hospitalization all over the world and thus has a substantial public health and
economic impact.97 In the past ten years, the prevalence of HF patients with normal
ejection fraction (HFPEF), a common group comprising at least 50% of the whole HF
population has increased.97 Physicians often do not calibrate HF therapy to a patient’s
risk for adverse outcomes, failing to deliver effective therapies to the highest risk
patients, for whom the benefits of therapy are likely to be greatest. The ability to
predict mortality risk could inform clinical decision-making, although there is still no
proven therapy that reduces mortality in HFPEF patients at the moment.
However, objective prognostic information could guide the appropriate application of
monitoring and treatment, potentially resulting in improvements in the quality of care
delivered to and outcomes of patients hospitalized with HFPEF. Several predictive
models have been reported for long term outcomes in HF with reduced ejection
fraction.124,
125
However, these models have not included HFPEF patients.
Furthermore, existing studies focus on clinical trial populations, which may not
represent the general HF population, or the use of non-contemporary cohorts, which
83
may have limited applicability to current practice.
The objective of this study was to derive and validate a predictive model for 1 year
mortality using readily available clinical data in a large contemporary
population-based cohort of patients hospitalized with HFPEF in a university hospital.
This information was then used to guide the development of a user-friendly and
accessible risk score for 1 year hospital mortality for HFPEF.
8.2 Methods
8.2.1 Patient population
We enrolled 847 consecutive HFPEF patients (LVEF ≥50%, including all causes of
heart failure) in our hospital between 06 June 2006 and 31 December 2010. The
study population was randomly divided into derivation (70%, n=588) and validation
(30%, n=259) cohorts. Medical record review was approved by the institutional
review board.
8.2.2 Candidate Predictor Variables
Potential predictor variables were selected based on prior literature, clinical relevance,
and general availability at time of presentation. The following variable domains were
considered: gender, age, atrial fibrillation (AF), coronary artery disease (CAD),
chronic obstructive pulmonary disease (COPD), renal failure, diabetes mellitus (DM),
primary cerebrovascular disease (CVD), hyperlipidemia, current smokers, systolic
84
blood pressure (SBP) at admission, diastolic blood pressure (DBP) at admission, heart
rate at admission, hemoglobin (Hb), sodium, blood urea, Cr, ALB, ALT, and
medications at discharge [diuretics, aldosterone antagonists, angiotensin-converting
enzyme inhibitor or angiotensin II receptor blockers (ACEI/ARB), beta-blocker,
calcium channel blockers (CCB), digoxin, warfarin, statins].
8.2.3 Statistical analysis
Categorical data were expressed as percentages, and continuous variables expressed as
mean ±SD and/or median with inter-quartile ranges (IQR). Continuous variables were
compared using Student's t-tests. Categorical variables were compared using Fisher
exact or Pearson chi-square tests as appropriate. Cox- regression analysis was
performed to assess candidate predictors of 1 year mortality from a prior selected
demographic, clinical, laboratory, and medication variables, based on existing
literature and a statistically significant univariate relationship with mortality in the
derivation sample.
Survival was estimated by the Kaplan–Meier method, and differences in survival
between groups were assessed by the log-rank test. Univariate and multivariate Cox
proportional-hazards models were used to determine the contribution of these
variables. To develop a practical prognostic score, we assigned the risk factors
identified by multivariate analysis weighted points proportional to the β regression
coefficient values (rounded to the nearest integer). A risk score was then calculated
85
for each patient, and the population was divided into three categories: patients at low
risk, patients at intermediate risk, and patients at high risk for death.
Model validation had two steps: discrimination and calibration. Discrimination was
assessed with the receiver operating characteristic (ROC) curve, area under ROC
(AUROC) curves, sensitivity, and specificity. A cumulative risk score was calculated
for every patient in the validation cohort. ROC curves were plotted with 1–specificity
and sensitivity measured along the horizontal and vertical axes, respectively, with all
possible cumulative risk scores in the validation cohort as cutoff points for the
prediction of death within 1 year of follow-up. Statistical analyses were performed
using SPSS version 17.0 for Windows (SPSS Inc., Chicago, Illinois). All calculated p
values were two-sided and p value <0.05 were considered statistically significant.
8.3 Results
8.3.1 Patient Characteristics and Outcomes
The characteristics of patients enrolled in our study are shown in the Table 8-1. No
significant differences in patient characteristics were present between the derivation
and validation samples. Death occurred in 119 (20.2%) patients within 12 months in
derivation cohort. Those who died were older and more likely to have
hypoalbuminemia, history of congestive heart failure, history of cerebrovascular
disease. They also had higher serum creatinine, blood urea levels as well as lower
serum sodium, albumin and hemoglobin levels at admission than those who were alive.
86
Additionally, those who died were likely to use ACEI/ARBs, Beta-Blockers and CCB
at discharge of index hospitalization.
8.3.2 Predictors of Mortality
In multivariable analysis, hypoalbuminemia (HR, 2.86; 95% CI, 1.97-4.14; p<0.001),
history of congestive heart failure (HR, 1.59; 95% CI, 1.10-2.30; p=0.01), history of
cerebrovascular disease (HR, 1.67; 95% CI, 1.05-2.63; p=0.03, blood urea>10mmol/L
(HR, 1.59; 95% CI, 1.10-2.31; p=0.02), not use of calcium channel blockers (HR, 1.87;
95% CI, 1.18-2.97; p=0.01), age>78 years (HR, 1.52; 95% CI, 1.03-2.23; p=0.035),
were independent predictors of 1 year’s death. (Shown in Table 8-2)
8.3.3 Generation of the Risk score
To calculate a risk score, we assigned each of the six prognostic variables a number
of points that was proportional to its regression coefficient (Table 2). Each of the six
independent prognostic factors was assigned a number of points proportional to its
regression coefficient: hypoalbuminemia (5 points), not use of calcium channel
blockers (3 points), history of congestive heart failure (2.5 points), history of
cerebrovascular disease (2.5 points), blood urea nitrogen>10mmol/L (2.5 points),
age>78 years (2 points). A score was calculated for each patient by adding together
the points corresponding to his or her risk factors. We calculated risk scores for each
patient and defined three risk groups: low risk (0 to 5.5 points), intermediate risk (6 to
10.5 points) and high risk (11 to 17.5 points). .
87
8.3.4 Validation of the risk score
Classification of the derivation cohort according to risk score resulted in the
assignment of 46.0% of the patients to the low-risk group, 36.6% to the
intermediate-risk group, and 14.4% to the high-risk group. The results were similar
for the validation cohort: 47.8% of the patients were in the low-risk group, 35.5% in
the intermediate-risk group, and 16.7% in the high-risk group. In the development
cohort, Kaplan–Meier survival analysis showed that the 1-year mortality rates for the
low-, intermediate-, and high risk groups were 10.5%, 22.3%, and 48.7%,
respectively (Log-rank χ2=66.3, p<0.001). In the validation cohort, the 1-year
mortality rates for the low-, intermediate-, and high-risk groups were 15.4%, 25.3%,
and 39%, respectively (Log-rank χ2=10.9, p=0.004) (Figure 8-1). The C statistic for
the point system was 0.72 in the development cohort and 0.62 in the validation cohort
(Figure 8-2).
8.4 Discussion
In this study we have developed a risk score which reliably predicts 1 year mortality in
patients with HFPEF and which is based on 6 clinical factors routinely collected at the
time of admission: Hypoalbuminemia, history of CHF, history of CVD, blood
urea>10mmol/L, not using CCB and age>78 years. The risk score provides a simple
method to stratify a HFPEF patient’s risk of death at 1 year into low-risk (0 to 5.5
points), intermediate-risk (6 to 10.5 points) or high-risk (11 to 17.5 points).
88
Recent studies estimated that the prevalence of HFPEF among patients with HF
averaged 54%, with a range from 40 to 71%143,144. Furthermore, Bhatia126 et al found
that HFPEF patients had also high 1 year mortality similar to those with HFREF
patients.
Our study is the first study to create a predictive model for 1 year outcome
of HFPEF patients. There are many available risk stratification models for in-hospital
mortality in patients hospitalized with SHF. But the available risk stratification
models for in-hospital mortality in patients hospitalized with HF have limitations.
The Acute Decompensated Heart Failure National Registry (ADHERE) study124
identified blood urea nitrogen (BUN), serum creatinine, and systolic blood pressure
as the best predictors of in-hospital mortality using classification and regression tree
analysis. This model is appealing because it uses only 3 variables to classify patients
as low, intermediate, or high risk. A prognostic model for in-hospital mortality from
OPTIMIZE-HF was recently published with some overlapping variables but did not
have a separate derivation and validation cohort and did not include data on
admission BUN.125 Another study reported that the presence of cancer, systolic blood
pressure<124 mm Hg, serum creatinine <1.4 mg/dL, BUN >37 mg/dL, serum
sodium <136 mol/L, and age >70 years predicted in-hospital mortality.145 However,
these three studies only recruited SHF patients. HFPEF patients were not included in
these two studies.
The GWTG-HF risk score is another predictive model for in-hospital mortality of HF
89
patients including HFPEF patients.146,147 This score uses commonly available clinical
variables to predict in-hospital mortality and provides clinicians with a validated tool
for risk stratification that is applicable to a broad spectrum of patients with heart
failure, including those with preserved LV systolic function. However, this model
can only be used in predicting in-hospital mortality rather than long-term mortality of
HFPEF patients.
The predictors found in our study were similar with those of other studies. Tribouilloy
et al also found that history of stroke, age and lower eGFR was independent predictor
for 5 year mortality of HFPEF patients.118 There is little information previously about
the use of hypoalbuminemia for the prediction of outcome of HFPEF patients. Our
group found that hypoalbuminemia was common in HFPEF patients and was
associated with increased risk of death.148 Renal dysfunction might be the main
pathophysiologic mechanism underlying hypoalbuminemia in HFPEF patients.
We also found the not use of CCB was an independent predictor of 1 year mortality
for HFPEF patients. Until now there has not been randomized clincal trial which
suggested use of CCB could reduce mortality of HFPEF patients. Our finding may be
related to high prevalence of hypertension in our HFPEF patient. Over 60% of
HFPEF patients in our cohort had a history of hypertension. Hypertension had been
found a predicitor of poor outcome for HFPEF patients in previous studies.1 The use
of CCB may be asscoiated with improved outcome of HFPEF patients through better
90
control of hypertension inour cohort. Another reason would be related to reduced
heart rate when using CCB especially the non-dihydropyridines such as diltiazem
and verapamil. 30% of HFPEF patients had history of AF in our cohort. Reduced
heart rate by use of CCB may be related to better ourcome in HFPEF pateints.
Further study about use of CCB and outcome of HFPEF patients was needed.
Limitations
There are some limitations in our study. First, the sample sizes of HFPEF patients were
relatively small.
Second, we did not have biomarkers such as BNP and
echocardiographic parameters such as restrictive filling patern for prediction. Third,
specific causes of death were not studied in our cohort. Finally, the score could be only
suitable for Chinese populations
.
8.5 Conclusions
A risk score derived from commonly available clinical variables can be used to predict
1 year mortality of HFPEF patients. Application of the risk score could influence the
quality of care provided to patients hospitalized with HFPEF, which might be
important in clinical practice especially in an ageing society like Hong Kong.
91
Table 8-1 Baseline Characteristics of HFPEF patients in derivation and validation groups
Parameters
Derivation group
Validation group
(N=588)
(N=259)
Death
Alive
Death
Alive
(N=119)
(N=469)
(n=76)
(n=235)
Age, years
80±10
76±11
0.003
77±12
77±10
0.93
Female, n (%)
68 (57)
308 (66)
0.08
38 (66)
121 (61)
0.49
Smokers, n (%)
32 (28)
126 (28)
0.91
17 (30)
52 (27)
0.70
SBP, mmHg
145±31
155±31
0.002
155±35
153±31
0.72
LVEF, %
62±9
62±8
0.70
63±8
62±8
0.49
BMI, kg/m2
24±5
25±4
0.44
25±3
24±
0.12
Cr, umol/L
174±146
131±87
<0.001
176±140
132±93
0.03
Hb, g/dl
11.1±2.5
11.7±2.2 0.02
10.5±2.1
11.6±2.1
0.001
Blood urea, mmol/L 13±9
10±5
<0.001
13±9
11±15
0.24
Albumin, g/L
33±6
37±5
<0.001
33±6
37±4
<0.001
Na, mmol/L
138±5
139±5
0.53
138±6
139±5
0.05
Medical
P value
p value
history-no.
(%)
HT
71 (60)
304 (66)
0.17
38 (67)
139 (70)
0.61
DM
39 (33)
189 (41)
0.11
26 (45)
86 (43)
0.85
CAD
23 (20)
120 (26)
0.12
9 (16)
57 (29)
0.05
AF
40(34)
126 (28)
0.18
11 (19)
54 (28)
0.19
92
COPD
16 (15)
51 (13)
0.46
6 (12)
16 (9)
0.55
CVA
25 (21)
61 (13)
0.03
10 (17)
29 (15)
0.64
Hyperlipidemia
11 (9)
84(18)
0.02
11 (20)
37 (19)
0.90
CRF
26 (22)
67 (15)
0.05
14 (24)
27 (14)
0.06
HF
60 (50)
167 (36)
0.005
17 (29)
72 (36)
0.32
ACEI/ARB
43 (36)
222 (48)
0.02
18 (31)
105 (54)
0.003
Beta-blockers
44 (37)
222 (48)
0.04
17 (29)
80 (41)
0.11
Aldosterone
3 (3)
19 (4)
0.42
4 (7)
8 (4)
0.38
Digoxin
14 (12)
68 (15)
0.42
5 (9)
26 (13)
0.34
CCB
24 (20)
150 (33)
0.01
15 (26)
58 (30)
0.58
Statin
32 (27)
148 (32)
0.30
15 (26)
74 (38)
0.10
Diuretics
71 (60)
290 (63)
0.58
31 (53)
121 (62)
0.26
Medication-no. (%)
Antagonists
SBP, systolic blood pressure at admission; LVEF, Left ventricular ejection fraction;
BMI, body mass index; DM, diabetes mellitus; CAD, coronary artery disease; AF,
atrial fibrillation; COPD, chronic obstructive pulmonary disease; CVD, primary
cerebrovascular disease; HF, heart failure; Cr, creatinine; eGFR, estimated glomerular
filtration rate; ALT, alanine aminotransferase; Hb, hemoglobin; ALB, serum albumin;
QOL, quality of life ACEI, angiotensin-converting enzyme inhibitor; ARB,
angiotensin II receptor blockers; CCB, Calcium channel blockers.
93
Table 8-2 Predictors of 1 year mortality of HFPEF patients
Univariate analysis
Multivariate analysis
Variable
HR
95%CI
P value
β
HR
95%CI
P value
Age>78 years
1.58
1.08-2.31
0.02
0.42
1.52
1.03-1.94
0.04
Hb<10 g/L
1.59
1.08-2.35
0.02
-
-
-
-
Urea>10 mmol/L
1.69
1.18-2.44
0.005
0.51
1.59
1.10-2.31
0.02
Cr>150 umol/L
1.73
1.19-2.51
0.004
-
-
-
-
Hypoalbuminemia 3.18
2.20-4.58
<0.001
1.05
2.86
1.98-4.14
<0.001
History of CHF
1.70
1.19-2.43
0.004
0.47
1.59
1.10-2.30
0.01
History of CVD
1.62
1.04-2.52
0.03
0.51
1.67
1.05-2.63
0.03
of 1.51
1.04-2.20
0.03
-
-
-
-
of 1.55
1.07-2.26
0.02
-
-
-
-
Not use of CCB
1.84
1.17-2.88
0.008
0.61
1.87
1.18-2.97
0.008
SBP at admission
0.99
0.98-1.00
0.001
-
-
-
-
Not
use
Beta-blocker
Not
use
ACEI/ARBs
Hb, hemoglobin; BUN, blood urea nitrogen; Cr, creatinine; CHF, congestive heart
failure; CVD, cerebrovascular disease; ACEI, angiotensin-converting enzyme
inhibitor; ARB, angiotensin II receptor blockers; CCB, calcium channel blockers; SBP,
systolic blood pressure;
94
Figure 8-1 Kaplan–Meier Survival Curves for the Derivation Cohort and the
Validation Cohort, According to the Prognostic Classification.
95
Figure 8-2 ROC curve of prediction of risk score in 1 year mortality for HFPEF
patients
96
CHARTER 9 ALBUMIN LEVELS PREDICT SURVIVAL IN PATIENTS
WITH HFPEF
9.1 Introduction
Although hypoalbuminemia occurs in one third of patients with systolic heart failure
(HF)149 and has been demonstrated to be independently associated with increased risk
of death,150 little is known the relationship between hypoalbuminaemia and outcome
in HF patients with preserved ejection fraction (HFPEF), a common group with at least
50% in whole HF population. Linssen et al151 found that atrial fibrillation was
associated with higher N-terminal pro–brain natriuretic peptide (NT-proBNP) levels
and was independently related to death or HF hospitalization in HFPEF patients. In
another study, von Haehling S et al112 found that anaemia was also an independent
predictor of death or hospitalization for cardiovascular reasons among elderly patients
with chronic HF and reduced or preserved/mildly reduced LVEF. However, albumin
has not been studied previously as a potential predictor of outcome in HFPEF patients
until now. The primary goal of this study was to examine the hypothesis that low
serum albumin is also an independent predictor of survival in HFPEF patients.
9.2 Methods
9.2.1 Patient population
We enrolled 611 consecutive HFPEF patients (LV ejection fraction ≥50%, including
all causes of heart failure) in our hospital between 01 June 2006 and 31 December
2009. Patients were excluded if serum albumin results were unavailable (n=35).
97
Finally, there were total 576 HFPEF patients with both Doppler-echocardiography and
routine blood testing including albumin levels which obtained within 24 hour after
admission. Medical record review was approved by the institutional review board.
9.2.2 Baseline measurements
Serum albumin levels were analyzed by the clinical laboratory using a bromocresol
purple dye-binding method. The reference range for this albumin assay is 35 to 47 g/L
at our institution.
Comprehensive 2-dimensional with Doppler transthoracic echocardiography was
performed in all participants (Vivid Five or Seven, General Electric, Milwaukee, WI,
USA) using a 2.5-MHz probe. All images were digitally stored with at least 3 cardiac
cycles for off-line analysis. The LV volumes and ejection fraction were assessed by
bi-plane Simpson’s method.
The presence of LV diastolic dysfunction was
confirmed by Doppler echocardiography by interrogation of transmitral flow pattern,
pulmonary venous inflow pattern, and pulse-wave tissue Doppler imaging assessment
of peak myocardial early diastolic velocity.152, 153 At least 3 consecutive beats in sinus
rhythm were measured and averaged.
A standardized quality of life (QOL) questionnaire was completed during admission.
QOL was assessed using the Minnesota Heart Failure Symptom Questionnaire, which
has been previously validated in this population.103,154
98
Other causes may be responsible for hypoabluminemia in HF patients including liver
dysfunction, renal failure and hemodilution. Therefore, alanine aminotransferase
(ALT) was used to evaluate the liver function, a history of renal failure, creatinine (Cr),
blood urea level and estimated glomerular filtration rate (eGFR) for renal dysfunction.
eGFR was measured using Modification of Diet in Renal Disease (MDRD) formula in
our study.155 Weight changes between baseline and discharge was recorded and those
with a weight loss >5% between baseline and 3 months was considered significant.
9.2.3 End points
The primary outcome for the study was all-cause mortality within 1 year. Secondary
outcome included cardiovascular death and HF re-hospitalization within 1 year.
9.2.4 Statistical analysis
All continuous variables were expressed as mean ± SD and categorical parameters
were presented as frequency (percentage) as appropriate. The difference between
continuous variables was analyzed by student t-test. Chi-square or Fisher exact test
was used for categorical data. Kaplan-Meier survival curves were constructed to
demonstrate 1 year survival in patients with low versus normal albumin levels. The
log-rank test was used to determine if actuarial survival was significantly different.
Hazard ratios (HR) were calculated using Cox-proportional hazards models. We put
27 variables into univariate analysis for screening the predictors of 1 year mortality in
99
our study. They were: gender, age, atrial fibrillation (AF), coronary artery disease
(CAD), chronic obstructive pulmonary disease (COPD), renal failure, diabetes
mellitus (DM), primary cerebrovascular disease(CVD), hyperlipidemia, current
smokers, systolic blood pressure (SBP) at admission, diastolic blood pressure (DBP)
at admission, heart rate at admission, hemoglobin (Hb), sodium, blood urea, Cr, ALB,
ALT,
and
medications
at
discharge
[diuretics,
aldosterone
antagonists,
angiotensin-converting enzyme inhibitor or angiotensin II receptor blockers
(ACEI/ARB), beta-blocker, calcium channel blockers (CCB), digoxin, warfarin,
statins]. A p≤0.05 (2-tailed) was considered as statistically significant. Data were
analyzed using SPSS 17.0 for Windows (SPSS, Inc, Chicago, IL).
9.3 Results
9.3.1 Baseline patient characteristics
We studied 576 patients (209 males, mean age 77±10 years, range 32-98 years) with
HFPEF (among cases, 83% in NYHA class III or IV). Baseline characteristics of the
study cohort are shown in Table 6-1. The mean LV ejection fraction (EF) was 64±9%
and mean albumin was 36g/L (range 29-53g/L). According to serum albumin ≤34g/L
defined as hypoalbuminemia, 160 patients (28%) were hypoalbuminemia.
Hypoalbuminemia was more common in patients with DM, chronic renal failure, low
Hb and lower levels of serum sodium, higher levels of blood urea and Cr. There was
no difference in LVEF, NYHA class III-IV, CAD, AF or COPD between those with
and without hypoalbuminemia. Patients with hypoalbuminemia were less likely to be
100
treated with ACEI/ARB, diuretics or beta-blockers at discharge.
9.3.2 Hypoalbuminemia and Cardiac Events
All patients were followed up for 1 year and 134 died within this period. The survival
was significantly lower in patients with hypoalbuminemia compared to those without
at 1 year follow-up (50% vs. 84%, Log-rank χ2=53.3, p<0.001; Figure 9-1), unadjusted
hazard ratios [HR 3.26, 95% confidence interval (CI) 2.33 to 4.57, p<0.001].
Cardiovascular mortality was also significantly higher in HFPEF patients with
hypoalbuminemia than those without at 1 year (21.8% vs. 8.9%, Log-rank χ2=19.7,
p<0.001; Figure 9-2). When using the albumin level as a continuous variable for
survival analysis, we found that the Area under Curve (AUC) was 0.70 for the
prediction of 1 years’ survival (Cut-off value: 34 g/L, sensitivity 79%, specificity 50%,
p<0.001; Figure 9-3). There was no significant difference in HF re-hospitalization
between those with and without hypoalbuminemia during follow-up of 1 year.
Univariate analysis indicated that HFPEF patients with hypoalbuminemia had an
unadjusted HR of 3.26 (95% CI 2.33-4.57, p<0.001; Table 6-2) for 1 year mortality,
compared to normal serum albumin group patients. Other variables entered into the
multivariable model included: age, male, Cr levels, SBP, history of CVD, history of
DM, blood urea levels, Hb levels and use of ACEI/ARB at discharge (Table 6-2).
After adjusting for these potential confounders in the multivariable analysis,
hypoalbuminemia (HR, 3.18; 95% CI, 2.77-4.45; p<0.001), history of CVD (HR, 1.67;
101
95% CI, 1.07-2.59; p=0.02) and older age (HR, 1.03; 95% CI, 1.01-1.05; p=0.02) were
found to be the most powerful predictors of all cause mortality in HFPEF patients at 1
year (Table 9-2).
9.3.3 Albumin and body mass index (BMI)
The prevalence of hypoalbuminemia appeared to differ in BMI groups (29.4%, 24.5%,
23.6% and 20.0% in HFPEF patients with BMI<18.5, 18.5-24.9, 25-29.9 and >30,
respectively although this was not statistically significant (p=0.91). After stratification
of the study cohort into subgroups based on BMI, hypoalbuminemia remained
significantly associated with increased mortality. In HFPEF patients with
hypoalbuminemia, we found that there was no significant difference of 1 year survival
among patients with BMI<18.5 (50%), 18.5-24.9 (69%), 25-29.9 (73%) and >30 (60%)
(p=0.69).
9.3.4 Causes of hypoaluminemia in HFPEF patients
The mean level of ALT was normal in our study cohort, and there was no difference
between the patients with hypoalbuminemia and those without (Table 9-1). The serum
level of Cr, BUN and history of renal failure were significantly higher in patients with
hypoalbuminemia than those without. However, eGFR was not significantly different
between the two groups (Table 9-1).
Finally, to analyze the effect of hemodilution in HFPEF patients, the weight records at
102
baseline and 3 months were checked but were available only in 17% (102/611) HFPEF
patients of this study and therefore this was not analyzed further.
9.4 Discussion
In this study, we found that hyopalbuminemia was present in approximately 30% of
HFPEF patients. At baseline, patients with hypoalbuminemia had lower levels of
serum sodium and Hb but higher levels of blood urea and Cr, which support the
hypothesis that HFPEF patients with hypoalbuminemia may have more serious heart
failure with some degree of renal failure. Importantly, the presence of
hypoalbuminemia was associated with significantly increased risk of all causes death
or death attributed to cardiovascular cause. Increased morbidity and mortality
persisted even after adjustments for potential confounders such as hypertension,
diabetes, renal insufficiency and ischemic heart disease.
Our results differ from the data of Uthamalingam156 et al, the only published study
about the relationship between serum albumin and outcome in HFPEF. They found
that in a cohort of 438 patients with acute decompensate heart failure 54% overall had
hypoalbuminemia (defined as a serum albumin ≤34g/L) and was predictive of
outcome but mainly in those with reduced EF heart failure (SHF). In the preserved
LVEF group (with LVEF >40%, n=194) there was no difference in 1 year mortality
between those with or without hypoalbuminemia. However, the mortality in those
with hypoalbuminemia was slightly higher in the SHF group than HFPEF (23% versus
103
19%; NS). However, in our study there was a clear and strong relationship between
hypoalbuminemia and total and cardiovascular mortality. One possible explanation is
the larger numbers in this study with 576 HFPEF patients (compared to 194) with 134
deaths, compared to 37 deaths in the HFPEF group in the Uthamalingam156 et al study.
Albumin is a hepatic protein, and its plasma concentration is mainly influenced by
several factors, including rate of albumin synthesis, exogenous albumin loss and
dilution.157-160 Synthesis of albumin is affected by nutritional intake, colloid oncotic
pressure variations, and liver function.157, 160 Plasma albumin levels are known to be
decreased in inflammatory conditions, including infection, trauma, and surgery.2
Fillipatos160 et al found that baseline hypoalbuminaemia was associated with increased
risk of incident HF during 10 years of follow-up among community-dwelling older
adults without HF. Low plasma levels in HF are probably due to multiple causes
including malnutrition, reduced synthesis due to hepatic congestion, hemodilution,
increased metabolic activity, inflammation, and proteinuria.161
9.4.1 Liver dysfunction
Liver dysfunction may be a main cause of hypoalbuminemia in HF patients.
Uthamalingam155 et al found that low serum albumin levels were associated with
increased severity of tricuspid regurgitation. Previous studies have linked right atrial
pressure and tricuspid regurgitation to hypoalbuminemia.162, 163 These suggested a
potential causal role, with loss of nutrients due to increased hepatic venous congestion,
104
decreased hepatic synthesis of albumin, as well as protein-losing enteropathy. In our
cohort, we used the ALT to evaluate the liver function of HFPEF patients and found
that there was no difference in ALT level at baseline between the patients with
hypoalbuminemia and those without. We did not found sufficient evidence to support
the concept that liver dysfunction maybe the cause of hypoalbuminemia in HFPEF
patients.
9.4.2 Hemodilution
Hemodilution may be present in HFPEF and contributes to hypoalbuminemia.164
Unfortunately, the weight changes recordings were only available in 17% of our study
population, and could not be used as the evidence to support this mechanism. In a
recent study of dilutional anemia, Abramov165 et al found a higher prevalence of an
expansion of plasma volume in patients with HF and low EF compared with those with
HFPEF (100% vs. 71%). They concluded that dilutional anemia caused by an
expansion in plasma volume without a red cell deficit occurs more commonly in
patients with HF and low EF than those with HFPEF. Probably the same arguments
can be applied to hypoalbuminemia.
9.4.3 BMI and hypoalbuminemia
“Wasting disease” in HF, also known as cardiac cachexia, has been identified as a
strong predictor of adverse prognosis.166 This undernutrition, which has been variably
defined as weight loss over time, low BMI, low percent ideal body weight, or
105
decreased fat mass, has invariably been linked to poor outcomes in HF.167, 168 Many
studies have suggested that decreased protein synthesis due to cardiac cachexia may be
a cause of low albumin in HF.168-170 Our study found that there was an inverse trend
between the percentage of hypoalbuminemia and BMI in patients with HFPEF, but
this was not statistically significant. Moreover, Uthamalingam155 et al found no
relationship between hypoalbuminemia and BMI. We compared the survival of
patients with lower BMI and normal BMI in the hypoalbuminemia group and found
that there was no significant difference in 1 year survival. This suggests that
hypoalbuminemia
and
cachexia
in
HFPEF
patients
may
have
discrete
pathophysiologic mechanisms and that hypoalbuminemia may not only be related to
energy intake.
9.4.4 Renal failure
In chronic disease states such as end-stage renal disease or dialysis and diabetes,
hypoalbuminemia is common and it may be due to renal failure. In our study, we found
that HFPEF patients with hypoalbuminemia were more likely to have history of
chronic renal failure and higher Cr as well as blood urea levels than those without. This
result suggested the renal dysfunction was one of the causes of hypoalbuminemia in
HFPEF patients, which was similar with patients in systolic heart failure.2, 10 Renal
failure, per se is not a cause of hypoalbuminemia, except for patients with underlying
nephrotic syndrome. Rather, it probably reflects more severe HF. There is also
evidence that severe hypoalbuminemia promotes fluid retention and edema through a
106
lowering in plasma oncotic pressure, which may in turn aggravate both cardiac and
renal failure. 26
9.4.5 B-type Natriuretic Peptides and albumin
Several studies have found that serum albumin was a strong and independent
predictor of adverse outcome in patient with HF even after adjusting for B-type
Natriuretic Peptides (BNP).172 437 consecutive patients with chronic systolic HF
under optimal treatment, BNP (RR 1.48, 95% CI 1.28-1.72, p <0.001) and serum
albumin (RR 0.88, 95% CI 0.79-0.98, p=0.015) independently predicted cardiac
death during a median follow up of 1,113 days.173 A similar observation was made in
patients with acute HF syndromes. In 707 consecutive patients admitted to 2
hospitals with this condition, BNP (HR 1.29, 95% CI 1.08-1.54, p=0.004), and serum
albumin (HR 0.92, 95% CI
0.89－0.96, p <0.001) independently predicted death
during a median follow-up of 421 days.172 Hypoalbuminemia was also identified as
the strongest prognostic factor in 349 elderly patients with acute HF, along with prior
HF hospitalization, serum sodium, blood urea nitrogen and BNP.174 In another
clinical study that included 146 nonagerians, serum albumin was the sole predictor of
mortality (HR 0.28 95% CI 0.14-0.54, p< 0.001) after adjusting for age, arterial
blood pressure, left ventricular ejection fraction, BNP, and routine laboratory tests.175
However, the studies mentioned above were focus on the SHF patients rather than
HFPEF patients. Unfortunately we have no data of BNP in our HFPEF patients.
Relationship of BNP and albumin in HFPEF patients still needed further study.
107
Evidence is growing that hypoalbuminemia may contribute to the progression of the
syndrome of heart failure through a facilitation of pulmonary edema, myocardial
edema, fluid retention, diuretic resistance, oxidative stress, inflammatory state. This
may in part explain the powerful prognostic relevance of serum albumin in heart
failure regardless of its phenotype and comorbidities.26
Our finding indicated the hypoalbuminaemia was the most powerful predictor for the
prognosis of HFPEF patients. Since hypoalbuminaemia is also a marker of
malnutrition, it provides further impetus for prospectively examining a potential
preventative or therapeutic role of nutritional intervention in HFPEF patients.
Limitations
We acknowledge certain limitations in the current study. First, this study was
observational and evaluated a cohort of patients with acutely decompensate heart
failure. Patients with chronic stable heart failure were excluded. Second, hemodilution
is one of the cause of hypoalbuminemia, but we do not have enough data to analyze;
also ALT may not be sensitive enough used as a marker of liver dysfunction, but it was
the only available marker of live function in this study. Third, serum markers of
inflammation, such as C-reactive protein which would help to delineate the
pathophysiologic relationship between albumin, inflammation, and mortality were not
measured. Forth, proteinuria was not assessed in our patients, which was a potential
108
cause of hypoalbuminemia but it unlikely to be the main cause in this group of patients.
Finally, we are unable to compare albuminemia with BNP in predicting outcome of
HF patients.
9.5 Conclusions
The results of this study indicate that hypoalbuminemia was common in HFPEF
patients and that HFPEF patients with hypoalbuminemia have increased risk of
mortality compared to those without hypoalbuminemia, even after adjustment for
multiple prognostic factors. Albumin levels could serve as a simple biomarker for
identifying HFPEF patients at a higher risk of death as in reduced EF heart failure.
Renal dysfunction may be the main pathophysiologic mechanism underlying
hypoalbuminemia in HFPEF patients. Further investigation into mechanisms
underlying hypoalbuminemia is warranted and may result in identifying potential
novel targets for HFPEF therapy.
109
Table 9-1 Baseline characteristics by the status of albumin in patients with HFPEF
Parameters
All HFPEF
ALB<34g/L ALB>34g/L p value
(n=576)
(n=160)
(n=416)
Age, years
77±10
78±9
76±11
0.07
Female, n (%)
367 (64)
97 (61)
270 (65)
0.34
Smokers, n (%)
139 (24)
35(22)
104 (25)
0.03
SBP, mmHg
154±33
149±36
156±31
0.003
LVEF, %
63.7±8.9
63.4±9.1
62.7±8.1
0.42
NYHA Class III-IV, n (%)
433 (83)
118 (84)
315 (82)
0.58
BMI, kg/m2
24.5±4.5
24.1±4.2
24.8±4.4
0.17
Hypertension, n (%)
373(65)
97 (61)
276 (66)
0.23
DM, n (%)
238 (41)
79 (50)
159 (38)
0.01
CAD, n (%)
141(25)
33 (21)
108 (26)
0.20
AF, n (%)
159(28)
40 (25)
119 (29)
0.40
COPD, n (%)
64 (11)
13 (8)
51 (12)
0.16
CVD, n (%)
72 (13)
20 (13)
52 (13)
0.98
Hyperlipidemia, n (%)
94 (16)
23 (15)
71 (17)
0.45
History of renal failure, n (%)
97 (17)
45 (28)
52 (13)
<0.001
Prior history of HF, n (%)
218 (38)
62 (39)
156 (38)
0.74
Demographic data
110
Laboratory
Serum Cr, umol/L
145.6±113.1 184.8±161.6 126.9±71.6
<0.001
eGFR, mL/min/1.73 m2
51.8±32
50.3±47
52.9±22
0.51
ALT, IU/L
33.9±54
33.9±63
33.9±50
0.99
Hb, g/dl
11.5±2.4
10.4±2.5
11.9±2.1
<0.001
Blood urea, mmol/L
10.8±10.6
13.1±15.3
9.7±8.2
0.006
ALB, g/L
36±5
29±4
39±3
<0.001
Serum sodium, mmol/L
138.6±4.9
137.7±5.4
139.0±4.6
0.01
Total scores
32.6±15.0
33.6±14.0
32.0±15.0
0.41
Physical Subscale Scores
18.3±7.6
18.6±7.3
18.2±7.8
0.71
Emotional Subscale Scores
6.3±4.7
6.6±4.5
6.2±4.8
0.56
ACEI/ARB, n (%)
298 (52)
63 (39)
235 (57)
<0.001
Beta-blocker, n (%)
238 (41)
55 (34)
183 (44)
0.04
Aldosterone antagonists, n (%) 28 (5)
7 (4)
21 (5)
0.74
Digoxin, n (%)
75 (13)
15 (10)
60 (14)
0.11
CCB, n (%)
139 (24)
29 (18)
110 (26)
0.04
Diuretics, n (%)
407 (71)
101 (63)
306 (74)
0.01
QOL scores
Medications
SBP, systolic blood pressure at admission; LVEF, LV ejection fraction; NYHA, New
York Heart Association; BMI, body mass index; DM, diabetes mellitus; CAD,
coronary artery disease; AF, atrial fibrillation; COPD, chronic obstructive pulmonary
111
disease; CVD, primary cerebrovascular disease; HF, heart failure; Cr, creatinine;
eGFR, estimated glomerular filtration rate; ALT, alanine aminotransferase; Hb,
hemoglobin; BUN, blood urea nitrogen; ALB, serum albumin; QOL, quality of life
ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor
blockers; CCB, Calcium channel blockers.
112
Table 2 Cox-regression of all cause mortality within 1 year for HFPEF patients
Univariate analysis
Multivariate analysis
Variable
HR
95%CI
p value
HR
95%CI
p value
Age, per year increase
1.03
1.01-1.05
0.003
1.03
1.01-1.05
0.02
Male
1.35
0.97-1.88
0.08
-
-
-
umol/L 1.01
1.00-1.01
<0.001
-
-
-
Serum
Cr,
per
increase
SBP, per mmHg increase
0.99
0.98-1.00
0.03
-
-
-
History of CVD
1.61
1.05-2.47
0.03
1.67
1.07-2.59
0.02
History of DM
0.73
0.51-1.03
0.07
-
Serum albumin<34g/L
3.26
2.33-4.57
<0.001
3.18
2.27-4.45
<0.001
Blood urea, per mmol/L 1.01
1.01-1.02
0.01
-
-
-
-
increase
Hb, per g/L increase
0.89
0.83-0.96
0.002
-
-
-
ACEI/ARB at discharge
0.66
0.47-0.93
0.02
-
-
-
HR, Hazard ratios; 95% CI, 95% confidence intervals; Cr, creatinine; SBP, systolic
blood pressure at admission; CVD, primary cerebrovascular disease; DM, diabetes
mellitus; Hb, hemoglobin; ACEI, angiotensin-converting enzyme inhibitor; ARB,
angiotensin II receptor blockers;
113
Figure 9-1 Kaplan-Meier survival analysis showing HFPEF patients with
hypoalbuminemia (Serum albumin≤34g/L) had significantly worse survival than
patients without hypoalbuminemia.
114
Figure 9-2 Kaplan-Meier survival analysis showing HFPEF patients with
hypoalbuminemia (Serum albumin≤34g/L) had significantly higher cardiovascular
death rate in 1y than patients without hypoalbuminemia.
115
Figure 9-3 ROC curve of prediction of serum albumin levels in 1 year mortality for
HFPEF patients
Serum albumin
Cut-off value
Sensitivity
Specificity
AUC
P value
34
79%
50%
0.70
<0.001
116
CHAPTER 10 GENERAL SUMMARY
10.1 Main findings of our study
Our study was first study which investigated the characteristics, mortality, quality of
life and prognosis factors of HFPEF patients in Chinese population. We found that the
HFPEF patients admitted in recent 5 year had lower mortality and heart failure
re-hospitalization rate in the first year than the previous cohort. We also studied the
change of health related Quality of life among HFPEF patients in a long-term
follow-up for the first time. We found that elderly HFPEF patient experienced similar
improvements in QOL compared to younger patients during follow-up after an index
hospital admission and therefore should not be denied treatment. Further more, we
have developed a risk score which reliably predicts 1 year mortality in patients with
HFNEF and which is based on 6 clinical factors routinely collected at the time of
admission. Hypoalbuminemia was found to be the most powerful predictor of 1 year
mortality for HFPEF patients in our cohort for the first time.
Our study was mainly focus on the all cause mortality and the related predictors for the
mortality of HFPEF patients. Lim et al did a review to analyze the 1-year mortality of
HFPEF patients in observational studies and clinical trials. They found that 1-yeaer
mortality of HFPEF patients were around 22%-29%, which was similar to our study.
However, little studies tried to compare the change of mortality overtime.
We found that the mortality of HFPEF patients improved in the recent five years when
117
compared to previous HFPEF cohorts in our hospital. It may be due to that HFPEF as a
possible diagnosis was more recognized in the past 5 years. Improved treatment may
also be a factor.
For HFPEF patients, there was little study about the health-related quality of life,
especially in elderly patients. We found that HRQOL in very elderly HFPEF patients
can improve to the same extent as younger HFPEF patients after 1 year. In addition, in
this study there was no difference in the medication prescribed in 1 year between
elderly HFPEF patients and younger ones which may be connected to the HRQOL
response.
In order to find the predictors of 1 year mortality for HFPEF patients, we built up and
validated a risk model for predicting 1 year mortality of HFPEF patients using 6
simple and clinical based parameters. Application of the risk score could influence the
quality of care provided to patients hospitalized with HFPEF, which might be
important in clinical practice especially in an ageing society like Hong Kong.
In the course of finding predictors of death for HFPEF patients, we found that we
found that hyopalbuminemia was present in approximately 30% of HFPEF patients.
Furthermore, the presence of hypoalbuminemia was associated with significantly
increased risk of all causes death or death attributed to cardiovascular cause. Increased
morbidity and mortality persisted even after adjustments for potential confounders
118
such as hypertension, diabetes, renal insufficiency and ischemic heart disease. Renal
dysfunction
may
be
the
main
pathophysiologic
mechanism
underlying
hypoalbuminemia in HFPEF patients. These results suggests physicians to pay
attention to the albumin level for HFPEF patients
In summary, the mortality and heart failure re-hospitalization rate in the first year was
decreased over time in HFPEF patients. Elderly HFPEF patient experienced similar
improvements in QOL compared to younger patients during follow-up after an index
hospital admission and therefore should not be denied treatment. An assessment model
using clinical available parameters was useful in predicting 1 year mortality for
HFPEF patients. Hypoalbuminemia was the most powerful predictor of 1 year
mortality for HFPEF patients.
10.2 Clinical implications
Our study found that lower albumin was related to higher mortality of HFPEF patients.
It suggested that physicians should pay more attention to the nutrition status of HFPEF
patients. Furthermore, we found that renal failure would be a cause for
hypoalbuminemia in HFPEF patients. As a result, we should be caution with the renal
function of HFPEF patients in the therapy, especially when using high dose of
diuretics.
Second, we found that the outcome of HFPEF patients improved in the past ten years
119
in our cohort. Although the reason of better outcome was still unclear, we suspected
that more awareness of the disease would be a reason. Our study also suggested maybe
a HF registry could benefit for HFPEF patients.
Third, we established a risk score for predicting 1 year mortality of HFPEF patients.
The risk score may instruct and help with the therapy of HFPEF patients. As
mentioned before, physicians would be more careful of renal functions and diuretics
doses of HFPEF patients. They would also pay more attention to the meeting the target
of anti-hypertension therapy for HFPEF patients, as hypertension was served as a risk
factor for the outcome of HFPEF patients.
Finally, we found that QOL had improved similarly in very elderly HFPEF patients as
with the younger ones. Many studies showed that anti-hypertension therapy was less
enough in very elderly HFPEF patients. There would be many reasons for that. One of
the reasons would be the side-effect of the drugs used in the treatment of HFPEF
patients such as ACEI/ARBs and beta-blockers. Co-morbidity such as renal failure
and diabetes would be another concern. These findings suggested that therapy of
HFPEF patients including anti-hypertension therapy should be similar in both elderly
and younger patients.
10.3 Potential for final development of research
First, the sample size of our study is still relatively small. As a result, predictive
120
value of the risk score built on our study still needed to revise in the future. Second,
the reason of why HFPEF patients admitted to hospital recently had better outcome
than the earlier cohort was still not clear. Next step we will try to compare the exact
drugs and doses used in two cohorts of HFPEF patients. Third, we will follow-up
HFPEF patients for longer time and build a new risk score for predicting long-term's
outcome.
In addition to improve the quality of current studies by enlarging sample sizes, we
will try to find more predictors for HFPEF patients in the future. SENIORS study
found that there was a trend that nebivolol could reduce all cause mortality of HFREF
patients when compared with placebo. But few HFPEF patients were included in this
trial. There was still unclear that whether heart rate would be a risk factor for HFPEF
patients. We can try to do some analysis to check if HR could predict mortality for
HFPEF patients.
A study found that lower cholesterol level was associated with worse outcome of
HFPEF patients. This finding was agreed with our results that history of
hyperlipidemia was associated with better outcome in our cohort. We can try to
check the lipid levels including total cholesterol, LDL-C, HDL-C and TG for each
HFPEF patients. Based on this, we can confirm whether higher LDL-C was an
independent factor of death for HFPEF patients. We can also find if statin therapy is
related to the outcome of HFPEF patients.
121
When regard to HRQoL, we can also try to do more analysis on the HFPEF patients.
There had been studies which illustrated that QoL impaired similarly in HFPEF and
HFREF patients. However, there have not been many studies which was focus on the
long term's change of QoL between HFREF and HFPEF until now. We can do a
longer follow-up to check if change of QoL was similar in both HFREF and HFPEF
patients after a long term's follow-up.
122
CHAPTER11 CONCLUSIONS
In this cohort study, we found that the mortality and heart failure re-hospitalization
rate in the first year was decreased over time in HFPEF patients. Elderly HFPEF
patient experienced similar improvements in QOL compared to younger patients
during follow-up after an index hospital admission. A risk score derived from
commonly available clinical variables can be used to predict 1 year mortality of
HFPEF patients, which might be useful in clinical practice. Hypoalbuminemia was the
most powerful predictor of 1 year mortality for HFPEF patients in our study.
123
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