1. health and fitness
2. disease

Original Investigation
GFR and Cardiovascular Outcomes After Acute Myocardial
Infarction: Results From the Korea Acute Myocardial
Infarction Registry
Eun Hui Bae, MD, PhD,1* Sang Yup Lim, MD, PhD,2* Kyung Hoon Cho, MD,1,3
Joon Seok Choi, MD,1 Chang Seong Kim, MD,1 Jeong Woo Park, MD, PhD,1
Seong Kwon Ma, MD, PhD,1 Myung Ho Jeong, MD, PhD,1,3 and
Soo Wan Kim, MD, PhD1
Background: Despite strong evidence linking decreased glomerular filtration rate (GFR) to worse outcomes, the impact of GFR on mortality and morbidity in patients with acute myocardial infarction (AMI) is not
well defined.
Study Design: Retrospective cohort study.
Setting & Participants: 12,636 patients with AMI in the Korea AMI Registry database from November 2005
to July 2008. 93% of patients in this registry had coronary angiography, and 91% of patients with coronary
angiography had percutaneous coronary intervention (PCI).
Predictor: GFR was estimated (eGFR) using the Chronic Kidney Disease Epidemiology Collaboration
(CKD-EPI) equation, and patients were grouped into 5 eGFR categories: ⬎90, 60-89, 30-59, 15-29, and ⬍15
mL/min/1.73 m2.
Outcomes: Primary end points were death and in-hospital complications. Secondary end points were major
adverse cardiac events (MACEs) during a 1-month (short-term) and 1-year (long-term) follow-up after AMI.
Results: Mean eGFR was 72.8 ⫾ 24.6 mL/min/1.73 m2, mean age was 64 ⫾ 13 years, and 70.4% were
men. A graded association was observed between eGFR and clinical outcomes. In adjusted analyses,
compared with eGFR ⬎90 mL/min/1.73 m2, patients with eGFR of 30-59, 15-29, and ⬍15 mL/min/1.73 m2
experienced increased risks of short- (respective HRs of 2.30 [95% CI, 1.70-3.11], 3.10 [95% CI, 2.14-4.14],
and 3.64 [95% CI, 2.44-5.43]; P ⬍ 0.001) and long-term MACEs (HRs of 1.58 [95% CI, 1.32-1.90], 2.12 [95%
CI, 1.63-2.75], and 2.50 [95% CI, 1.89-3.29]; P ⬍ 0.001). Older age, Killip class higher than I, PCI, and
high-sensitivity C-reactive protein level also were associated with higher short- and long-term MACEs. Use of
␤-blockers, angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs), and
statins was associated with decreased risk of MACEs.
Limitations: Single assessment of serum creatinine.
Conclusion: eGFR was associated independently with mortality and complications after AMI. PCI, ␤-blocker,
ACE inhibitor or ARB, and statin use were associated with decreased risks of short- and long-term MACEs.
Am J Kidney Dis. 59(6):795-802. © 2012 by the National Kidney Foundation, Inc.
INDEX WORDS: Acute myocardial infarction; glomerular filtration rate; major adverse cardiac event.
C
hronic kidney disease (CKD) is a worldwide public health problem.1 Kidney function is a strong
risk factor for fatal and nonfatal cardiovascular events,2
with patients requiring long-term renal replacement
therapy at particularly high risk.3 The most common
cause of death in patients with CKD is cardiovascular
disease.4
There is limited accuracy in using serum creatinine
level as an indicator of kidney function on account of
a nonlinear association with glomerular filtration rate
(GFR) that varies by age, sex, race, and lean body
mass.5,6 As a result, the National Kidney Foundation
recommends using estimates of GFR determined from
validated equations instead of serum creatinine level
to define decreased kidney function. Moreover, recent
studies suggested that even mildly decreased kidney
function is an independent predictor of long-term
mortality in patients who have known or suspected
coronary artery disease.5,7,8 The reasons for this assoAm J Kidney Dis. 2012;59(6):795-802
ciation may be higher prevalences of coronary risk
factors and lower use of established strategies to
modify cardiovascular risk.9 However, although there
is strong evidence linking lower GFR to poorer outcomes, the effect of GFR on mortality and morbidity
From the 1Department of Internal Medicine, Chonnam National
University Medical School, Gwangju; 2Department of Internal
Medicine, Korea University, Ansan; and 3Cardiovascular Research Institute of Chonnam National University, Gwangju, Korea.
* E.H.B. and S.Y.L. contributed equally to this work.
Received April 29, 2011. Accepted in revised form January 21,
2012. Originally published online March 26, 2012.
Address correspondence to Soo Wan Kim, MD, PhD, Department of Internal Medicine, Chonnam National University Medical
School, Hakdong 8, Dongku, Gwangju 501-757, Korea. E-mail:
[email protected]
© 2012 by the National Kidney Foundation, Inc.
0272-6386/$36.00
doi:10.1053/j.ajkd.2012.01.016
795
Bae et al
in patients who experience acute myocardial infarction (AMI) may not be fully appreciated, and patients
with decreased GFR receiving acute cardiac care may
receive less aggressive treatments than usual or be
less likely to received evidence-based therapies as
those without decreased GFR.10-13 In addition, providers may be hesitant to explore coronary anatomy due
to potential contrast toxicity.
The present study was undertaken to evaluate the
effect of kidney function on clinical outcomes, including major adverse cardiac events (MACEs), in patients with AMI. In addition, we explored prognostic
factors of AMI in relation to level of GFR.
METHODS
Korea AMI Registry
The Korea AMI Registry (KAMIR) is a prospective multicenter
online registry designed to describe characteristics and clinical
outcomes of patients with AMI and reflects current management of
patients with AMI in Korea. The registry included 52 community
and university hospitals with the capability of primary percutaneous coronary intervention (PCI). Data were collected retrospectively at each site by a trained study coordinator based on a
standardized protocol. We analyzed the enrolled data to investigate
the impact of GFR on mortality and morbidity in patients with
AMI. The study protocol was approved by the ethics committee at
each participating institution, and all patients were informed about
their participation in this registry.
Study Design and Patient Population
The registry included 13,901 consecutive patients who were
admitted to the hospital between November 2005 and July 2008,
whose discharge diagnosis was AMI based on cardiac enzyme
levels and electrocardiographic findings. Patients who were lost to
follow-up within 1 year of AMI and those with missing data were
excluded. Overall, 12,636 patients (91% of the cohort) had all data
available for calculation of estimated GFR (eGFR) and constituted
the final study sample.
Exposure
The CKD Epidemiology Collaboration (CKD-EPI) equation
was used to estimate GFR in milliliters per minute per 1.73 m2.14
Serum creatinine concentration was determined by the Jaffé method,
which was calibrated to isotope-dilution mass spectrometry. Patients were divided into 5 eGFR groups corresponding to strata
used to define CKD stages9: ⱖ90, ⱖ60-⬍90, ⱖ30-⬍60, ⱖ15⬍30, and ⬍15 mL/min/1.73 m2.
Outcomes
AMI was defined as the presence of clinical signs or symptoms:
increased cardiac biomarker levels (creatine kinase-MB [0-25
U/L], troponin I [0-0.1 ng/mL], or troponin T [0-0.04 ng/mL]), and
12-lead electrocardiographic findings. For patients with AMI,
ST-segment elevation myocardial infarction (STEMI) was defined
as the presence of new ST-segment elevation of at least 1 mm (0.1
mV) in continuous leads or new left bundle-branch block on the
index electrocardiogram, and those who were not classified as
STEMI were considered to have non-STEMI (NSTEMI) given the
presence of positive biomarkers in all. Left ventricular ejection
fraction (LVEF) was checked by 2-dimensional echocardiography
using a modified Simpson’s method. Primary end points were
796
death and complications, including cardiogenic shock, ventricular
tachycardia and fibrillation (need for antiarrhythmic agent and/or
defibrillation), atrioventricular block (need for pacemaker), recurrent ischemia and myocardial infarction (MI), cerebrovascular
accident, major bleeding, or multiorgan failure during hospitalization. Secondary end points were MACEs, including cardiac death,
MI or stroke, need for emergency or elective repeated revascularization, or coronary artery bypass graft (CABG) surgery during
follow-up.
Other Variables
Baseline variables included age, sex, body mass index, and
several coronary risk factors, including hypertension (defined as
history of hypertension, admission blood pressure ⬎140 mm Hg
systolic or ⬎90 mm Hg diastolic), current smoking, previous
history of ischemic heart disease, hyperlipidemia (defined as
history of hyperlipidemia, total cholesterol level of 240 mg/dL, or
low-density lipoprotein [LDL] cholesterol level ⬎101 mg/dL),
diabetes mellitus (DM; defined as history of DM or random blood
glucose level ⬎200 mg/dL), clinical symptoms at admission (chest
pain or dyspnea), high-sensitivity C-reactive protein (hs-CRP)
level, and Killip class.
Use of certain medications was recorded on admission (aspirin,
angiotensin-converting enzyme [ACE] inhibitors, angiotensin receptor blockers [ARBs], diuretics, statins, ␤-blockers, or vasopressors). Surgical interventions (CABG, thrombolysis, or PCI) and
coronary care unit stay were recorded.
Statistical Analysis
Continuous variables with normal distributions were expressed
as mean ⫾ standard deviation for each of the 5 groups based on
level of kidney function and were compared using 1-way analysis
of variance. Continuous data with skewed distribution were presented as median (with 25th and 75th percentiles) and compared
using the Kruskal-Wallis test. Categorical variables were compared using ␹2 tests, when appropriate. All P values were 2 sided,
with ␣ ⫽ 0.05. Univariate and multivariate analyses were performed to determine the prognostic significance of clinical variables for in-hospital and long-term clinical end points. Variables
with P ⬍ 0.25 on univariate analysis were entered into multivariate
logistic regression models and Cox proportional hazard models.
Multivariate logistic regression modeling was used to compare
in-hospital outcomes by eGFR stratum, and candidates for adjustment included age, sex, comorbid conditions (previous MI, DM,
hypertension, elevated lipid concentrations, and current smoking),
Killip class higher than I, LVEF ⬍40%, therapeutic modalities and
medical treatments during hospitalization, angiographic findings,
and PCI-related complications. Cox proportional hazards modeling was used to compare 1-month and 1-year clinical outcomes by
eGFR. The following variables were included in adjustment: age,
sex, comorbid conditions (previous MI, previous PCI, DM, hypertension, and current smoking), Killip class higher than I, LVEF
⬍40%, therapeutic modalities, medical treatments during hospitalization, and hs-CRP level. Increasingly adjusted models were built
for 1-month and 1-year MACEs to assess the relative confounding
contributed by certain factors. Statistical analysis was done with
SPSS software, version 17.0 for Windows (SPSS Inc, www.spss.
com).
RESULTS
Baseline Characteristics
A total of 12,636 patients were included in the present
study; 3,491 (27.6%) patients with eGFR ⬎90 mL/min/
Am J Kidney Dis. 2012;59(6):795-802
GFR in Acute Myocardial Infarction
Table 1. Baseline Characteristics
Baseline Variables
Age (y)
Men
BMI (kg/m2)
Risk factor
Hypertension
Diabetes
mellitus
Smoking
Hyperlipidemia
History of IHD
At admission
SBP (mm Hg)
DBP (mm Hg)
Killip class
Diagnosis
STEMI
NSTEMI
Group 1
eGFR >90
(n ⴝ 3,491)
Group 2
eGFR 60-89
(n ⴝ 5,791)
Group 3
eGFR 30-59
(n ⴝ 2,609)
Group 4
eGFR 15-29
(n ⴝ 439)
Group 5
eGFR <15
(n ⴝ 306)
P
55.1 ⫾ 10.5
2,799 (80.2)
64.1 ⫾ 11.8
4,215 (72.8)
71.9 ⫾ 10.2
1,485 (56.9)
73.7 ⫾ 9.5
211 (48.1)
66.2 ⫾ 12.3
178 (58.2)
⬍0.001
⬍0.001
24.3 ⫾ 3.5
24.0 ⫾ 3.5
23.5 ⫾ 3.5
23.0 ⫾ 3.3
23.5 ⫾ 4.1
⬍0.001
1,334 (38.2)
751 (21.5)
2,618 (45.2)
1,390 (24.0)
1,649 (63.2)
965 (37.0)
331 (75.3)
241 (54.9)
222 (72.5)
176 (57.4)
⬍0.001
⬍0.001
2,381 (68.2)
363 (10.4)
394 (11.3)
3,463 (59.8)
521 (9.0)
880 (15.2)
1,143 (43.8)
243 (9.3)
556 (21.3)
167 (38.1)
51 (11.7)
123 (28.0)
110 (35.8)
35 (11.6)
93 (30.5)
⬍0.001
0.06
⬍0.001
131.3 ⫾ 25.9
81.1 ⫾ 20.1
128.8 ⫾ 27.7
78.4 ⫾ 16.6
122.7 ⫾ 33.8
74.8 ⫾ 26.5
120.7 ⫾ 36.1
71.9 ⫾ 20.0
134.4 ⫾ 38.7
78.9 ⫾ 20.9
⬍0.001
⬍0.001
1.3 ⫾ 0.5
1.5 ⫾ 0.9
1.8 ⫾ 1.0
2.1 ⫾ 1.1
2.2 ⫾ 1.2
⬍0.001
2,170 (62.2)
1,343 (37.8)
3,614 (62.4)
2,226 (37.6)
1,499 (59.4)
1,021 (40.6)
165 (45.1)
217 (54.9)
112 (38.9)
169 (61.1)
⬍0.001
⬍0.001
Note: Categorical variables given as number (percentage); continuous variables, as mean ⫾ standard deviation. eGFR given in
mL/min/1.73 m2.
Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; IHD, ischemic heart
disease; NSTEMI, non–ST-segment myocardial infarction; SBP, systolic blood pressure; STEMI, ST-segment elevation myocardial
infarction.
1.73 m2; 5,791 (45.8%) with eGFR of 60-89 mL/min/1.73
m2; 2,609 (20.6%) with eGFR of 30-59 mL/min/1.73 m2;
439 (3.5%) with eGFR of 15-29 mL/min/1.73 m2; and 306
(2.4%) with eGFR ⬍15 mL/min/1.73 m2; 61.6% had
STEMI; and 39.4% had NSTEMI. Table 1 lists baseline
characteristics of patients by eGFR category at baseline. Overall, median follow-up was 404 days, mean
age was 64 years, 70.4% were men, and mean body
mass index was 24 kg/m2. Hypertension was present
in 48.8%, DM was present in 27.7%, 42.7% were
current smokers, hyperlipidemia was present in 9.5%,
and 16% had a history of ischemic heart disease.
Lower GFR was associated with older age, higher
prevalence of hypertension and DM, lower prevalence of current smoking, and higher Killip class. The
proportion of patients who had NSTEMI was higher
in patients with lower eGFR categories.
Biochemical Parameters, LVEF, and Coronary
Angiographic Findings
Table 2 summarizes baseline continuous variables.
Total cholesterol levels, LDL cholesterol levels, and
LVEF were lower, whereas hs-CRP and NT-pro-BNP
(N-terminal pro–B-type natriuretic peptide) levels were
higher in patients with lower eGFR. Table 3 lists
findings from coronary angiography: the number of
involved vessels was higher with more decreased
eGFR. In contrast, the prevalence of 1-vessel disease
Am J Kidney Dis. 2012;59(6):795-802
was higher in patients with relatively normal kidney
function. Other findings, such as infarct-related artery,
American College of Cardiology/American Heart Association classification,15 and Thrombolysis in Myocardial Infarction (TIMI) flow, also varied by eGFR
category.
Hospital Treatment on Admission by Kidney Function
and Medication
Table 4 lists receipt of specific treatments at admission by eGFR category. The use of thrombolysis and
PCI generally was lower with lower eGFR categories,
although PCI use was slightly higher in patients with
eGFR ⬍15 mL/min/1.73 m2 than in those with eGFR
of 15-29 mL/min/1.73 m2 (Fig 1A). On the contrary,
use of CABG was increased in patients with more
greatly decreased kidney function. Use of ␤-blockers,
ACE inhibitors or ARBs, and statins was lower in
patients with more decreased kidney function, again
with the exception of patients with eGFR ⬍15 mL/min/
1.73 m2 (Fig 1B). In contrast, lower eGFR was
associated with higher use of vasopressors and diuretics.
Outcomes According to eGFR
Table 5 lists in-hospital, short-term, and long-term
outcomes according to eGFR. The estimated inhospital death rate was 1.2% in patients with eGFR
797
Bae et al
Table 2. Biochemical Parameters and LVEF
Baseline Variables
Group 1
eGFR >90
(n ⴝ 3,491)
Group 2
eGFR 60-89
(n ⴝ 5,791)
Group 3
eGFR 30-59
(n ⴝ 2,609)
Group 4
eGFR 15-29
(n ⴝ 439)
Group 5
eGFR <15
(n ⴝ 306)
P
100 ⫾ 9.9
75 ⫾ 8.5
47 ⫾ 8.2
23 ⫾ 4.2
7.6 ⫾ 3.7
⬍0.001
Troponin T (ng/mL)
1.4 (0.3-5.2)
1.8 (0.4-5.6)
1.9 (0.4-6.0)
2.9 (0.8-8.0)
1.6 (0.3-5.8)
0.06
Troponin I (ng/mL)
22 (4-50)
19 (3-50)
17 (3-50)
14 (4-44)
14 (3-45)
0.8
80 (21-206)
79 (19-211)
56 (15-177)
32 (12-100)
29 (10-105)
⬍0.001
Total cholesterol (mg/dL)
184 (158-213)
180 (155-208)
172 (145-203)
164 (135-192)
165 (135-197)
⬍0.001
LDL cholesterol (mg/dL)
119 (96-145)
114 (92-138)
108 (85-134)
98 (71-128)
98 (75-131)
⬍0.001
eGFR (mL/min/1.73 m2)
CK-MB (U/L)
hs-CRP (mg/dL)
0.7 (0.2-3.2)
0.8 (0.2-4.1)
1.6 (0.3-8.2)
4.1 (0.9-15.2)
2.6 (0.6-11.4)
⬍0.001
NT-pro-BNP (pg/mL)
255 (69-849)
430 (105-1,526)
1,750 (354-5,594)
7,575 (2,213-22,424)
24,777 (6,382-35,000)
⬍0.001
LVEF (%)
53.5 ⫾ 11.1
51.9 ⫾ 12.1
48.0 ⫾ 13.7
46.3 ⫾ 12.4
45.9 ⫾ 12.5
⬍0.001
Note: Normally distributed variables given as mean ⫾ standard deviation; non–normally distributed variables, as median (25th-75th
percentile). Conversion factors for units: eGFR in mL/min/1.73 m2 to mL/s/1.73 m2, ⫻0.01669; cholesterol in mg/dL to mmol/L,
⫻0.02586. No conversion necessary for troponins in ng/mL and ␮g/L.
Abbreviations: CK-MB, creatine kinase-MB; eGFR, estimated glomerular filtration rate; hs-CRP, high-sensitivity C-reactive protein;
LDL, low-density lipoprotein; LVEF, left ventricle ejection fraction; NT-pro-BNP, N-terminal pro–B-type natriuretic peptide.
⬎90 mL/min/1.73 m2, but 18.3% in those with eGFR
⬍15 mL/min/1.73 m2. Mean length of coronary care
unit stay was significantly increased at lower eGFRs.
In 1 month, MACE, cardiac death, and noncardiac
death rates were higher at lower GFRs, whereas rates
of AMI and target-lesion revascularization did not
differ across eGFR groups. At 12 months, MACE and
cardiac death rates also increased with lower eGFR.
Tables 6 and 7 list associations between baseline
eGFR category and 1-month and 1-year MACEs in
Table 3. Baseline Coronary Angiographic Findings
Variable
Group 1
eGFR >90
(n ⴝ 3,491)
Group 2
eGFR 60-89
(n ⴝ 5,791)
Group 3
eGFR 30-59
(n ⴝ 2,609)
Group 4
eGFR 15-29
(n ⴝ 439)
Group 5
eGFR <15
(n ⴝ 306)
3,358 (96.2)
5,562 (96.0)
2,270 (87.0)
319 (72.6)
249 (81.4)
0.001
P
Coronary angiography
Infarct-related artery
Left anterior
descending artery
Left circumflex artery
Right coronary artery
Left main stem
1,713 (51.0)
2,648 (47.6)
981 (43.2)
108 (33.7)
109 (43.9)
⬍0.001
581 (17.3)
1,007 (30.0)
57 (1.7)
940 (16.9)
1,874 (33.7)
100 (1.8)
327 (14.4)
890 (39.2)
73 (3.2)
56 (17.5)
141 (44.1)
15 (4.8)
52 (20.9)
75 (30.0)
13 (5.2)
0.02
⬍0.001
⬍0.001
Involved vessel number
1 vessel
2 vessels
3 vessels
Left main, complex
Left main, isolated
1,679 (50.0)
960 (28.6)
635 (18.9)
67 (2.0)
17 (0.5)
2,357 (43.2)
1,713 (31.4)
1,233 (22.6)
136 (2.5)
16 (0.3)
692 (30.5)
697 (30.7)
788 (34.7)
84 (3.7)
9 (0.4)
72 (22.7)
98 (30.6)
127 (39.7)
20 (6.3)
2 (0.6)
62 (24.8)
68 (27.4)
105 (42.3)
13 (5.1)
1 (0.4)
⬍0.001
0.08
⬍0.001
⬍0.001
⬍0.001
ACC/AHA classification
A
B1
B2
C
161 (4.8)
651 (19.4)
987 (29.4)
1,558 (46.4)
267 (4.9)
955 (17.5)
1,522 (27.9)
2,711 (49.7)
95 (4.2)
370 (16.3)
581 (25.6)
1,224 (53.9)
18 (5.5)
49 (15.4)
62 (19.5)
190 (59.6)
7 (2.8)
38 (15.4)
58 (23.4)
145 (58.4)
0.5
0.04
⬍0.001
⬍0.001
TIMI flow
TIMI 0
TIMI 1
TIMI 2
TIMI 3
1,417 (42.2)
386 (11.6)
500 (14.9)
1,054 (31.4)
2,471 (45.3)
611 (11.2)
878 (16.1)
1,495 (27.4)
547 (24.1)
406 (17.9)
295 (13.0)
1,022 (45.0)
133 (41.8)
39 (12.1)
44 (13.7)
103 (32.4)
80 (31.2)
29 (11.8)
41 (16.6)
98 (39.4)
⬍0.001
0.07
0.08
⬍0.001
Note: Values given as number (percentage). eGFR given in mL/min/1.73 m2.
Abbreviations: ACC/AHA; American College of Cardiology/American Heart Association; eGFR, estimated glomerular filtration rate;
TIMI; Thrombolysis in Myocardial Infarction.
798
Am J Kidney Dis. 2012;59(6):795-802
GFR in Acute Myocardial Infarction
Table 4. Hospital Treatment at Admission
Medical therapy
Thrombolysis
PCI
CABG
Aspirin
␤-Blocker
ACEi
ARB
Statin
Diuretics
Vasopressor
Group 1
eGFR >90
(n ⴝ 3,491)
Group 2
eGFR 60-89
(n ⴝ 5,791)
Group 3
eGFR 30-59
(n ⴝ 2,609)
Group 4
eGFR 15-29
(n ⴝ 439)
Group 5
eGFR <15
(n ⴝ 306)
P
398 (11.4)
44 (1.3)
2,979 (85.3)
70 (2.0)
3,453 (98.9)
2,719 (77.9)
2,639 (75.6)
429 (12.3)
2,726 (78.1)
684 (19.6)
461 (13.2)
689 (11.9)
63 (1.1)
4,876 (84.2)
162 (2.8)
5,722 (98.8)
4,198 (72.5)
4,129 (71.3)
776 (13.4)
4,297 (74.2)
1,633 (28.2)
984 (17.0)
524 (20.1)
34 (1.3)
1,974 (75.7)
76 (2.9)
2,539 (97.3)
1,709 (65.5)
1,680 (64.4)
454 (17.4)
1,730 (66.3)
1,255 (48.1)
376 (14.5)
144 (32.8)
4 (0.9)
257 (58.5)
34 (7.8)
422 (96.1)
262 (59.7)
244 (55.6)
82 (18.7)
268 (61.0)
287 (65.4)
143 (32.6)
97 (31.8)
2 (0.7)
206 (67.3)
1 (0.2)
296 (96.7)
211 (69.0)
164 (53.6)
86 (28.1)
181 (59.2)
148 (48.4)
97 (31.7)
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
⬍0.001
Note: Values given as number (percentage). eGFR given in mL/min/1.73 m2.
Abbreviations: ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CABG, coronary artery bypass
graft; eGFR, estimated glomerular filtration rate; PCI, percutaneous coronary intervention.
increasingly adjusted models. Although attenuated, a
monotonic increase in risk of these outcomes by lower
eGFR was found.
Table 8 lists associations of CKD with PCI and
medication use. Use of PCI and use of a higher
number of treatments from ␤-blocker, ACE inhibitor
or ARB, and statin therapy were associated with
decreased risk of short- and long-term MACEs.
DISCUSSION
Cardiovascular disease in patients with CKD is
common and has major implications in terms of both
human suffering and health care cost. The present
study evaluated prognostic factors and clinical outcomes of patients experiencing AMI by eGFR level.
Interestingly, STEMI was more common in patients
with higher eGFR category, whereas the prevalence of
NSTEMI was higher in patients with lower eGFR
category, as previously shown by others.16 These
findings may suggest that loss of kidney function is an
important correlate of cardiovascular risk factor profile:
nontraditional risk factors become increasingly prevalent at lower kidney function, which may contribute to
the pathogenesis of NSTEMI. Vascular calcification is
common in patients with CKD and is one of the
predictors of cardiovascular death.17
Lower eGFR category was associated significantly
with dyspnea, higher Killip class, higher clinical manifestations on admission, and higher NT-pro-BNP level.
Moreover, LVEF was decreased and Killip class was
increased with more decreased kidney function. In
contrast, LDL cholesterol level was significantly lower
in patients with decreasing GFR. The decrease in
GFR with age and the lower prevalence of hyperlipidemia may indicate increased malnutrition and inflammation in patients with severely decreased kidney
function.18 LDL cholesterol levels were lowest in
groups 4 and 5, which is remarkable in light of some
Figure 1. Rates of (A) percutaneous coronary intervention (PCI) and (B) ␤-blocker, angiotensin-converting enzyme inhibitor (ACEi)
or angiotensin receptor blocker (ARB), and statin use.
Am J Kidney Dis. 2012;59(6):795-802
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Bae et al
Table 5. Outcomes According to eGFR
Outcomes
CCU stay (d)
In-hospital death
Out-of-hospital outcome
1-mo MACEs
Cardiac death
Noncardiac death
MI
TLR
12-mo MACEs
Cardiac death
MI
TLR
Group 1
eGFR >90
(n ⴝ 3,491)
Group 2
eGFR 60-89
(n ⴝ 5,791)
Group 3
eGFR 30-59
(n ⴝ 2,609)
Group 4
eGFR 15-29
(n ⴝ 439)
Group 5
eGFR <15
(n ⴝ 306)
P
3.3 ⫾ 3.4
42 (1.2)
3.5 ⫾ 3.3
174 (3.0)
4.7 ⫾ 5.6
305 (11.7)
6.6 ⫾ 8.5
88 (20.0)
6.7 ⫾ 9.9
56 (18.3)
⬍0.001
⬍0.001
126 (3.6)
59 (1.7)
7 (0.2)
17 (0.5)
42 (1.2)
374 (11.5)
84 (2.4)
35 (1.0)
255 (7.3)
353 (6.1)
226 (3.9)
29 (0.5)
23 (0.4)
69 (1.2)
724 (12.5)
295 (5.1)
41 (0.7)
388 (6.7)
467 (17.9)
376 (14.4)
39 (1.5)
21 (0.8)
31 (1.2)
678 (26.0)
493 (18.9)
31 (1.2)
154 (5.9)
137 (31.2)
117 (26.7)
12 (2.7)
5 (1.0)
3 (0.8)
191 (43.4)
154 (35.0)
12 (2.7)
25 (5.7)
80 (26.2)
62 (20.2)
11 (3.7)
3 (1.1)
3 (1.1)
130 (42.5)
107 (35.1)
7 (2.2)
16 (5.2)
⬍0.001
⬍0.001
⬍0.001
0.1
0.2
⬍0.001
⬍0.001
0.001
0.001
Note: Categorical variables given as number (percentage); continuous variables, as mean ⫾ standard deviation. eGFR given in
mL/min/1.73 m2.
Abbreviations: CCU, coronary care unit; eGFR, estimated glomerular filtration rate; MACE, major adverse cardiac event; MI,
myocardial infarction; TLR, target-lesion revascularization.
previous studies in which lipid-lowering treatments
did not reduce the risk of cardiovascular mortality in
patients with advanced CKD.19,20 We also found that
hs-CRP level was increased, whereas body mass index was decreased with lower eGFR. These findings
suggest that individuals with advanced kidney dysfunction become more and more malnourished, as seen by
low levels of albumin, prealbumin, and transferrin,
which has been suggested to be a means by which
inflammation is activated.18,21 Kidney failure leads to
alterations in plasma components and endothelial structure and function that favor vascular injury, which
potentially could help instigate the inflammatory response.22-24 The ongoing loss of kidney function in
patients with CKD may result in dyslipidemia or
accumulation of uremic toxins, which can trigger
oxidative stress and inflammation and, as a result,
have a role in endothelial dysfunction and worsening
atherosclerosis.22 Our analysis confirms that by coronary angiography, there were more involved vessels
in patients with more severely decreased kidney function, and significant independent predictors of shortand long-term MACEs.
There is convincing evidence, obtained mainly
through randomized controlled trials, of the benefits of aspirin, ACE inhibitors, ␤-blockers, and
statins in blocking recurrent events and improving
survival after acute coronary syndrome.25 However, major cardiovascular disease trials frequently
do not include patients with kidney disease, thus
not providing data for the effect of interventions
and treatment on patients with kidney disease.10,11,13
We found that use of ␤-blockers, ACE inhibitors,
and statins was associated with decreased risk of
Table 6. Multivariate Analysis of 1-Month MACEs After AMI
Outcomes
Group 2: eGFR 60-89
(n ⴝ 5,791)
Group 3: eGFR 30-59
(n ⴝ 2,609)
Group 4: eGFR 15-29
(n ⴝ 439)
Group 5: eGFR <15
(n ⴝ 306)
Unadjusted
(1) Adjusted for age
(2) Model 1 ⫹ Killip class ⬎I
(3) Model 2 ⫹ DM, HTN
(4) Model 3 ⫹ hs-CRP
(5) Model 4 ⫹ PCI
(6) Model 5 ⫹ medicationb
1.75 (1.40-2.19)a
1.30 (1.03-1.65)a
1.18 (0.93-1.49)
1.20 (0.95-1.52)
1.28 (0.96-1.71)
1.30 (0.97-1.73)
1.16 (0.87-1.56)
5.76 (4.65-7.15)a
3.40 (2.67-4.33)a
2.62 (2.05-3.45)a
2.73 (2.15-3.47)a
2.84 (2.11-3.83)a
2.81 (2.09-3.78)a
2.30 (1.70-3.11)a
10.9 (8.44-14.3)a
6.09 (4.56-8.12)a
4.04 (3.00-5.43)a
4.34 (3.24-5.79)a
4.22 (2.93-6.09)a
3.76 (2.61-5.43)a
3.10 (2.14-4.14)a
8.85 (6.53-11.9)a
6.38 (4.65-8.74)a
4.17 (3.01-5.77)a
4.16 (3.02-5.74)a
4.45 (2.99-6.60)a
3.90 (2.61-5.82)a
3.64 (2.44-5.43)a
Note: Analysis used sequential stratified adjustment with confounders. Values shown are hazard ratio (95% confidence interval).
Group 1 (eGFR ⬎90 mL/min/1.73 m2; n ⫽ 3,491) is the reference group.
Abbreviations: AMI, acute myocardial infarction; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; hs-CRP,
high-sensitivity C-reactive protein; HTN, hypertension; MACE, major adverse cardiac event; PCI, percutaneous coronary intervention.
a
P ⬍ 0.05.
b
␤-Blockers, diuretics, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and statins.
800
Am J Kidney Dis. 2012;59(6):795-802
GFR in Acute Myocardial Infarction
Table 7. Multivariate Analysis of 1-Year MACEs After AMI
Outcomes
Group 2: eGFR 60-89
(n ⴝ 5,791)
Group 3: eGFR 30-59
(n ⴝ 2,609)
Group 4: eGFR 15-29
(n ⴝ 439)
Group 5: eGFR <15
(n ⴝ 306)
Unadjusted
(1) Adjusted for age
(2) Model 1 ⫹ Killip class ⬎I
(3) Model 2 ⫹ DM, HTN
(4) Model 3 ⫹ hs-CRP
(5) Model 4 ⫹ PCI
(6) Model 5 ⫹ medicationsb
1.23 (1.07-1.40)a
1.01 (0.88-1.17)
0.96 (0.84-1.11)
0.96 (0.83-1.11)
0.96 (0.81-1.13)
0.97 (0.82-1.14)
0.93 (0.79-1.09)
2.98 (2.60-3.42)a
2.12 (1.82-2.47)a
1.82 (1.56-2.12)a
1.77 (1.51-2.07)a
1.73 (1.45-2.07)a
1.73 (1.44-2.07)a
1.58 (1.32-1.90)a
5.29 (4.35-6.43)a
3.67 (2.98-4.51)a
2.82 (2.28-3.49)a
2.67 (2.15-3.31)a
2.43 (1.88-3.15)a
2.26 (1.74-2.93)a
2.12 (1.63-2.75)a
5.13 (4.12-6.40)a
4.11 (3.28-5.15)a
3.20 (2.54-4.03)a
3.04 (2.40-3.83)a
2.90 (2.20-3.83)a
2.69 (2.04-3.56)a
2.50 (1.89-3.29)a
Note: Analysis used sequential stratified adjustment with confounders. Values shown are hazard ratio (95% confidence interval).
Group 1 (eGFR ⬎90 mL/min/1.73 m2; n ⫽ 3,491) is the reference group.
Abbreviations: AMI, acute myocardial infarction; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; hs-CRP,
high-sensitivity C-reactive protein; HTN, hypertension; MACE, major adverse cardiac event; PCI, percutaneous coronary intervention.
a
P ⬍ 0.05.
b
␤-Blockers, diuretics, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and statins.
short- and long-term MACEs during follow up,
which indicates the potential effectiveness of
␤-blocker, ACE-inhibitor or ARB, and statin treatments for secondary prevention after AMI and of
PCI to decrease MACEs in patients with severely
decreased kidney function. In this context, the
relationship between kidney disease and cardiovascular mortality has been shown to apply to individuals with advanced renal functional impairment.
The risk of MACEs and cardiac death at both 1
month and 1 year increased with lower eGFR. These
findings are consistent with previous studies,26,27 indicating that low eGFR is associated independently
with greater risk of cardiovascular mortality and complications. Patients who have lower GFRs have more
severe cardiovascular disease on presentation of MI,
underutilization of cardioprotective medications (␤blockers, ACE inhibitors, ARBs, and statins), and less
intensive application of PCI or CABG, which in part
contributes to poor outcomes in patients with CKD.
Interestingly, the present study showed the stark inTable 8. Composite Effect of Medication and PCI on 1-Month
and 1-Year MACE After AMI
Outcomes
1-mo MACE
1-y MACE
No medicationa
1 medicationa
2 medicationsa
3 medicationsa
No PCI
PCI
1.00 (reference)
0.26 (0.21-0.31)b
0.14 (0.12-0.17)b
0.11 (0.94-0.13)b
1.00 (reference)
0.39 (0.35-0.45)b
1.00 (reference)
0.31 (0.26-0.36)b
0.26 (0.20-0.26)b
0.20 (0.18-0.23)b
1.00 (reference)
0.54 (0.48-0.60)b
Note: Values shown are hazard ratio (95% confidence interval).
Abbreviations: AMI, acute myocardial infarction; MACE, major
adverse cardiac event; PCI, percutaneous coronary intervention.
a
P ⬍ 0.05.
b
␤-Blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and statins.
Am J Kidney Dis. 2012;59(6):795-802
crease in risk of cardiovascular death at 1 year without
a concomitant increase in risk of MI. This finding is
not consistent with the previous study, which showed
that cardiovascular death was increased with reinfarction risk in patients with lower eGFRs.26 The discrepancy is unclear, although it might be due to racial
differences, different treatment modalities, and underlying diseases.
The Framingham Heart Study provided early evidence of the association between mildly decreased
kidney function and death and adverse cardiovascular
events in the general population.28 In a large study
published in 2004, Go et al2 convincingly showed, in
a diverse population of adults, an independent and
graded inverse correlation between lower kidney function and greater event rates of cardiovascular morbidity and mortality. However, this study showed that
groups 1 and 2, normal and mildly decreased kidney
function, did not show a difference in short- and
long-term MACEs. In particular, severely decreased
kidney function increased the risk of in-hospital death
and coronary care unit stay. Thus, patients with more
advanced CKD needed aggressive treatment and careful monitoring to experience better clinical outcomes.
The present study has several limitations. First,
KAMIR is a multicenter retrospective registry study
and not a randomized controlled study. Therefore, the
present analysis could not account for unmeasured
confounders, including time from onset of symptoms
to presentation or time from presentation to reperfusion in STEMI cases. The treatment bias with evidence-based therapy may be a proxy for better care or
more attentive clinicians; thus, the association of a
favorable outcome with a particular medication may
be a reflection in part due to such bias. The study also
is limited in that serum creatinine was measured at
admission and may have included individuals with
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Bae et al
acute kidney injury whose eGFR was not estimated at
equilibrium.
In conclusion, lower eGFR was found to be an
important predictor of short- and long-term MACEs.
Age, Killip class higher than I, DM, hypertension, and
hs-CRP level were risk predictors of short- and longterm MACEs. Receipt of PCI and use of ␤-blockers,
ACE inhibitors or ARBs, and statins was associated
with decreased risk of MACEs.
ACKNOWLEDGEMENTS
Support: This research was supported by Basic Science Research Program through the National Research Foundation of
Korea funded by the Ministry of Education, Science and Technology (2010-0008732), and by the Korea Science and Engineering
Foundation through the Medical Research Center for Gene Regulation (grant 2011-0030732) at Chonnam National University.
Financial Disclosure: The authors declare that they have no
other relevant financial interests.
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