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2. medicine
3. pediatrics

4/28/2010
Scoring Systems for severity of illness:
Why and what
Mohan Mysore, MD, FCCM
Children’s Hospital &
Medical Center
Omaha, Nebraska USA
Why do we need scoring systems ?
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Large randomized control trials are gold
standard
d d
Expensive
Might not always be feasible
Alternative is observational methods/studies
Need to consider patient characteristics before
any inferences can be drawn
Need to “adjust for case mix”
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Need a common language
Outcome: Definition
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A measurable change in health or other relevant
status
Outcome should be tied to something of
interest
Outcomes could be:
Short-term
Short Intermediate
 Long
Long--term
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Outcomes determined/impacted by:
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Input
I
t (equipment,
( i
t staff)
t ff)
Processes of care (type,
skill and timing of care)
Case mix of patients
Type of facility
Underlying
y g disease
processes (eg Bone
marrow transplant)
Another way to classify:
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E i
Environmental
t l factors
f t
Unit characteristics
Provider characteristics
Organizational
characteristics
Inter--organizational
Inter
g
charactersitics
Outcomes - categories
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Perceptual outcomes:
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P ti t-family
PatientPatient
f il satisfaction
ti f ti
Clinical outcomes:
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Change in clinical
parameters
Morbidity
Mortality
Functional outcomes
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Financial outcomes:
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Effi i
Efficiency
Length of stay
Cost
Resource utilization
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A word about mortality…..
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Mortality in a particular disease might be low (with
py), but morbidityy might
g be significant
g
intensive therapy),
and have longlong-term impact
Outcome measures other than mortality might need to
be looked at:
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Ventilator free days
Organ failure
Days of vasopressor use
Disability metrics: Pediatric Overall Performance Category
(POPC) and Pediatric Cerebral Performance Category
(PCPC) scores
Reference:
Fiser DH: Assessing the outcome of pediatric intensive care. J Peds 1992;121;681992;121;68-74
Scoring systems
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Aimed at quantifying case mix and using
resultant
l
score to estimate
i
outcome
Provide a standard by which patient severity of
illness can be gauged
Permits stratification of patients to determine
outcomes – such as riskrisk-adjusted mortality
Particularly beneficial if large and diverse patient
populations are being studied
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Types of scoring systems
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Specific or Generic
Anatomical: Extent of injury; usually fixed
Physiological: Impact of injury on function; score may change
based on effect of injury
Physiology--based systems:
Physiology
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Neonatal: APGAR, CRIB
Pediatric: PRISM, PIM
Cardiac: CCOS
Adult: APACHE, TISS, MPM etc.
Each system is designed for a specific patient population.
No “one size fits all”
Glasgow Coma Scale
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Motor
Obeys
y commands
Localizes to pain
Flexes to pain
Abnormal flexion
Abnormal extension
No response
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6
5
4
3
2
1
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Score Ranges from 33--15
Has been studied extensively
and validated – corresponds
to outcome.
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Verbal
Oriented
Confused
Words only
Sounds only
No response
5
4
3
2
1
Eye Opening
Spontaneous
To speech
To pain
No response
4
3
2
1
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TISS
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Therapeutic Intervention Scoring System (TISS): 1974
Q
Quantify
if severity
i off illness
ill
among ICU patients
i
b
based
d
on type and amount of treatment recd.
Sicker the patient, greater the complexity of treatment
Provides a proxy measure of the severity of illness for a
patient
Simplified version based on 28 therapeutic activities
(TISS 28)
APACHE score
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Acute Physiology and Chronic Health Evaluation
Mid--1970s
Mid
1970 – Dr.William
D Willi K
Knaus
34 Physiological variables which impact outcome
APACHE II (1985): Narrowed down to 12 parameters.
Points assigned to each variable according to its most
abnormal value in first 24 hours. Score range: 00-71
Updated to APACHE III (1991)
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0
Pediatric Trauma Score
Variables (help)
Value
Points
Weight (kg)
Systolic BP (mmHg)
Mental status
Airway
Skeletal
Open wounds
Score . ..............
References
TEPAS JJ et coll. The Pediatric Trauma Score as a predictor of injury severity in the injured child. J. Pediat. Surg. 1987;22:14-8.
RAMENOFSKY M et coll. The Predictive Validity of the Pediatric Trauma Score. J. Trauma 1988;28:1038-42.
PRISM – Pediatric Risk of Mortality
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Data collected during first 24 hours
in the PICU
Age in months
Post--operative state
Post
Revised to PRISM III (proprietary)
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Reference:
Pollack MM et al: Pediatric Risk of
Mortality score: Crit Care Med
1988:16:1110--6
1988:16:1110
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Systolic BP
Diastolic BP
HR
RR
PaO2/FiO2
PaCO2
PT/PTT
Total bilirubin
Calcium
Potassium
Glucose
HCO3
Pupillary reaction
GCS
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PRISM III and PICUEs
PRISM III and PICUEs
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PIM – Pediatric Index of Mortality
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A new pediatric-specific score introduced in 1997
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PIM2 (Pediatric Index of Mortality)
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• Systolic Blood Pressure *
• Pa02 *
• Fi02 *
• Base Excess *
• Weight (kg) *
• Height (cm) *
• Pupillary Reactions *
• Mechanical Ventilation *
• Elective Admission to ICU *
• Recovery from Surgery or a Procedure is the Main Reason for ICU Admission *
• Admitted
Ad itt d Following
F ll i Cardiac
C di Bypass
B
*
• High Risk Diagnosis *
• Low Risk Diagnosis *
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Shann F, Pearson G, Slater A, et al: Paediatric index of mortality (PIM): A mortality
prediction model for children in intensive care.Intensive Care Med 1997; 23:201–207
Variables (help)
Values (1 if Yes, 0 otherwise)
Beta
Elective admission
Recovery post procedure
Cardiac bypass
High risk diagnosis
Low risk diagnosis
No response of pupils to bright light
(> 3 mm and both fixed)
Mechanical ventilation
(at any time during first hour in ICU)
Systolic Blood Pressure (mmHg)
0.01395
Base Excess (mmHg)
(arterial or capillary blood)
0.1040
FiO2*100/ PaO2 (mmHg)
0.2888
..
Predicted Death Rate : .
... .. .
Logit = (-4.8841) + (values * Beta) + (0.01395 * (absolute(SBP-120))) + (0.1040 * (absolute base excess)) + (0.2888 *
(100*FiO2/PaO2))
Predicted death rate = eLogit/ (1+eLogit)
Reference
A. Slater et al. PIM 2 : a revised version of the Paediatric Index of Mortality. Intensive Care Med 2003 ;29:278-85.
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PRISM and PIM
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Studies have compared the two systems in the same
ggroups
p of patients
p
303 pts. over 9 month period
PRISM III at 12h and 24h and PIM
Expected mortality: 6.96% by PRISM III at 12h
and 6.95% at 24h versus 7.5% based on PIM
Showed them to be adequate indicators of mortality
pr b bilit in
probability
i a PICU population
p p l ti
Reference: Gemke RJ and vanVught J in Intensive Care Medicine 2002 Feb
28(2): 204204-7
PRISM and PIM
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2-phase prospective observational study conducted in 10 PICUs
in Australia and NZ
Phase 1: 1997
1997--99: PIM, PRISM and PRISM III
Phase 2: 2000
2000--01: PIM 2
26,966 patients (<16 yrs)
Compared number of observed to predicted deaths in different
diagnostic and risk groups
Results: PIM 2 was most accurate with no difference between
observed and predicted mortality. P
PIM
M ((116%),
6%), PRISM
PR SM III
(130%) and PRISM (189%) all overestimated mortality
Reference: Slater A, Shann F and ANZICS Pediatric study group in Pediatric
Crit Care Med 2004 Sep; 5(5): 447447-54
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SAPS and MPM
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Simplified Acute Physiology Score
Updated to SAPS II in 1993
Mortality Prediction Model (MPM)
Organ Failure scoring systems
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Take into account severity of dysfunction in
each organ system and its effect on prognosis of
dysfunction in several organ systems
Need constant updating since therapies and
outcomes evolve and improve
PLOD: Pediatric Logistic Organ Dysfunction
score. Looks
L k at 6 organ systems andd gives
i each
ha
score based on physiologic/lab data.
Refrence: Leteurtre S. et al. Validation of the paediatric logistic organ dysfunction (PELOD) score:
prospective, observational, multicentre study. Lancet. 2003 Jul 19;362(9379):19219;362(9379):192-7
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P-MODS
Pediatric Multiple Organ Dysfunction Score
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Looks at 5 parameters:
PaO
P O2/FiO2
 Lactic Acid
 Bilirubin
 BUN
 Fibrinogen
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Reference: Graciano AL et al: The Pediatric Multiple Organ Dysfunction
Score (P(P-MODS): development and validation of an objective scale to
measure the severity of multiple organ dysfunction in critically ill children.
Crit Care Med 2005 Jul;33(7):1484Jul;33(7):1484-91
Composite Clinical Outcome Score
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New score developed by Children’s Hospital, Boston
Predictive validity of CCOS to predict LOS and
Psychomotor Development Index at 1year after cardiac
surgery
711 infants from 19881988-2004
Patients placed into 4 groups:
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D-TGA ggroup
p
Conotruncal group
VSD group
High--risk diagnoses (HLHS)
High
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Composite Clinical Outcome Score
(CCOS)
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Clinical Variable
Score 0
1
2
Time
Ti untilil first
fi negative
i fluid
fl id balance
b l
<3 44-5 >6
Time until sternal closure
<2 33--4 >5
Time until first extubation
<4 55-8 >9
Death/Use of ECMO 7
 Results:
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I
Increase
in
i CCOS by
b 1 increased
i
d LOS andd likelihood
lik lih d off still
ill
being in the hospital at any point in time by 62%
CCOS significantly predictive of 1 year PDI
Yerlagadda V et al: Pediatr Crit Care Med.2006 Vol 7 No 6
Selecting a scoring system
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Main criteria:
Proposed use
Accuracy
Validity of score
 Reliability of score
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G d
Goodness
off fit
fi
Discrimination of scoring system
 Calibration of scoring system
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Roles of scoring systems
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Evaluative research
Outcome prediction*
Clinical management of individual patients*
Comparative studies of efficiency, outcomes
In concluding…
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Scoring systems are getting more sophisticated while at the same
time, becoming simpler to apply
Newer systems are more accurate in predicting outcome
The calculation of a risk assigned to a measured score is an
epidemiologic tool.
Still only provide probabilities and do not accurately predict
whether an individual patient will survive
Should not be used alone to determine outcome of critical care
Scoring systems will need to be dynamic and require
updating/recalibration
Inter--observer variability remains an issue and is an important
Inter
factor which needs to be taken into consideration (Ref: Keulen J,
Polderman K: Reliability of PRISM and PIM scores in paediatric intensive care)
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Shukran
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