Illness Severity Scoring Systems
21/11/10
A-Z
OH page 17 ->
SP Notes
- used to:
(1)
(2)
(3)
(4)
stratify groups of critically ill patients by severity
compare groups of patients in research trials
compare ICU’s
predict mortality and prognosis for individuals and groups
- most measure physiological variables
- some measure interventions
- derived from logistic regression from large demographic data sets
DESIRABLE FEATURES
-
scores calculated on basis or easily/routinely recordable variables
well calibrated and validated
high level of discrimination
can indicate prognosis and risk (expected mortality rates)
can indicated need and speed for treatment
applicable to all patients in ICU
applicable to different patient cohorts and other health care systems/countries
considers co-morbidity
considers organisational aspects
provides a common language for discussion
method to evaluate critical care practice and process
allows ability to compare groups in clinical trials
SCORING SYSTEMS
- GCS = Glasgow Coma Score in Coma
- TISS = Therapeutic Interventions Scoring System
- APACHE = Acute Physiology, Age and Chronic Health Evaluation Systems
- SAPS = Simplified Acute Physiology Score
- MPM = Mortality Prediction Models
- POSSUM = Physiological and Operative Severity Score for the enumeration of Mortality and
Morbidity
- SOFA = Sequential Organ Failure Assessment
Glasgow Coma Score
-
EVM scores
provides systematic way of assessing the head injured
directs investigation and therapy
affected by alcohol and sedation
used in APACHE II
useful in prognostication
Jeremy Fernando (2010)
Therapeutic Intervention Scoring System (TISS)
-
developed to: estimate severity of illness and quantify burden of work for ICU staff
daily collection of 76 items (interventions and treatments)
good indicator of nursing and medical work
poor measure of severity of illness
less widely used
can be used as an allocation of resources (accountancy) tool
Acute Physiology, Age, and Chronic Health Evaluation Systems (I – IV)
- APACHE
- I – 1987
- II – 1985 (most widely used in the world, 12 variables, score of 0-71, worse values in first
24 hours in ICU, limited by derivation from an historical data set)
- III – 1991 (score of 0-299, 16 variables, improved prognostication, improved discrimination
and calibration)
- IV – 2006 (large data set, more variables included, more accurate, only used in US)
Simplified Acute Physiology Score (SAPS 1-3)
- SAPS 1 (French ICU’s, solely looked at physiology)
- SAPS 2 (1993, European and North American, added chronic health conditions, greater
calibration and discrimination)
- SAPS 3 (2005, around the world, 20 variables – prior to admission, at admission, acute
physiological derangement)
Mortality Prediction Models (MPM I and II)
I
- variables at admission and during first 24 hours.
- computes a hospital risk of death from the absence or presence of factors in a logistic
regression equation.
II
- based on the same data set as SAPS II
- outcome prediction @ 24, 48 and 72 hrs
POSSUM
-
1991
12 acute physiological parameters (surgery and severity of surgery)
useful tool for surgeons who needed a risk adjustment tool
meant to predict death but was found to over predict.
P-POSSUM – Porthsmouth: predicts hospital mortality more accurately
V-POSSUM – vascular surgery
Cr-POSSUM – colon cancer resection
Sequential Organ Failure Assessment (SOFA)
Jeremy Fernando (2010)
-
6 organs and grades organ function
simple and take into account supportive treatments
good way of tracking patient morbidity
often used to analyse secondary endpoints in research trials
APPLICATION OF SCORING SYSTEMS
Standardised mortality ratio (SMR)
- is the comparison of predicted with observed mortality rates.
- need to considered in the context of case mix and calibration
Description of Case Mix
-
stratification of patients for clinical trials
comparison of predicted and observed outcomes
allows prediction of resources to manage severity of illness at presentation
predict length of stay
PROBLEMS OF USING SEVERITY OF ILLNESS SCORES TO COMPARE OUTCOMES BETWEEN
ICU’S
- often done
- problems revolve around: quality of data, appropriateness of model and disease categories.
- specific problems:
(1) variation in recording of data (timing, criteria) -> ideally should be audited and reviewed
regularly by trained personnel.
(2) differences in patient populations not accounting for by the diagnostic groups.
(3) multiple organ involvement leads to difficult categorisation of disease.
(4) corrupted data, missed data – lack of trained data collectors.
(5) bias or fraud due competition for funding.
(6) historical variations, comparing data across time.
(7) outcomes may be dependent on not only ICU management, but the whole hospital
package, surgeon, radiology…
APACHE vs SOFA
APACHE = Acute Physiology, Age and Chronic Health Evaluation (I-IV)
SOFA = Sequential Organ Failure Assessment
Jeremy Fernando (2010)
APACHE
SOFA
Basis
- 3 factors:
(1) pre-existing disease
(2) patient reserve
(3) severity of acute illness
- degree of organ dysfunction during acute
illness (initially used in sepsis -> later
validated for other MOD syndromes)
Score
-
- defined score 1-4 for each organ system
- resp, cvs, cns, renal, coag, liver
Scoring Duration
- most abnormal measurements in
first 24 hrs of ICU stay
- daily scoring of individual and composite
scores possible during ICU stay
Population Outcome
Comparison
- SMR (observed and predicted)
can be used for large patient
population
- no predicted mortality algorithm
- higher SOFA score associated with worse
outcome
- treatment effects on SOFA
Individual Patient
Outcome
- not possible to predict individual
patient outcome or response to
therapy
- response of organ dysfunction to therapy
can be followed over time
physiological variables
chronic health conditions
emergency/elective admissions
post op/non-operative admissions
Jeremy Fernando (2010)