The SUPPORT prognostic model. Objective estimates of survival for seriously ill hospitalized adults. Study to understand prognoses and preferences for outcomes and risks of treatments.
OBJECTIVE
To develop and validate a prognostic model that estimates survival over a 180-day period for seriously ill hospitalized adults (phase I of SUPPORT [Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments]) and to compare this model's predictions with those of an existing prognostic system and with physicians' independent estimates (SUPPORT phase II).


METHODS
Prospective cohort study.


METHODS
5 tertiary care academic centers in the United States.


METHODS
4301 hospitalized adults were selected for phase I according to diagnosis and severity of illness; 4028 patients were evaluated from phase II.


METHODS
A survival model was developed using the following predictor variables: diagnosis, age, number of days in the hospital before study entry, presence of cancer, neurologic function, and 11 physiologic measures recorded on day 3 after study entry. Physicians were interviewed on day 3. Patients were followed for survival for 180 days after study entry.


RESULTS
The area under the receiver-operating characteristics (ROC) curve for prediction of surviving 180 days was 0.79 in phase I, 0.78 in the phase II independent validation, and 0.78 when the acute physiology score from the APACHE (Acute Physiology, Age, Chronic Health Evaluation) III prognostic scoring system was substituted for the SUPPORT physiology score. For phase II patients, the SUPPORT model had equal discrimination and slightly improved calibration compared with physician's estimates. Combining the SUPPORT model with physician's estimates improved both predictive accuracy (ROC curve area = 0.82) and the ability to identify patients with high probabilities of survival or death.


CONCLUSIONS
A limited amount of readily available clinical information can provide a foundation for long-term survival estimates that are as accurate as physicians' estimates. The best survival estimates combine an objective prognosis with a physician's clinical estimate.
Knaus
W A
WA
George Washington University Medical Center, Washington, DC.
Harrell
F E
FE
Lynn
J
J
Goldman
L
L
Phillips
R S
RS
Connors
A F
AF
Dawson
N V
NV
Fulkerson
W J
WJ
Califf
R M
RM
Desbiens
N
N
Layde
P
P
Oye
R K
RK
Bellamy
P E
PE
Hakim
R B
RB
Wagner
D P
DP
D018806:APACHE; D000046:Academic Medical Centers / Q000706:statistics & numerical data*; D000368:Aged; D016638:Critical Illness / Q000401:mortality*; D003657:Decision Making; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care; D011379:Prognosis; D011446:Prospective Studies; D012372:ROC Curve; D012720:Severity of Illness Index; D016019:Survival Analysis; D014481:United States