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The SUPPORT Prognostic Model: Objective Estimates of Survival for Seriously Ill Hospitalized Adults

William A. Knaus, MD; Frank E. Harrell, PhD; Joanne Lynn, MD, MA; Lee Goldman, MD, MPH; Russell S. Phillips, MD; Alfred F. Connors, MD; Neal V. Dawson, MD; William J. Fulkerson, MD; Robert M. Califf, MD; Norman Desbiens, MD; Peter Layde, MD, MSc; Robert K. Oye, MD; Paul E. Bellamy, MD; Rosemarie B. Hakim, PhD; and Douglas P. Wagner, PhD
[+] Article and Author Information

From the George Washington University Medical Center, Washington, D.C.; Duke University Medical Center, Durham, North Carolina; Dartmouth Medical School, Hanover, New Hampshire; Beth Israel Hospital, Boston, Massachusetts; Metrohealth Medical Center, Cleveland, Ohio; Marshfield Medical Research Foundation and the Marshfield Clinic, Marshfield, Wisconsin; and University of California, Los Angeles, School of Medicine, Los Angeles, California. Requests for Reprints: William A. Knaus, MD, National Coordinating Center, The Robert Wood Johnson Foundation Critically Ill Hospitalized Adult Program, ICU Research Unit, 2300 K Street NW, Washington, DC 20037. Grant Support: By The Robert Wood Johnson Foundation. The opinions and findings contained in this article are those of the authors and do not necessarily represent the views of The Robert Wood Johnson Foundation or their Board of Trustees.

Copyright ©2004 by the American College of Physicians

Ann Intern Med. 1995;122(3):191-203. doi:10.7326/0003-4819-122-3-199502010-00007
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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).

Design: Prospective cohort study.

Setting: 5 tertiary care academic centers in the United States.

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

Measurements: 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 physicians' estimates. Combining the SUPPORT model with physicians' 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.


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Figure 1.
Relative contributions of major prognostic elements in the SUPPORT model as measured by the amount of chi-square accounted for by each element.

Physiology = all physiologic measures except Glasgow coma scale; cancer comorbidity = presence of cancer in addition to disease category; previous hospital stay = days in the hospital before study entry.

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Appendix Figure 1. The 6-month mortality in the 4301 phase I SUPPORT patients was 48.1%, but because the shapes of survival curves varied substantially, the nine disease groups (Appendix 1) were collapsed into four classes. ARF = acute respiratory failure, CHF = congestive heart failure, COPD = chronic obstructive pulmonary disease, MOSF = multiple organ system failure.
Relation between disease classification in the SUPPORT prognostic model and proportion of patients surviving to 6 months.
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Appendix Figure 2. The SUPPORT physiology score consists of 11 physiologic measures taken on study day 3, represented on a continuous basis. The risk score is equivalent to the log relative hazard. A risk score of 0 indicates that the value of the variable places the patient at the “standard” or baseline risk, and a positive or negative score indicates an increased or decreased risk, respectively, relative to baseline. Dotted lines indicate 95% CIs. The relative risk assigned to respiratory rate was adapted directly from the APACHE III prognostic scoring system, modified by a regression coefficient estimated in SUPPORT. = the number of patients in the subgroup; d = the number of patients in the subgroup who had died by 180 days.
SUPPORT Physiology Score in the SUPPORT prognostic model.n
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Appendix Figure 3. The two components of this factor are chronologic age and the presence of cancer as a comorbidity; the interaction between age and disease is illustrated. Disease groups were combined according to the relative effect of age into multiple organ system failure with cancer; lung and colon cancer; acute respiratory failure (ARF) or multiple organ system failure (MOSF) without cancer, and cirrhosis and coma; congestive heart failure (CHF); and chronic obstructive pulmonary disease (COPD). Mean patient age was 63 years. Because the selection criteria required patients to have severe primary disease (Appendix 1), the only comorbidity that had independent prognostic significance was cancer; metastatic solid tumors had the highest relative risk.
Long-term health evaluation in the SUPPORT prognostic model.
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Appendix Figure 4. Risk for death was associated with the number of days the patient spent in the hospital before study entry. This effect was strongest in patients hospitalized for at least 1 week before study entry.
Effect of previous hospital days on prognosis.
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Figure 2.
Prospective validation of calibration of the various prognostic models based on day 3 data for survival to 180 days after study entry for 4028 Phase II patients.Top.Middle.Bottom.

The fraction surviving is on the vertical axis and the model prediction is on the horizontal. Each bar represents the mean prediction for 100 patients in each interval; the heights of the bars indicate 95% CIs. The overall calibration or reliability of the model is expressed as the closeness of the fit of this curve to the diagonal, which is the ideal fit. SUPPORT prognostic model. Receiver-operating characteristics curve area for 180-day survival = 0.78. SUPPORT prognostic model with the APACHE III acute physiology score substituted for the SUPPORT physiology score. Receiver-operating characteristics curve area for 180-day survival = 0.78. Physicians' estimates. There are fewer bars because physicians did not use all possible probabilities in forming predictions; they tended to use multiples of 0.05. Receiver-operating characteristics curve area = 0.78. Average number of predictions for physicians = 269.

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Appendix Figure 5. Survival curve is generated for 180 days. This report includes enhancement of model's estimate with the physicians' estimate. Prognostic variables used in generating day 3 feedback report and their relative weights in the estimate for survival.
Top. Phase II Feedback report generated on study day 3 for patient admitted to SUPPORT with multiple organ system failure with malignancy.Bottom.
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Figure 3.
Comparison, in high and low ends of the prognostic range, of the predictive ability of four prognostic models.Top.nnBottom.n

SPS = SUPPORT prognostic model; APS = SUPPORT prognostic model with the APACHE III acute physiology score; MD = physician's prediction; and SPS & MD = physician-enhanced SUPPORT model. Results are based on 4028 phase II patients who were followed for 180 days and for whom physician estimates were obtained. The vertical axes represent the number of patients in each sample. Cross-hatched sections indicate the proportion of patients who died; open sections indicate survivors. Number of phase II patients with a 180-day survival probability of less than 0.15. Physicians predicted a survival less than 0.15 for more patients ( = 753), but within this risk group, had the highest survival rate and the lowest calibration of the three prognostic models. The SUPPORT physician-enhanced model had the lowest survival rate when predicting low probability of survival and the second highest number of patients ( = 668). Number of phase II patients with a survival probability of greater than 0.85. The physician-enhanced prognostic model predicted the largest sample ( = 496) and had a 91% survival rate; the physician-estimated sample had an 85% mortality rate.

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