Prediction of survival for older hospitalized patients: The HELP survival model

OBJECTIVE: To develop and validate a model estimating the survival time of hospitalized persons aged 80 years and older. DESIGN: A prospective cohort study with mortality follow-up using the Natio nal Death Index. SETTING: Four teaching hospitals in the US. PARTICIPANTS: Hospitalized patients enrolled between January 1993 and Novem ber 1994 in the Hospitalized Elderly Longitudinal Project (HELP). Patients were excluded if their length of hospital stay was 48 hours or less or if a dmitted electively for planned surgery. MEASUREMENTS: A log-normal model of survival time up to 711 days was develo ped with the following variables: patient demographics, disease category, n ursing home residence, severity of physiologic imbalance, chart documentati on of weight loss, current quality of life, exercise capacity, and function al status. We assessed whether model accuracy could be improved by includin g symptoms of depression or history of recent fall, serum albumin, physicia n's subjective estimate of prognosis, and physician and patient preferences for general approach to care. RESULTS: A total of 1266 patients were enrolled over a 10-month period, (me dian age 84.9, 61% female, 68% with one or more dependency), and 505 (40%) died during an average follow-up of more than 2 years. Important prognostic factors included the Acute Physiology Score of APACHE III collected on the third hospital day, modified Glasgow coma score, major diagnosis (ICU cate gories together, congestive heart failure, cancer, orthopedic, and all othe r), age, activities of daily living, exercise capacity, chart documentation of weight loss, and global quality of life. The Somers' Dry for a model in cluding these factors was 0.48 (equivalent to a receiver-operator curve (RO C) area of 0.74, suggesting good discrimination). Bootstrap estimation indi cated good model validation (corrected Dry of 0.46, ROC of 0.73). A nomogra m based on this log-normal model is presented to facilitate calculation of median survival time and 10th and 90th percentile of survival time. A count of geriatric syndromes or comorbidities did not add explanatory pow er to the model, nor did the hospital of patient recruitment, depression, o r the patient preferences for general approach to care. The physician's per ception of the patient's preferences and the physician's subjective estimat e of the patient's prognosis improved the estimate of survival time signifi cantly. CONCLUSIONS: Accurate estimation of length of life for older hospitalized p ersons may be calculated using a limited amount of clinical information ava ilable from the medical chart plus a brief interview with the patient or su rrogate. The accuracy of this model can be improved by including measures o f the physician's perception of the patient's preferences for care and the physician's subjective estimate of prognosis.