USING ELECTRONIC MEDICAL RECORDS TO PREDICT MORTALITY IN PRIMARY-CAREPATIENTS WITH HEART-DISEASE - PROGNOSTIC POWER AND PATHOPHYSIOLOGIC IMPLICATIONS

OBJECTIVE: To identify high-risk patients with heart disease by using data stored in an electronic medical record system to predict six-year mortality. DESIGN: Retrospective cohort study. SETTING: Academic prim ary care general internal medicine practice affiliated with an urban t eaching hospital with a state-of-the-art electronic medical record sys tem. PATIENTS: Of 2,434 patients with evidence of ischemic heart disea se or heart failure or both who visited an urban primary care practice in 1986, half were used to derive a proportional hazards model, and h alf were used to validate it.MEASUREMENTS: Mortality from any cause wi thin six years of inception date. Model discrimination was assessed wi th the C statistic, and goodness-of-fit was measured with a calibratio n curve and Hosmer-Lemeshow statistic. MAIN RESULTS: Of these patients 82% had evidence of ischemic heart disease, 53% heart failure, and 35 % both conditions. Mean survival among the 653 (27%) who died was 2.8 years; mean follow-up among survivors was 5.0 years. Those with both h eart conditions had the highest mortality rate (45% at 6 years), follo wed by isolated heart failure (39%) and ischemic heart disease (18%). Of 300 potential predictive characteristics, 100 passed a univariate s creen and were submitted to multivariable proportional hazards regress ion. Twelve variables contributed independent predictive information: age, weight, more than one previous hospitalization for heart failure, and nine conditions indicated on diagnostic tests and problem lists. No drug treatment variables were independent predictors. The model C s tatistic was 0.76 in the derivation sample of patients and 0.74 in a r andomly selected validation sample, and it was well calibrated. Patien ts in the lowest and highest quartiles of risk differed more than five -fold in their average risk. CONCLUSIONS: Routine clinical data stored in patients' electronic medical records are capable of predicting mor tality among patients with heart disease. This could allow increasingl y scarce health care resources to be focused on those at highest morta lity risk.