Using electronic medical records to predict mortality in primary care patients with heart disease

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Abstract
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 primary care general internal medicine practice affiliated with an urban teaching hospital with a state-of-the-art electronic medical record system. PATIENTS: Of 2,434 patients with evidence of ischemic heart disease or heart failure or both who visited an urban primary care practice in 1986, half were used to derive a proportional hazards model, and half were used to validate it. MEASUREMENTS: Mortality from any cause within six years of inception date. Model discrimination was assessed with the C statistic, and goodness-of-fit was measured with a calibration 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 heart conditions had the highest mortality rate (45% at 6 years), followed by isolated heart failure (39%) and ischemic heart disease (18%). Of 300 potential predictive characteristics, 100 passed a univariate screen and were submitted to multivariable proportional hazards regression. 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 statistic was 0.76 in the derivation sample of patients and 0.74 in a randomly selected validation sample, and it was well calibrated. Patients 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 mortality among patients with heart disease. This could allow increasingly scarce health care resources to be focused on those at highest mortality risk.