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Using Computer-based Medical Records to Predict Mortality Risk for Inner-city Patients with Reactive Airways Disease

Affiliations of the authors: Wishard Memorial Hospital (WMT, MDM); Regenstrief Institute for Health Care (WMT, MDM, DLG, XHZ); Indiana University School of Medicine (WMT, XHZ); Richard L. Roudebush Veterans Affairs Medical Center (WMT, MDM); Purdue School of Pharmacy (MDM), Indianapolis, IN.

Correspondence and reprints: William M. Tierney, MD, Regenstrief Institute for Health Care, Sixth Floor, RHC, 1001 West Tenth Street, Indianapolis, IN 46202. E-mail: btierney{at}vax1.iupui.edu

Abstract Objective: To use routine data from a comprehensive electronic medical record system to predict death among patients with reactive airways disease.

Design: Retrospective cohort study conducted in an academic primary care internal medicine practice. Subjects were 1,536 adults with reactive airways disease: 542 with asthma and 994 with chronic obstructive pulmonary disease (COPD).

Measurements: The dependent variable was death from any cause within 3 years following patients' first primary care appointment in 1992. Multivariable logistic regression was used to identify independent predictors of 3-year mortality, with half of the patients used to derive the predictive model and the other half used to assess its predictability.

Results: Of the 1,536 study patients, 191 (12%) died in the 3-year follow-up period. From information available on or before patients' first primary care visit in 1992, multivariable predictors of 3-year mortality were coincidental heart failure, male sex, presence of COPD, lower weight, low serum albumin concentration level, and a prior arterial PO₂ of less than 60 mmHg; use of an inhaled corticosteroid was protective. The c-statistic (ROC curve area) in the validation cohort was 0.76, indicating good discrimination, and goodness of fit was excellent by Hosmer-Lemeshow chi-square (P > 0.5). Only 24% of the patients in the validation cohort were designated at high risk (estimated 15% 3-year mortality), but this group contained more than half of the deaths within 3 years for the entire cohort.

Conclusions: Data generated during routine care and stored in a comprehensive electronic medical record can accurately predict mortality among patients with reactive airways disease. Such technology can be used by practices to control for severity of illness when assessing clinical practice and to identify high-risk patients for interventions to improve prognosis.

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