Predicting 30-Day Pneumonia Readmissions Using Electronic Health Record Data
Readmissions after hospitalization for pneumonia are common, but the few risk-prediction models have poor to modest predictive ability. Data routinely collected in the electronic health record (EHR) may improve prediction.OBJECTIVE:
To develop pneumonia-specific readmission risk-prediction models using EHR data from the first day and from the entire hospital stay (“full stay”).DESIGN:
Observational cohort study using stepwise-backward selection and cross-validation.SUBJECTS:
Consecutive pneumonia hospitalizations from 6 diverse hospitals in north Texas from 2009–2010.MEASURES:
All-cause nonelective 30-day readmissions, ascertained from 75 regional hospitals.RESULTS:
Of 1463 patients, 13.6% were readmitted. The first-day pneumonia-specific model included sociodemographic factors, prior hospitalizations, thrombocytosis, and a modified pneumonia severity index; the full-stay model included disposition status, vital sign instabilities on discharge, and an updated pneumonia severity index calculated using values from the day of discharge as additional predictors. The full-stay pneumonia-specific model outperformed the first-day model (C statistic 0.731 vs 0.695; P = 0.02; net reclassification index = 0.08). Compared to a validated multi-condition readmission model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores, the full-stay pneumonia-specific model had better discrimination (C statistic range 0.604–0.681; P < 0.01 for all comparisons), predicted a broader range of risk, and better reclassified individuals by their true risk (net reclassification index range, 0.09–0.18).CONCLUSIONS:
EHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. This approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores.