Predicting 30-Day Pneumonia Readmissions Using Electronic Health Record Data

J Hosp Med. 2017 Apr;12(4):209-216. doi: 10.12788/jhm.2711.


Background: 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. Journal of Hospital Medicine 2017;12:209-216.

Publication types

  • Observational Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Electronic Health Records / statistics & numerical data*
  • Hospitalization*
  • Humans
  • Models, Statistical*
  • Patient Readmission / statistics & numerical data*
  • Pneumonia*
  • Retrospective Studies
  • Severity of Illness Index
  • Texas