Predicting 30-day pneumonia readmissions using electronic health record data
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. © 2017 Society of Hospital Medicine
© 2017 Society of Hospital Medicine
Acknowledgments
The authors would like to acknowledge Ruben Amarasingham, MD, MBA, president and chief executive officer of Parkland Center for Clinical Innovation, and Ferdinand Velasco, MD, chief health information officer at Texas Health Resources for their assistance in assembling the 6-hospital cohort used in this study.
Disclosures
This work was supported by the Agency for Healthcare Research and Quality-funded UT Southwestern Center for Patient-Centered Outcomes Research (R24 HS022418-01); the Commonwealth Foundation (#20100323); the UT Southwestern KL2 Scholars Program supported by the National Institutes of Health (KL2 TR001103 to ANM and OKN); and the National Center for Advancing Translational Sciences at the National Institute of Health (U54 RFA-TR-12-006 to E.A.H.). The study sponsors had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. The authors have no financial conflicts of interest to disclose
