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Predicting 30‐Day Pneumonia Readmissions Using Electronic Health Record Data
Author(s) -
Makam Anil N.,
Nguyen Oanh Kieu,
Clark Christopher,
Zhang Song,
Xie Bin,
Weinreich Mark,
Mortensen Eric M.,
Halm Ethan A.
Publication year - 2017
Publication title -
journal of hospital medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.128
H-Index - 65
eISSN - 1553-5606
pISSN - 1553-5592
DOI - 10.12788/jhm.2711
Subject(s) - medicine , pneumonia , pneumonia severity index , medicaid , emergency medicine , electronic health record , interquartile range , observational study , severity of illness , intensive care medicine , community acquired pneumonia , health care , economic growth , economics
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.