TY - JOUR
T1 - Predicting 30-day pneumonia readmissions using electronic health record data
AU - Makam, Anil N.
AU - Nguyen, Oanh Kieu
AU - Clark, Christopher
AU - Zhang, Song
AU - Xie, Bin
AU - Weinreich, Mark
AU - Mortensen, Eric M.
AU - Halm, Ethan A.
N1 - Funding Information:
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 South-western 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.
Publisher Copyright:
© 2017 Society of Hospital Medicine.
PY - 2017/4
Y1 - 2017/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85037552485&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85037552485&partnerID=8YFLogxK
U2 - 10.12788/jhm.2711
DO - 10.12788/jhm.2711
M3 - Article
C2 - 28411288
AN - SCOPUS:85037552485
SN - 1553-5606
VL - 12
SP - 209
EP - 216
JO - Journal of hospital medicine (Online)
JF - Journal of hospital medicine (Online)
IS - 4
ER -