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
Pneumonia is a leading cause of hospitalizations in the U.S., accounting for more than 1.1 million discharges annually.1 Pneumonia is frequently complicated by hospital readmission, which is costly and potentially avoidable.2,3 Due to financial penalties imposed on hospitals for higher than expected 30-day readmission rates, there is increasing attention to implementing interventions to reduce readmissions in this population.4,5 However, because these programs are resource-intensive, interventions are thought to be most cost-effective if they are targeted to high-risk individuals who are most likely to benefit.6-8
Current pneumonia-specific readmission risk-prediction models that could enable identification of high-risk patients suffer from poor predictive ability, greatly limiting their use, and most were validated among older adults or by using data from single academic medical centers, limiting their generalizability.9-14 A potential reason for poor predictive accuracy is the omission of known robust clinical predictors of pneumonia-related outcomes, including pneumonia severity of illness and stability on discharge.15-17 Approaches using electronic health record (EHR) data, which include this clinically granular data, could enable hospitals to more accurately and pragmatically identify high-risk patients during the index hospitalization and enable interventions to be initiated prior to discharge.
An alternative strategy to identifying high-risk patients for readmission is to use a multi-condition risk-prediction model. Developing and implementing models for every condition may be time-consuming and costly. We have derived and validated 2 multi-condition risk-prediction models using EHR data—1 using data from the first day of hospital admission (‘first-day’ model), and the second incorporating data from the entire hospitalization (‘full-stay’ model) to reflect in-hospital complications and clinical stability at discharge.18,19 However, it is unknown if a multi-condition model for pneumonia would perform as well as a disease-specific model.
This study aimed to develop 2 EHR-based pneumonia-specific readmission risk-prediction models using data routinely collected in clinical practice—a ‘first-day’ and a ‘full-stay’ model—and compare the performance of each model to: 1) one another; 2) the corresponding multi-condition EHR model; and 3) to other potentially useful models in predicting pneumonia readmissions (the Centers for Medicare and Medicaid Services [CMS] pneumonia model, and 2 commonly used pneumonia severity of illness scores validated for predicting mortality). We hypothesized that the pneumonia-specific EHR models would outperform other models; and the full-stay pneumonia-specific model would outperform the first-day pneumonia-specific model.
