AHRQ Study Examines Risk Models for Predicting Hospital Readmissions

A risk prediction model designed to forecast hospital readmissions based on patients’ electronic health records (EHR) for entire hospital stays was only modestly better than a model based on EHR data for just the day of admission, according to a new AHRQ-funded study. The study on predicting 30-day, all-cause hospital readmissions was based on data from nearly 33,000 hospital stays at six Dallas-Fort Worth hospitals from 2009 to 2010. The authors said their model was the first to use comprehensive EHR data from entire hospital stays. That model was shown to be only modestly better at predicting risk of readmission despite including many additional clinically relevant prognostic factors. The authors said customized models that are disease-specific may be more effective in predicting readmission compared with the common multi-condition, readmission risk prediction model. The study “Predicting All-Cause Readmissions Using Electronic Health Record Data From the Entire Hospitalization: Model Development and Comparison” appeared in the Journal of Hospital Medicine. Access the abstract.

2016 Feb 29. doi: 10.1002/jhm.2568. [Epub ahead of print]

Predicting all-cause readmissions using electronic health record data from the entire hospitalization: Modeldevelopment and comparison.

Nguyen OK1,2, Makam AN1,2, Clark C3, Zhang S4, Xie B3, Velasco F5, Amarasingham R1,2,3, Halm EA1,2.

Author information

Abstract

BACKGROUND:

Incorporating clinical information from the full hospital course may improve prediction of 30-day readmissions.

OBJECTIVE:

To develop an all-cause readmissions risk-prediction model incorporating electronic health record (EHR) data from the full hospital stay, and to compare "full-stay" model performance to a "first day" and 2 other validated models, LACE (includes Length of stay, Acute [nonelective] admission status, Charlson Comorbidity Index, and Emergency department visits in the past year), and HOSPITAL (includes Hemoglobin at discharge, discharge from Oncology service, Sodium level at discharge, Procedure during index hospitalization, Index hospitalization Type [nonelective], number of Admissions in the past year, and Length of stay).

DESIGN:

Observational cohort study.

SUBJECTS:

All medicine discharges between November 2009 and October 2010 from 6 hospitals in North Texas, including safety net, teaching, and nonteaching sites.

MEASURES:

Thirty-day nonelective readmissions were ascertained from 75 regional hospitals.

RESULTS:

Among 32,922 admissions (validation = 16,430), 12.7% were readmitted. In addition to many first-day factors, we identified hospital-acquired Clostridium difficile infection (adjusted odds ratio [AOR]: 2.03, 95% confidence interval [CI]: 1.18-3.48), vital sign instability on discharge (AOR: 1.25, 95% CI: 1.15-1.36), hyponatremia on discharge (AOR: 1.34, 95% CI: 1.18-1.51), and length of stay (AOR: 1.06, 95% CI: 1.04-1.07) as significant predictors. The full-stay model had better discrimination than other models though the improvement was modest (C statistic 0.69 vs 0.64-0.67). It was also modestly better in identifying patients at highest risk for readmission (likelihood ratio +2.4 vs. 1.8-2.1) and in reclassifying individuals (net reclassification index 0.02-0.06).

CONCLUSIONS:

Incorporating clinically granular EHR data from the full hospital stay modestly improves prediction of 30-day readmissions. Given limited improvement in prediction despite incorporation of data on hospital complications, clinical instabilities, and trajectory, our findings suggest that many factors influencing readmissions remain unaccounted for. Further improvements in readmission models will likely require accounting for psychosocial and behavioral factors not currently captured by EHRs. Journal of Hospital Medicine 2016. © 2016 Society of Hospital Medicine.