Abstract
Background: Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). We assessed how well a previously validated 30-day EHR-based readmission prediction model predicts 7-day readmissions and compared differences in strength of predictors. Methods: We conducted an observational study on adult hospitalizations from 6 diverse hospitals in North Texas using a 50-50 split-sample derivation and validation approach. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We then compared the discrimination and calibration of the 7-day model to the 30-day model to assess model performance. To examine the changes in the point estimates between the two models, we evaluated the percent changes in coefficients. Results: Of 32,922 index hospitalizations among unique patients, 4.4% had a 7-day admission and 12.7% had a 30-day readmission. Our original 30-day model had modestly lower discrimination for predicting 7-day vs. any 30-day readmission (C-statistic of 0.66 vs. 0.69, p ≤ 0.001). Our re-derived 7-day model had similar discrimination (C-statistic of 0.66, p = 0.38), but improved calibration. For the re-derived 7-day model, discharge day factors were more predictive of early readmissions, while baseline characteristics were less predictive. Conclusion: A previously validated 30-day readmission model can also be used as a stopgap to predict 7-day readmissions as model performance did not substantially change. However, strength of predictors differed between the 7-day and 30-day model; characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.
Original language | English (US) |
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Article number | 227 |
Journal | BMC Medical Informatics and Decision Making |
Volume | 20 |
Issue number | 1 |
DOIs | |
State | Published - Sep 15 2020 |
Keywords
- Care transitions
- Clinical decision support
- Early readmissions
- Healthcare quality improvement
- Hospital medicine
- Hospital utilization
- Predictive model
ASJC Scopus subject areas
- Health Policy
- Health Informatics
- Computer Science Applications