Key result
A re-derived 7-day readmission risk prediction model had similar discrimination as the original 30-day model for predicting 7-day readmissions (C-statistic 0.66 vs 0.66, p=0.38), though with improved calibration.
Why the study?
Early readmissions within 7 days of discharge may be more preventable than later readmissions, prompting assessment of how well a validated 30-day EHR-based model predicts 7-day readmissions.
Does a re-derived 7-day readmission prediction model improve prediction of 7-day readmissions compared to a standard 30-day model in adult hospitalizations?
Observational (n=32,922)
Yes
Does a re-derived 7-day readmission prediction model improve prediction of 7-day readmissions compared to a standard 30-day model in adult hospitalizations?
A standard 30-day hospital readmission risk prediction model can be effectively used as a stopgap to predict early 7-day readmissions, though discharge characteristics are more predictive for early readmissions.
Answers are grounded in this paper and cite their sources.
Existing 30-day models may suffice temporarily for 7-day readmission prediction; leaves open need for prospective validation of re-derived models.
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.
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Saleh et al. (2020) conducted an observational in Hospital readmission (n=32,922). Re-derived 7-day readmission risk prediction model vs. Original 30-day readmission risk prediction model was evaluated on Discrimination (C-statistic) for predicting 7-day readmissions (p=0.38). A re-derived 7-day readmission risk prediction model had similar discrimination as the original 30-day model for predicting 7-day readmissions (C-statistic 0.66 vs 0.66, p=0.38), though with improved calibration.
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