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External validation of EPIC’s Risk of Unplanned Readmission model, the LACE+ index and SQLape® as predictors of unplanned hospital readmissions: A monocentric, retrospective, diagnostic cohort study in Switzerland

Citation

Hwang, Aljoscha Benjamin (2021), External validation of EPIC’s Risk of Unplanned Readmission model, the LACE+ index and SQLape® as predictors of unplanned hospital readmissions: A monocentric, retrospective, diagnostic cohort study in Switzerland, Dryad, Dataset, https://doi.org/10.5061/dryad.70rxwdbxw

Abstract

Introduction: Readmissions after an acute care hospitalization are relatively common, costly to the health care system, and are associated with significant burden for patients. As one way to reduce costs and simultaneously improve quality of care, hospital readmissions receive increasing interest from policy makers. It is only relatively recently that strategies were developed with the specific aim of reducing unplanned readmissions using prediction models to identify patients at risk. EPIC’s Risk of Unplanned Readmission model promises superior performance. However, it has only been validated for the US setting. Therefore, the main objective of this study is to externally validate the EPIC’s Risk of Unplanned Readmission model and to compare it to the internationally, widely used LACE+ index, and the SQLAPE® tool, a Swiss national quality of care indicator.

Methods: A monocentric, retrospective, diagnostic cohort study was conducted. The study included inpatients, who were discharged between the 1st of January 2018 and the 31st of December 2019 from the Lucerne Cantonal Hospital, a tertiary-care provider in Central Switzerland. The study endpoint was an unplanned 30-day readmission. Models were replicated using the original intercept and beta coefficients as reported. Otherwise, score generator provided by the developers were used. For external validation, discrimination of the scores under investigation were assessed by calculating the area under the receiver operating characteristics curves (AUC). Calibration was assessed with the Hosmer-Lemeshow X2 goodness-of-fit test This report adheres to the TRIPOD statement for reporting of prediction models.

Results: At least 23,116 records were included. For discrimination, the EPIC´s prediction model, the LACE+ index and the SQLape® had AUCs of 0.692 (95% CI 0.676-0.708), 0.703 (95% CI 0.687-0.719) and 0.705 (95% CI 0.690-0.720). The Hosmer-Lemeshow X2 tests had values of p<0.001.

Conclusion: In summary, the EPIC´s model showed less favorable performance than its comparators. It may be assumed with caution that the EPIC´s model complexity has hampered its wide generalizability - model updating is warranted.