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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

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 fr...

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Autores principales: Hwang, Aljoscha Benjamin, Schuepfer, Guido, Pietrini, Mario, Boes, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589185/
https://www.ncbi.nlm.nih.gov/pubmed/34767558
http://dx.doi.org/10.1371/journal.pone.0258338
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author Hwang, Aljoscha Benjamin
Schuepfer, Guido
Pietrini, Mario
Boes, Stefan
author_facet Hwang, Aljoscha Benjamin
Schuepfer, Guido
Pietrini, Mario
Boes, Stefan
author_sort Hwang, Aljoscha Benjamin
collection PubMed
description 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 1(st) of January 2018 and the 31(st) 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 X(2) 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 X(2) 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.
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spelling pubmed-85891852021-11-13 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 Hwang, Aljoscha Benjamin Schuepfer, Guido Pietrini, Mario Boes, Stefan PLoS One Research Article 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 1(st) of January 2018 and the 31(st) 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 X(2) 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 X(2) 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. Public Library of Science 2021-11-12 /pmc/articles/PMC8589185/ /pubmed/34767558 http://dx.doi.org/10.1371/journal.pone.0258338 Text en © 2021 Hwang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Hwang, Aljoscha Benjamin
Schuepfer, Guido
Pietrini, Mario
Boes, Stefan
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589185/
https://www.ncbi.nlm.nih.gov/pubmed/34767558
http://dx.doi.org/10.1371/journal.pone.0258338
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