IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0258338.html
   My bibliography  Save this article

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

Author

Listed:
  • Aljoscha Benjamin Hwang
  • Guido Schuepfer
  • Mario Pietrini
  • Stefan Boes

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

Suggested Citation

  • Aljoscha Benjamin Hwang & Guido Schuepfer & Mario Pietrini & Stefan Boes, 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," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-33, November.
    • Handle: RePEc:plo:pone00:0258338
    DOI: 10.1371/journal.pone.0258338
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0258338
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0258338&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0258338?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Margaret Pepe & Holly Janes & Gary Longton & Wendy Leisenring & Polly Newcomb, 2004. "Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic or Prognostic Marker," UW Biostatistics Working Paper Series 1035, Berkeley Electronic Press.
    2. Anne-Laure Blanc & Thierry Fumeaux & Jérôme Stirnemann & Elise Dupuis Lozeron & Aimad Ourhamoune & Jules Desmeules & Pierre Chopard & Arnaud Perrier & Nicolas Schaad & Pascal Bonnabry, 2019. "Development of a predictive score for potentially avoidable hospital readmissions for general internal medicine patients," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-16, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Debashis Ghosh & Michael S. Sabel, 2022. "A Weighted Sample Framework to Incorporate External Calculators for Risk Modeling," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 363-379, December.
    2. Anna-Karin Ivert & Marie Torstensson Levander & Juan Merlo, 2013. "Adolescents' Utilisation of Psychiatric Care, Neighbourhoods and Neighbourhood Socioeconomic Deprivation: A Multilevel Analysis," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-1, November.
    3. Margaret Sullivan Pepe & Tianxi Cai & Gary Longton, 2006. "Combining Predictors for Classification Using the Area under the Receiver Operating Characteristic Curve," Biometrics, The International Biometric Society, vol. 62(1), pages 221-229, March.
    4. Holly Janes & Margaret S. Pepe, 2008. "Matching in Studies of Classification Accuracy: Implications for Analysis, Efficiency, and Assessment of Incremental Value," Biometrics, The International Biometric Society, vol. 64(1), pages 1-9, March.
    5. Carlos A Labarrere & John R Woods & James W Hardin & Beate R Jaeger & Marian Zembala & Mario C Deng & Ghassan S Kassab, 2014. "Early Inflammatory Markers Are Independent Predictors of Cardiac Allograft Vasculopathy in Heart-Transplant Recipients," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-18, December.
    6. Lukas Higi & Angela Lisibach & Patrick E Beeler & Monika Lutters & Anne-Laure Blanc & Andrea M Burden & Dominik Stämpfli, 2021. "External validation of the PAR-Risk Score to assess potentially avoidable hospital readmission risk in internal medicine patients," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-14, November.
    7. Diego Tomassi & Liliana Forzani & Efstathia Bura & Ruth Pfeiffer, 2017. "Sufficient dimension reduction for censored predictors," Biometrics, The International Biometric Society, vol. 73(1), pages 220-231, March.
    8. Osamu Komori, 2011. "A boosting method for maximization of the area under the ROC curve," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(5), pages 961-979, October.
    9. Kenichi Hayashi & Shinto Eguchi, 2024. "A new integrated discrimination improvement index via odds," Statistical Papers, Springer, vol. 65(8), pages 4971-4990, October.
    10. Tianle Chen & Yuanjia Wang & Huaihou Chen & Karen Marder & Donglin Zeng, 2014. "Targeted Local Support Vector Machine for Age-Dependent Classification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1174-1187, September.
    11. Anna Persmark & Maria Wemrell & Sofia Zettermark & George Leckie & S V Subramanian & Juan Merlo, 2019. "Precision public health: Mapping socioeconomic disparities in opioid dispensations at Swedish pharmacies by Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-21, August.
    12. Michael Lebenbaum & Osvaldo Espin-Garcia & Yi Li & Laura C Rosella, 2018. "Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT)," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-11, January.
    13. Michael King & Louise Marston & Igor Švab & Heidi-Ingrid Maaroos & Mirjam I Geerlings & Miguel Xavier & Vicente Benjamin & Francisco Torres-Gonzalez & Juan Angel Bellon-Saameno & Danica Rotar & Anu Al, 2011. "Development and Validation of a Risk Model for Prediction of Hazardous Alcohol Consumption in General Practice Attendees: The PredictAL Study," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-10, August.
    14. Fagrell Trygg, Nadja & Månsdotter, Anna & Gustafsson, Per E., 2021. "Intersectional inequalities in mental health across multiple dimensions of inequality in the Swedish adult population," Social Science & Medicine, Elsevier, vol. 283(C).
    15. Wei Zhang & Larry L. Tang & Qizhai Li & Aiyi Liu & Mei‐Ling Ting Lee, 2020. "Order‐restricted inference for clustered ROC data with application to fingerprint matching accuracy," Biometrics, The International Biometric Society, vol. 76(3), pages 863-873, September.
    16. Vanya Van Belle & Ben Van Calster, 2015. "Visualizing Risk Prediction Models," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-16, July.
    17. Shuangge Ma & Jian Huang, 2007. "Combining Multiple Markers for Classification Using ROC," Biometrics, The International Biometric Society, vol. 63(3), pages 751-757, September.
    18. Pia Kjær Kristensen & Raquel Perez-Vicente & George Leckie & Søren Paaske Johnsen & Juan Merlo, 2020. "Disentangling the contribution of hospitals and municipalities for understanding patient level differences in one-year mortality risk after hip-fracture: A cross-classified multilevel analysis in Swed," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-14, June.
    19. Quang Bao Le & Boubaker Dhehibi, 2019. "A Typology-Based Approach for Assessing Qualities and Determinants of Adoption of Sustainable Water Use Technologies in Coping with Context Diversity: The Case of Mechanized Raised-Bed Technology in E," Sustainability, MDPI, vol. 11(19), pages 1-21, September.
    20. Hajime Uno & Tianxi Cai & Lu Tian & L. J. Wei, 2011. "Graphical Procedures for Evaluating Overall and Subject-Specific Incremental Values from New Predictors with Censored Event Time Data," Biometrics, The International Biometric Society, vol. 67(4), pages 1389-1396, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0258338. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.