IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0259864.html
   My bibliography  Save this article

External validation of the PAR-Risk Score to assess potentially avoidable hospital readmission risk in internal medicine patients

Author

Listed:
  • Lukas Higi
  • Angela Lisibach
  • Patrick E Beeler
  • Monika Lutters
  • Anne-Laure Blanc
  • Andrea M Burden
  • Dominik Stämpfli

Abstract

Background: Readmission prediction models have been developed and validated for targeted in-hospital preventive interventions. We aimed to externally validate the Potentially Avoidable Readmission-Risk Score (PAR-Risk Score), a 12-items prediction model for internal medicine patients with a convenient scoring system, for our local patient cohort. Methods: A cohort study using electronic health record data from the internal medicine ward of a Swiss tertiary teaching hospital was conducted. The individual PAR-Risk Score values were calculated for each patient. Univariable logistic regression was used to predict potentially avoidable readmissions (PARs), as identified by the SQLape algorithm. For additional analyses, patients were stratified into low, medium, and high risk according to tertiles based on the PAR-Risk Score. Statistical associations between predictor variables and PAR as outcome were assessed using both univariable and multivariable logistic regression. Results: The final dataset consisted of 5,985 patients. Of these, 340 patients (5.7%) experienced a PAR. The overall PAR-Risk Score showed rather poor discriminatory power (C statistic 0.605, 95%-CI 0.575–0.635). When using stratified groups (low, medium, high), patients in the high-risk group were at statistically significant higher odds (OR 2.63, 95%-CI 1.33–5.18) of being readmitted within 30 days compared to low risk patients. Multivariable logistic regression identified previous admission within six months, anaemia, heart failure, and opioids to be significantly associated with PAR in this patient cohort. Conclusion: This external validation showed a limited overall performance of the PAR-Risk Score, although higher scores were associated with an increased risk for PAR and patients in the high-risk group were at significantly higher odds of being readmitted within 30 days. This study highlights the importance of externally validating prediction models.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0259864
    DOI: 10.1371/journal.pone.0259864
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0259864
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0259864&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0259864?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. Grolemund, Garrett & Wickham, Hadley, 2011. "Dates and Times Made Easy with lubridate," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i03).
    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.
    3. Karel G M Moons & Joris A H de Groot & Walter Bouwmeester & Yvonne Vergouwe & Susan Mallett & Douglas G Altman & Johannes B Reitsma & Gary S Collins, 2014. "Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist," PLOS Medicine, Public Library of Science, vol. 11(10), pages 1-12, October.
    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. Laha, A. K. & Putatunda, Sayan, 2017. "Travel Time Prediction for Taxi-GPS Data Streams," IIMA Working Papers WP 2017-03-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    2. Fraccaroli, Nicolò & Giovannini, Alessandro & Jamet, Jean-François & Persson, Eric, 2022. "Ideology and monetary policy. The role of political parties’ stances in the European Central Bank’s parliamentary hearings," European Journal of Political Economy, Elsevier, vol. 74(C).
    3. Michael A Ruderman & Deirdra F Wilson & Savanna Reid, 2015. "Does Prison Crowding Predict Higher Rates of Substance Use Related Parole Violations? A Recurrent Events Multi-Level Survival Analysis," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-19, October.
    4. Jiaxin Li & Zijun Zhou & Jianyu Dong & Ying Fu & Yuan Li & Ze Luan & Xin Peng, 2021. "Predicting breast cancer 5-year survival using machine learning: A systematic review," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-23, April.
    5. Igor Martins & Hedibert Freitas Lopes, 2024. "What events matter for exchange rate volatility ?," Papers 2411.16244, arXiv.org.
    6. repec:irf:wpaper:010 is not listed on IDEAS
    7. Fisaha Haile Tesfay & Kathryn Backholer & Christina Zorbas & Steven J. Bowe & Laura Alston & Catherine M. Bennett, 2022. "The Magnitude of NCD Risk Factors in Ethiopia: Meta-Analysis and Systematic Review of Evidence," IJERPH, MDPI, vol. 19(9), pages 1-19, April.
    8. Shamil D. Cooray & Lihini A. Wijeyaratne & Georgia Soldatos & John Allotey & Jacqueline A. Boyle & Helena J. Teede, 2020. "The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal," IJERPH, MDPI, vol. 17(9), pages 1-20, April.
    9. Gregory Eady & Tom Paskhalis & Jan Zilinsky & Richard Bonneau & Jonathan Nagler & Joshua A. Tucker, 2023. "Exposure to the Russian Internet Research Agency foreign influence campaign on Twitter in the 2016 US election and its relationship to attitudes and voting behavior," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    10. Antonio Palazón-Bru & María José Prieto-Castelló & David Manuel Folgado-de la Rosa & Ana Macanás-Martínez & Emma Mares-García & María de los Ángeles Carbonell-Torregrosa & Vicente Francisco Gil-Guillé, 2020. "Development, and Internal, and External Validation of a Scoring System to Predict 30-Day Mortality after Having a Traffic Accident Traveling by Private Car or Van: An Analysis of 164,790 Subjects and ," IJERPH, MDPI, vol. 17(24), pages 1-13, December.
    11. Paulien Van Acker & Wim Van Biesen & Evi V Nagler & Muguet Koobasi & Nic Veys & Jill Vanmassenhove, 2021. "Risk prediction models for acute kidney injury in adults: An overview of systematic reviews," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-14, April.
    12. David Van Dijcke & Austin L. Wright, 2021. "Profiling Insurrection: Characterizing Collective Action Using Mobile Device Data," Working Papers 2021-13, Becker Friedman Institute for Research In Economics.
    13. Srikanth Aravamuthan & Dorte Dopfer & Emil Walleser, 2024. "DairyCoPilot—Automated data compilation and analysis tools for DairyComp data assets," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-12, April.
    14. Christian Thiele & Gerrit Hirschfeld & Ruth Brachel, 2021. "Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9733-9750, December.
    15. Sara J Baart & Veerle Dam & Luuk J J Scheres & Johanna A A G Damen & René Spijker & Ewoud Schuit & Thomas P A Debray & Bart C J M Fauser & Eric Boersma & Karel G M Moons & Yvonne T van der Schouw & on, 2019. "Cardiovascular risk prediction models for women in the general population: A systematic review," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-14, January.
    16. Hebatullah Abdulazeem & Sera Whitelaw & Gunther Schauberger & Stefanie J Klug, 2023. "A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-25, September.
    17. Röseler, Lukas & Bögler, Hannah L. & Koßmann, Lisa & Krueger, Sabine M. & Bickenbach, Sabrina L. C. & Bühler, Ricarda & della Guardia, Jasmin & Köppel, Lisa-Marie A. & Möhring, Jarl & Ponader, Susanne, 2024. "Need for Cognition, Cognitive Load, and Forewarning do not Moderate Anchoring Effects. A Replication Study of Epley & Gilovich (Journal of Behavioral Decision Making, 2005; Psychological Science, 2006," Journal of Comments and Replications in Economics (JCRE), ZBW - Leibniz Information Centre for Economics, vol. 3, pages 1-42.
    18. Ye, Yuxiang & Koch, Steven F. & Ye, Xianming, 2025. "The effect of temperature on household hourly electricity consumption: Evidence from South Africa," Energy, Elsevier, vol. 319(C).
    19. Hans Van Remoortel & Hans Scheers & Emmy De Buck & Winne Haenen & Philippe Vandekerckhove, 2020. "Prediction modelling studies for medical usage rates in mass gatherings: A systematic review," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-20, June.
    20. Laha, A. K. & Putatunda, Sayan, 2017. "Real Time Location Prediction with Taxi-GPS Data Streams," IIMA Working Papers WP 2017-03-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    21. Carrie M. Leslie & Alva I. Strand & Elizabeth A. Ross & Giovanni Tolentino Ramos & Eli S. Bridge & Phillip B. Chilson & Christopher E. Anderson, 2021. "Shifting the Balance among the ‘Three Rs of Sustainability:’ What Motivates Reducing and Reusing?," Sustainability, MDPI, vol. 13(18), pages 1-12, September.

    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:pone00:0259864. 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.