IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0322101.html
   My bibliography  Save this article

Modelling in-hospital length of stay: A comparison of linear and ensemble models for competing risk analysis

Author

Listed:
  • Juan Carlos Espinosa-Moreno
  • Fernando García-García
  • Naia Mas-Bilbao
  • Susana García-Gutiérrez
  • María José Legarreta-Olabarrieta
  • Dae-Jin Lee

Abstract

Length of Stay (LoS) for in-hospital patients is a relevant indicator of efficiency in healthcare. Moreover, it is often related to the occurrence of hospital-acquired complications. In this work, we aim to explore time-to-event analysis for modelling LoS. We employed competing risk models (CR), as we considered two mutually exclusive outcomes: favorable discharge and deterioration. The explanatory variables included the patient’s sex, age, and longitudinal vital signs collected from a dataset comprising N=19,602 admissions. To address sparse measurements, we transformed longitudinal vital signs into cross-sectional statistics. Our approach involves data pre-processing, imputation of missing data, and variable selection. We proposed four types of CR models: Cause-specific Cox, Sub-distribution hazard, and two variants of Random Survival Forests, with both generalised Log-Rank test (cause-specific hazard estimates) and Gray’s test (cumulative incidences estimations) as node splitting rules. Performance in LoS CR models was evaluated over a time frame from 2 to 15 days. Additionally, we considered baselines with two well-established clinical early warning scores the National Early Warning Score (NEWS) and the Modified Early Warning Score (MEWS). The best model was Random Survival Forest using Gray’s test split, with Integrated Brier Score[×100] of 0.386, C-Index above 99%, and Brier Score below 0.006, along the entire time frame. Employing cross-sectional statistics derived from vital signs, along with rigorous data pre-processing, outperformed the degree of correctness of modelling LoS, compared to NEWS and MEWS.

Suggested Citation

  • Juan Carlos Espinosa-Moreno & Fernando García-García & Naia Mas-Bilbao & Susana García-Gutiérrez & María José Legarreta-Olabarrieta & Dae-Jin Lee, 2025. "Modelling in-hospital length of stay: A comparison of linear and ensemble models for competing risk analysis," PLOS ONE, Public Library of Science, vol. 20(8), pages 1-25, August.
    • Handle: RePEc:plo:pone00:0322101
    DOI: 10.1371/journal.pone.0322101
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322101
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0322101&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0322101?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. C Vasilakis & A H Marshall, 2005. "Modelling nationwide hospital length of stay: opening the black box," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(7), pages 862-869, 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. John Bowers, 2013. "Balancing operating theatre and bed capacity in a cardiothoracic centre," Health Care Management Science, Springer, vol. 16(3), pages 236-244, September.
    2. Asaduzzaman, Md & Chaussalet, Thierry J., 2014. "Capacity planning of a perinatal network with generalised loss network model with overflow," European Journal of Operational Research, Elsevier, vol. 232(1), pages 178-185.
    3. J P Oddoye & M A Yaghoobi & M Tamiz & D F Jones & P Schmidt, 2007. "A multi-objective model to determine efficient resource levels in a medical assessment unit," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1563-1573, December.
    4. Willoughby, Keith A. & Chan, Benjamin T.B. & Marques, Shauna, 2016. "Using simulation to test ideas for improving speech language pathology services," European Journal of Operational Research, Elsevier, vol. 252(2), pages 657-664.
    5. Fermín Mallor & Cristina Azcárate & Julio Barado, 2015. "Optimal control of ICU patient discharge: from theory to implementation," Health Care Management Science, Springer, vol. 18(3), pages 234-250, September.
    6. Fermín Mallor & Cristina Azcárate & Julio Barado, 2016. "Control problems and management policies in health systems: application to intensive care units," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 62-89, June.
    7. N C Proudlove & S Black & A Fletcher, 2007. "OR and the challenge to improve the NHS: modelling for insight and improvement in in-patient flows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 145-158, February.
    8. Mark Fackrell, 2009. "Modelling healthcare systems with phase-type distributions," Health Care Management Science, Springer, vol. 12(1), pages 11-26, March.
    9. Fermín Mallor & Cristina Azcárate, 2014. "Combining optimization with simulation to obtain credible models for intensive care units," Annals of Operations Research, Springer, vol. 221(1), pages 255-271, October.
    10. B Shaw & A H Marshall, 2007. "Modelling the flow of congestive heart failure patients through a hospital system," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 212-218, February.
    11. Bruce Jones & Sally McClean & David Stanford, 2019. "Modelling mortality and discharge of hospitalized stroke patients using a phase-type recovery model," Health Care Management Science, Springer, vol. 22(4), pages 570-588, December.
    12. Azcarate, Cristina & Esparza, Laida & Mallor, Fermin, 2020. "The problem of the last bed: Contextualization and a new simulation framework for analyzing physician decisions," Omega, Elsevier, vol. 96(C).
    13. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G., 2016. "Predictive analytics model for healthcare planning and scheduling," European Journal of Operational Research, Elsevier, vol. 253(1), pages 121-131.
    14. Amir Elalouf & Dmitry Tsadikovich & Sharon Hovav, 2021. "A simulation-based approach for improving the clinical blood sample supply chain," Health Care Management Science, Springer, vol. 24(1), pages 216-233, March.

    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:pone00:0322101. 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.