Prediction of hospital readmission of multimorbid patients using machine learning models

PLOS ONE RESEARCH ARTICLE Prediction of hospital readmission of multimorbid patients using machine learning models Jules Le Lay ID1☯, Edgar Alfonso-Lizarazo2☯, Vincent Augusto1‡*, Bienvenu Bongue3,4, Malek Masmoudi5, Xiaolan Xie1, Baptiste Gramont6, Thomas Célarier4,7,8‡ a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Mines Saint-Etienne, Univ Clermont Auvergne, INP Clermont Auvergne, CNRS, UMR 6158 LIMOS, Centre CIS, Saint-Étienne France, 2 Université de Lyon, Univ Jean Monnet Saint-Étienne, LASPI, EA3059, SaintÉtienne, France, 3 Centre technique d’appui et de formation des centers d’examens de santé (CETAF), INSERM, U1059, SAINBIOSE, Dysfonction Vasculaire et Hémostase, Université de Lyon, Université Jean Monnet, Saint-Étienne, France, 4 Chaire Santé des Ainés, University of Jean Monnet, Saint-Étienne, France, 5 University of Sharjah, College of Engineering, Sharjah, United Arab Emirates, 6 Department of Internal Medicine, University Hospital of Saint-Etienne, Saint-Étienne, France, 7 Department of Clinical Gerontology, University Hospital of Saint-Etienne, Saint-Étienne, France, 8 Gérontopôle Auvergne-Rhône-Alpes, SaintÉtienne, France ☯ These authors contributed equally to this work. ‡ VA and TC also contributed equally to this work. * OPEN ACCESS Citation: Le Lay J, Alfonso-Lizarazo E, Augusto V, Bongue B, Masmoudi M, Xie X, et al. (2022) Prediction of hospital readmission of multimorbid patients using machine learning models. PLoS ONE 17(12): e0279433. https://doi.org/10.1371/ journal.pone.0279433 Editor: Antonio De Vincentis, University Campus Bio-Medico di Roma, ITALY Received: March 24, 2022 Accepted: December 7, 2022 Published: December 22, 2022 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0279433 Copyright: © 2022 Le Lay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data cannot be shared publicly. Data are available from the CNIL for researchers who meet the criteria for access to confidential data (data accessed with CNIL Abstract Objective The objective of this study is twofold. First, we seek to understand the characteristics of the multimorbid population that needs hospital care by using all diagnoses information (ICD-10 codes) and two aggregated multimorbidity and frailty scores. Second, we use machine learning prediction models on these multimorbid patients characteristics to predict rehospitalization within 30 and 365 days and their length of stay. Methods This study was conducted on 8 882 anonymized patients hospitalized at the University Hospital of Saint-Étienne. A descriptive statistical analysis was performed to better understand the characteristics of the patient population. Multimorbidity was measured using raw diagnoses information and two specific scores based on clusters of diagnoses: the Hospital Frailty Risk Score and the Calderon-Larrañaga index. Based on these variables different machine learning models (Decision Tree, Random forest and k-nearest Neighbors) were used to predict near future rehospitalization and length of stay (LoS). Results The use of random forest algorithms yielded better performance to predict both 365 and 30 days rehospitalization and using the diagnoses ICD-10 codes directly was significantly more efficient. However, using the Calderon-Larrañaga’s clusters of diagnoses can be used as an PLOS ONE | https://doi.org/10.1371/journal.pone.0279433 December 22, 2022 1 / 15 PLOS ONE authorization number 919300). CNIL web site: https://www.cnil.fr/. Funding: This work was funded by the ‘Agence Nationale de la Recherche’, which funded the thesis on the study of multimorbid pathways under grant number ANR-18-CE19-0016. (thesis of author JLL, project manager VA). https://anr.fr/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Prediction of hospital readmission of multimorbid patients using machine learning models efficient substitute for diagnoses information for predicting readmission. The predictive power of the algorithms is quite low on length of stay indicator. Conclusion Using machine learning techniques using patients’ diagnoses information and CalderonLarrañaga’s score yielded efficient results to predict hospital readmission of multimorbid patients. These methods could help improve the management of care of multimorbid patients in hospitals. Competing interests: The authors have declared that no competing interests exist. Introduction The management for care of multimorbid patients, in hospitals is a rising concern among the scientific community. Multimorbid patients tend to have more complex needs and require coordinated care from several providers [1]. Multimorbidity, defined as the “co-occurrence of multiple chronic or acute diseases and medical conditions within one person” [2], is highly prevalent in Europe. Based on the Survey on Health, Aging and Retirement in Europe (SHARE) Nielsen et al. [3] found that 31.42% of the participants above 50 years old in 14 European countries and Israel were affected by multimorbidity.. Southern Europe (Italy, Spain, France and Israel) and Northern Europe (Denmark, Sweden, and the Netherlands) had a slightly lower multimorbidity prevalence (29.8% and 26.2% respectively). Currently, there are multiple research projects to improve the overall quality of care both inside and outside of healthcare centers, establishing dedicated care pathways for multimorbid patients [4–6]. Barnett et al. [7] reported an association between age, sex, deprivation and multimorbidity based on a list of 40 medical conditions. This list was built using policy recommendations and important chronic conditions identified in [8]. However, counting conditions can be quite limiting, and are a controversial measure of multimorbidity for these studies [9]. [10] highlighted the importance of using a standard measure of multimorbidity to analyze and compare the results of studies in which different scores have been built to describe multimorbidity. The most common measure of multimorbidity is the Charlson comorbidity index score, originally introduced in 1987 [11] and first updated in 1994 [12] and numerous times since to be applied with administrative databases [13, 14] or to predict other outcomes [15]. In a recent systematic review, [9] explored the different multimorbidity measures developed outside of the counts of conditions. The hospital frailty risk score (HFRS), which uses weighted co (...truncated)