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Retrospective Study
Copyright ©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Feb 6, 2026; 14(4): 117700
Published online Feb 6, 2026. doi: 10.12998/wjcc.v14.i4.117700
Machine learning model for predicting hospital-acquired functional decline in older patients with postoperative cardiovascular surgery
Ryotaro Hiramatsu, Shinsuke Imaoka, Shohei Minata, Hidenori Sako, Noboru Sato
Ryotaro Hiramatsu, Shinsuke Imaoka, Shohei Minata, Department of Rehabilitation, Oita Oka Hospital, Oita 870-0192, Japan
Hidenori Sako, Department of Cardiovascular Surgery, Oita Oka Hospital, Oita 870-0192, Japan
Noboru Sato, Digital Promotion Bureau, Keiwakai Social Healthcare Corporation, Oita 870-0192, Japan
Co-first authors: Ryotaro Hiramatsu and Noboru Sato.
Co-corresponding authors: Shinsuke Imaoka and Shohei Minata.
Author contributions: Hiramatsu R, Sato N, Imaoka S, and Minata S conceptualized and designed the study; Sato N and Hiramatsu R were responsible for the comprehensive data management and established the clinical database; Sako H and Minata S assisted in data collection; Minata S, Imaoka S, and Sato N performed the statistical analysis; Hiramatsu R, Imaoka S, Sato N, and Minata S contributed to the analysis and interpretation of the data; Hiramatsu R and Sato N wrote the original draft of the manuscript. All authors have read and approved the final manuscript. Hiramatsu R and Sato N contributed equally to this work and are identified as the co-first authors. Hiramatsu R led the conceptual framework and primary drafting of the manuscript, integrating clinical rehabilitative insights. Sato N was instrumental in designing the data architecture, managing the longitudinal clinical data, and preparing the manuscript’s methodology section. Both authors’ combined efforts were essential for the study’s execution and the completion of the first draft. Imaoka S and Minata S served as the co-corresponding authors. Imaoka S provided overall strategic supervision and secured the necessary institutional resources, focusing on the clinical validity of the research. Minata S oversaw the rigorous statistical modeling and was responsible for the advanced data re-interpretation and validation. Both Imaoka S and Minata S shared the responsibility for critical intellectual revisions, managed the peer-review process, and take full responsibility for the integrity and academic standards of the published work.
Institutional review board statement: This study was reviewed and approved by the Institutional Review Board of Oita Oka Hospital (approval No. A0078).
Informed consent statement: Informed consent was obtained in the form of an opt-out procedure.
Conflict-of-interest statement: All authors declare that they have no conflict of interest to disclose.
Data sharing statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Shinsuke Imaoka, PhD, Research Fellow, Department of Rehabilitation, Oita Oka Hospital, 3-7-11 Nishitsurusaki, Oita 870-0192, Japan. imaoka2734@keiwakai.oita.jp
Received: December 15, 2025
Revised: January 8, 2026
Accepted: January 23, 2026
Published online: February 6, 2026
Processing time: 53 Days and 23.7 Hours
Abstract
BACKGROUND

Hospital-acquired functional decline (HAFD) is a poor prognostic factor in older patients who have undergone cardiovascular surgery.

AIM

To develop a model to predict HAFD and to identify its associated factors.

METHODS

This retrospective observational study included 144 patients who underwent cardiovascular surgery between May 2019 and December 2023. HAFD was defined as a change in the preoperative and pre-discharge short physical performance battery score. Seven machine learning models were constructed, and their performance was evaluated using the area under the receiver operating characteristic curve (AUC) values. The models were further interpreted using SHapley Additive exPlanations (SHAP) values.

RESULTS

Among the 144 participants, 41 (28.5%) experienced HAFD. Of the 7 machine learning models, the extreme gradient boosting model (XGBoost) achieved the best performance, with an AUC of 0.87. SHAP analysis revealed that being female and having a slower preoperative walking speed markedly impacted HAFD occurrence.

CONCLUSION

We developed a high-accuracy model to predict HAFD in older patients who have undergone cardiovascular surgery and identified key associated factors, informing preoperative evaluations and interventions in clinical practice.

Keywords: Cardiac surgery; Machine learning; Hospital-acquired functional decline; Extreme gradient boosting model; Predictive modeling

Core Tip: Hospital-acquired functional decline (HAFD) is a critical yet underrecognized complication in older patients undergoing cardiovascular surgery. We developed and validated a machine learning–based prediction model for HAFD using preoperative clinical and physical function data. Among seven models, the extreme gradient boosting model demonstrated the highest predictive performance. SHapley Additive exPlanations analysis identified female sex and slower preoperative walking speed as key contributors to HAFD. This interpretable model may support early risk stratification and targeted preoperative interventions to prevent functional decline in clinical practice.