Copyright
©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients
Chu-Wei Tian, Xiang-Xu Chen, Liu Shi, Huan-Yi Zhu, Guang-Chun Dai, Hui Chen, Yun-Feng Rui
Chu-Wei Tian, Xiang-Xu Chen, Liu Shi, Huan-Yi Zhu, Guang-Chun Dai, Hui Chen, Yun-Feng Rui, Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
Chu-Wei Tian, Xiang-Xu Chen, Liu Shi, Huan-Yi Zhu, Guang-Chun Dai, Hui Chen, Yun-Feng Rui, Multidisciplinary Team for Geriatric Hip Fracture Comprehensive Management, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
Chu-Wei Tian, Xiang-Xu Chen, Liu Shi, Huan-Yi Zhu, Guang-Chun Dai, Hui Chen, Yun-Feng Rui, Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, Jiangsu Province, China
Author contributions: Tian CW and Chen XX contributed equally to this work; Tian CW and Chen XX designed this study and wrote the manuscript; Zhu HY performed the experiments and analyzed the data; Tian CW and Zhu HY collected the clinical data; Shi L, Dai GC and Chen H provided technical support; Rui YF provided the idea and revised and proofread the paper; All the authors have read and approved the final manuscript.
Supported by Winfast Charity Foundation for Financial Support, No. YL20220525.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Zhongda Hospital, Affiliated to Southeast University (2022ZDSYLL183-P01).
Informed consent statement: All study participants or their legal guardian provided informed written consent about personal and medical data collection prior to study enrolment.
Conflict-of-interest statement: The authors declare no competing financial interests or conflicts of interest that could have influenced the design, data collection, analysis, interpretation, or publication of this study.
Data sharing statement: Requests for data access should be directed to the corresponding author at ruiyunfeng@126.com.
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: Yun-Feng Rui, MD, PhD, Chief Doctor, Deputy Director, Professor, Surgeon, Department of Orthopaedics, Zhongda Hospital, School of Medicine, Southeast University, No. 87 Dingjiaqiao, Nanjing 210009, Jiangsu Province, China. ruiyunfeng@126.com
Received: July 22, 2023
Peer-review started: July 22, 2023
First decision: September 4, 2023
Revised: September 8, 2023
Accepted: September 28, 2023
Article in press: September 28, 2023
Published online: October 18, 2023
BACKGROUND
Geriatric hip fractures are one of the most common fractures in elderly individuals, and prolonged hospital stays increase the risk of death and complications. Machine learning (ML) has become prevalent in clinical data processing and predictive models. This study aims to develop ML models for predicting extended length of stay (eLOS) among geriatric patients with hip fractures and to identify the associated risk factors.
AIM
To develop ML models for predicting the eLOS among geriatric patients with hip fractures, identify associated risk factors, and compare the performance of each model.
METHODS
A retrospective study was conducted at a single orthopaedic trauma centre, enrolling all patients who underwent hip fracture surgery between January 2018 and December 2022. The study collected various patient characteristics, encompassing demographic data, general health status, injury-related data, laboratory examinations, surgery-related data, and length of stay. Features that exhibited significant differences in univariate analysis were integrated into the ML model establishment and subsequently cross-verified. The study compared the performance of the ML models and determined the risk factors for eLOS.
RESULTS
The study included 763 patients, with 380 experiencing eLOS. Among the models, the decision tree, random forest, and extreme Gradient Boosting models demonstrated the most robust performance. Notably, the artificial neural network model also exhibited impressive results. After cross-validation, the support vector machine and logistic regression models demonstrated superior performance. Predictors for eLOS included delayed surgery, D-dimer level, American Society of Anaesthesiologists (ASA) classification, type of surgery, and sex.
CONCLUSION
ML proved to be highly accurate in predicting the eLOS for geriatric patients with hip fractures. The identified key risk factors were delayed surgery, D-dimer level, ASA classification, type of surgery, and sex. This valuable information can aid clinicians in allocating resources more efficiently to meet patient demand effectively.
Core Tip: Traditional models have been built to identify risk factors for extended length of stay (eLOS), offering new insights for optimizing treatment for hip fracture patients under the enhanced recovery after surgery concept. However, these traditional statistical methods suffer from poor performance and lack of features. Machine learning (ML) is a scientific discipline focused on teaching computers to learn from data, showing superior predictive performance compared to traditional methods. This study aims to develop ML models for predicting eLOS among geriatric patients with hip fractures, identify associated risk factors, and compare the performance of each model.
