Published online Oct 18, 2023. doi: 10.5312/wjo.v14.i10.741
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
Geriatric hip fractures are a frequent occurrence and can lead to increased risks of complications and mortality during prolonged hospital stays. This study focuses on utilizing machine learning (ML) to create predictive models aimed at forecasting extended length of stay (eLOS) in elderly patients with hip fractures.
This research endeavor seeks to construct ML models to forecast eLOS in geriatric patients afflicted with hip fractures. Additionally, the study aims to discern the pertinent risk factors contributing to eLOS and conduct a comparative assessment of the performance of each developed model.
This research endeavors to construct ML models for the purpose of forecasting eLOS in geriatric patients who have suffered hip fractures. Furthermore, it seeks to discern the pertinent risk factors associated with this outcome and conduct a comparative analysis of the model performances. We have successfully formulated a highly precise ML model for the prediction of eLOS in patients with hip fractures. Significantly, factors such as delayed surgical intervention, elevated D-dimer levels, American Society of Anaesthesiologists (ASA) classification, surgical procedure type, and gender exhibited notable associations with eLOS. The integration of ML into clinical settings holds the potential to enhance the diagnostic and therapeutic processes for elderly hip fracture patients, assist clinicians in informed decision-making, and optimize the allocation of healthcare resources.
A retrospective investigation was carried out at a sole orthopaedic trauma center, encompassing all individuals who underwent surgery for hip fractures from January 2018 to December 2022. This study compiled a comprehensive array of patient characteristics, encompassing demographics, general health status, injury-related information, laboratory results, surgical data, and length of hospital stay. Features that demonstrated significant distinctions in univariate analysis were incorporated into the development of ML models, which were subsequently subjected to cross-validation. The research then undertook a comparative assessment of the ML models’ performance and identified the risk factors associated with eLOS.
Incorporating a cohort of 763 patients, of which 380 experienced eLOS, the study evaluated the predictive performance of various ML models, with decision tree random forest, and eXtreme Gradient Boosting models emerging as the most robust. Additionally, the artificial neural network model demonstrated commendable results. Following cross-validation, the support vector machine and logistic regression models displayed superior predictive capabilities. Key predictors for eLOS encompassed delayed surgery, D-dimer levels, ASA classification, type of surgery, and gender.
The application of ML yielded exceptional accuracy in forecasting eLOS among geriatric hip fracture patients. Notably, the study identified significant risk factors, including delayed surgery, D-dimer levels, ASA classification, surgical procedure type, and gender. This valuable insight has the potential to assist clinicians in optimizing resource allocation to meet patient demands more effectively.
Future research in ML applications for predicting eLOS in geriatric hip fracture patients will likely focus on refining models, integrating them into clinical practice, ensuring interpretability, and addressing ethical and practical considerations to enhance the utility and impact of these predictive tools in healthcare.