Construction and validation of a predictive model based on machine learning algorithm for the risk of subsyndromic delirium after hip arthroplasty in the elderly

Background: subsyndromal delirium (SSD) can gradually develop into complete delirium, leading to prolonged hospital stays, increased risks of complications and death. This study aims to construct and validate a postoperative subsyndromal delirium risk prediction model for elderly hip arthroplasty patients using machine learning algorithms based on our electronic health record data. Methods: Electronic data of older adults who underwent hip arthroplasty at Tangdu Hospital, Second Affiliated Hospital of Air Force Military Medical University from January 2020 to December 2023 were retrospectively analyzed. The data were divided into a training set (n=442) and a validation set (n=190) according to a ratio of 7:3, and four machine learning algorithms: logistic regression (LR), extreme gradient boosting (XGB), Gaussian Naive Bayes (GNB), and random forests (RF) were used to construct a predictive model of postoperative SSD. The area under the receiver operating characteristic curve (AUROC), sensitivity, accuracy, and area under the precision-recall curve (AUPRC) were used to evaluate the above four prediction models, and the variables were selected based on the SHAP values for the prediction model based on the optimal machine learning algorithm. Results : A total of 632 elderly patients who underwent hip arthroplasty were included in this study, of which 265 (41.93%) developed postoperative SSD, with 23 (3.64%) progressed from SSD to POD. After a comprehensive analysis, we ultimately chose the optimal-performing XGB-based algorithm to develop the prediction model. The first 6 variables selected based on SHAP values were: operative time, ASA classification, cumulative duration of intraoperative hypotension, previous delirium, age, and postoperative use of analgesics. The postoperative SSD prediction model developed based on these 6 variables had an AUROC of 0.849 (0.769-0.929), and an AUPRC of 0.657 (0.600-0.714). In the external test set, the AUROC was 0.842 (0.754-0.930) and the AUPRC was 0.650 (0.621-0.679). Conclusion: We used machine learning techniques to select six variables and demonstrate a predictive model for the risk of postoperative SSD in older adults with hip arthroplasty. The model would provide a useful tool for identifying patients at high risk for postoperative SSD, aiding in the identification of those with elevated postoperative SSD risk.