Predicting postoperative delirium after hip arthroplasty for elderly patients using machine learning

Abstract

Background: Postoperative delirium (POD) is a common and severe complication after hip arthroplasty for elderly patients. We aim to develop and validate a machine learning method that determines essential features related to postoperative delirium and predicts POD after hip arthroplasty for elderly patients.Methods: We reviewed preoperative and intraoperative clinical data and laboratory tests of hip arthroplasty elderly patients between January 2017 and April 2021 in Orthopedics of First A liated Hospital of Chongqing Medical University. The Confusion Assessment Method (CAM) was administered to the patients in their perioperative period. Machine learning algorithms were trained to predict the POD and determine leading features. The predictive performance was evaluated using the area under the curve (AUC), accuracy (ACC), sensitivity, speci city, and F1-score.Results: 476 arthroplasty elderly patients (POD = 86, non-POD = 390) with general anesthesia were included in this study, and the combination of feature selection method mutual information (MI) and linear binary classi er using logistic regression (LR) achieved an encouraging performance (AUC = 0.94, ACC = 0.88, sensitivity = 0.85, speci city = 0.90, F1-score = 0.87) on a balanced test dataset. Conclusion: The machine learning (ML) model could predict POD for arthroplasty elderly patients with satisfying accuracy and revealed the major risk factors of suffering POD such as age, Cystatin C, GFR, CHE, CRP, LDH, monocyte count (MONO), history of mental illness or psychotropic drug use and intraoperative blood loss. Proper preoperative interventions for these factors could assist clinicians in reducing the incidence of POD in arthroplasty elderly patients.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence

“…In addition, models exist to explore the risk of general medical complications after orthopaedic surgery 30,[129][130][131][132][133][134][135][136][137][138][139][140][141][142][143][144][145][146][147] (Table V). Using AI to effectively predict postoperative complications, physicians can attempt to minimize the individual risk of complications through targeted interventions, although this potential has been slow to become realized in clinical practice.…”

“…For institutional resources, many health care systems are partnering with large companies with cloud-based servers and technical AI resources, such as Amazon Web Services. Failure to have established a business relationship to obtain such resources, or lack of technology to facilitate large-scale processes (i.e., 143 THA Identification of patients at high risk of experiencing hyponatremia after THA Buddhiraju et al 130 THA Prediction of blood transfusion after primary and revision THA Ding et al 133 THA Prediction of DVT and PE after THA Shah et al 145 THA Prediction of major complications following THA Chen et al 131 THA Prediction of postoperative delirium after THA Knee Jung et al 138 TKA Prediction of postoperative delirium after TKA Jo et al 137 TKA Development of a web-based predictive model for transfusion after TKA Kolin et al 142 TKA Prediction of risk for postoperative anemia and blood transfusion after TKA Tsai et al 147 TKA Identification of patients at high-risk of prolonged opioid prescription after TKA Klemt et al 141 TKA Prediction of extended opioid use after primary TKA Hip & Knee Shohat et al 146 TKA, THA Prediction of VTE and MBE after TKA and THA Gabriel et al 135 TKA, THA Prediction of persistent opioid use among patients undergoing TKA and THA Onishchenko et al 144 TKA, THA Prediction of major adverse cardiac events after TKA and THA Dai et al 132 TKA, THA Prediction of ischemic stroke risk in patients after TKA and THA Huang et al 136 TKA, THA Prediction of risk for postoperative blood transfusion after TKA and THA Harris et al 30 TKA, THA Prediction of 30-day mortality and cardiac complications after TJA Abraham et al 129 rTKA, rTHA Prediction of 30-day mortality, cardiac complications, and respiratory complications after rTKA and rTHA Spine Kim et al 140 Posterior lumbar spinal fusion Prediction of complications after posterior lumbar spinal fusion Kim et al 139 ASD Prediction of surgical complications after ASD surgery Durand et al 134 ASD Prediction of blood transfusion requirement after ASD surgery *ASD 5 adult spinal deformity, DVT 5 deep venous thrombosis, MBE 5 major bleeding event, PE 5 pulmonary embolism, rTHA 5 revision total hip arthroplasty, rTKA 5 revision total knee arthroplasty, THA 5 total hip arthroplasty, TJA 5 total joint arthroplasty, TKA 5 total knee arthroplasty, and VTE 5 venous thromboembolism.…”