Development and validation of a machine learning-based model for postoperative ischemic stroke in middle-aged and elderly patients with hip or knee arthroplasty.

Postoperative ischemic stroke in middle-aged and elderly patients with hip or knee arthroplasty remains a major postoperative challenge, little is known about its incidence and risk factors. This study sought to create a nomogram for precise prediction of ischemic stroke after hip or knee arthroplasty. Discharge data of all middle-aged and elderly patients undergoing primary hip or knee arthroplasty from May 2013 to October 2020 were queried. These patients were then followed up over time to determine their risk of ischemic stroke. Clinical parameters and blood biochemical features were analyzed by the use of univariable and multivariable generalized logistic regression analysis. A nomogram to predict the risk of ischemic stroke was constructed and validated with bootstrap resampling. Eight hundred twenty-eight patients were included for analysis; Fifty-one were diagnosed with ischemic stroke. After final regression analysis, age, the neutrophil-to-lymphocyte ratio (NLR), a standard deviation of red blood cell distribution width, American Society of Anesthesiologists, low-density lipoprotein, and diabetes were identified and were entered into the nomogram. The nomogram showed an area under the receiver operating characteristic curve of 0. 841 (95% confidence interval [CI], 0.809-0.871). The calibration curves for the probability of ischemic stroke showed optimal agreement between the probability as predicted by the nomogram and the actual probability (Hosmer-Lemeshow test: P = .818). We developed a practical nomogram that can predict the risk of ischemic stroke for middle-aged and elderly patients with hip or knee arthroplasty. This model has the potential to assist clinicians in making treatment recommendations.