Development and validation comparison of multiple models for perioperative neurocognitive disorders during hip arthroplasty.

ABSTRACT: This study aims to develop optimal predictive models for perioperative neurocognitive disorders (PND) in hip arthroplasty patients, thereby advancing clinical practice. Data from all hip arthroplasty patients in the MIMIC-IV database were utilized to predict PND. With 62 variables, we applied multiple logistic regression, artificial neural network (ANN), Naive Bayes, support vector machine, and decision tree (XgBoost) algorithms to forecast PND. Feature analysis, receiver operating characteristic curve (ROC) and calibration curve plotting, and sensitivity, specificity, and F-measure β = 1 (F1-score) assessments were conducted on both training and validation sets for classifying models' effectiveness. Brier score and Index of prediction accuracy (IPA) were employed to compare prediction cap

SUBMITTER: Wang G 

PROVIDER: S-EPMC11920437 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature