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 capabilities in both sets. Among 3,292 hip arthroplasty patients in the MIMIC database, 331 developed PND. Five models using different algorithms were constructed. After thorough comparison and validation, the ANN model emerged as the most effective model. Performance metrics on the training set for the ANN model were: ROC: 0.954, Accuracy: 0.938, Precision: 0.758, F1-score: 0.657, Brier Score: 0.048, IPA: 90.8%. On the validation set, the ANN model performed as follows: ROC: 0.857, Accuracy: 0.903, Precision: 0.539, F1-score: 0.432, Brier Score: 0.071, IPA: 71.4%. An online visualization tool was developed ( https://xyyy.pythonanywhere.com/ ).

Keywords: Artificial neural network (ANN); Hip arthroplasty; Model development; Perioperative neurocognitive disorders (PND).

Fig. 1

The relationships among the included variables.

Fig. 2

The key features of models established by different algorithms.

Fig. 3

ROC curves based on different models in the training and validation sets.

Fig. 4

Calibration plots and Decision Curve Analysis based on different models in the training and validation sets.