Construction and Validation of a Predictive Model for Voriconazole Treatment Efficacy in Influenza-Associated Pulmonary Aspergillosis Based on Machine Learning

Purpose

To analyze risk factors affecting voriconazole efficacy in influenza-associated pulmonary aspergillosis (IAPA) and build interpretable machine learning (ML) predictive models. Patients and Methods: This retrospective study enrolled voriconazole-treated IAPA patients into a primary retrospective set (Jan 1, 2020–Jul 31, 2025) and a separate temporal external validation set (Aug 1, 2025–Apr 30, 2026). To avoid data leakage, the primary set was stratified by treatment response and randomly split at a 7:3 ratio into training and internal validation set prior to feature screening. Forty-two baseline candidate variables were collected. Feature selection was performed exclusively within the training dataset via univariate screening, 10-fold cross-validated Least Absolute Shrinkage and Selection Operator (LASSO), and Variance Inflation Factor (VIF) collinearity filtering. Parallel multivariate logistic regression was performed to identify independent predictors without additional stepwise elimination. Validation sets were only used for model performance evaluation. Seven ML algorithms were constructed, and SHapley Additive exPlanations (SHAP) analysis was applied to interpret the best-performing model.