aceConstruction 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 rat…
A CatBoost model flagged likely voriconazole non-response in IAPA patients with AUC 0.88 internally and 0.83 on a later temporal validation set, which could support individualized antifungal therapy.
The SHAP-based feature importance only partly matched the logistic regression ranking, and the external validation AUC had a wide confidence interval (0.70-0.94), indicating uncertainty about performance in new patients.
Evidence
- Peer-reviewedDrug Design, Development and Therapy2026-10-12
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Truvace Impact Record TRV-2026-2062, v1: “Construction and Validation of a Predictive Model for Voriconazole Treatment Efficacy in Influenza-Associated Pulmonary Aspergillosis Based on Machine Learning.” Truvace, 2026-10-21. /record/TRV-2026-2062 (accessed at citation time). sha256 2cede59c7a81190f…
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