Machine learning-based risk prediction for invasive pulmonary aspergillosis in non-neutropenic intensive care unit patients, with an exploratory analysis of pathogen-related risk factors

Abstract

Background Invasive pulmonary aspergillosis (IPA) is increasingly recognized in non-neutropenic patients, but validated bedside risk stratification tools remain scarce. We developed a machine learning model to predict IPA and tested whether corticosteroid effect depends on bacterial co-pathogen. Methods This multicenter retrospective cohort study enrolled 837 non-neutropenic adults hospitalized with pulmonary infections across three tertiary hospitals in China between December 2020 and December 2025. The discovery cohort was split into training and internal validation sets. Four classifiers were compared, with Random Forest selected for optimal performance. The final model was externally validated in an independent cohort. Feature importance was assessed using SHAP (SHapley Additive exPlanations) analysis. Corticosteroid-bacteria interactions were examined using subgroup analyses and propensity score matching, with the synergy index S calculated to quantify biological interaction. A simplified bedside score was derived from the top-ranking predictors. Results The Random Forest model achieved external validation AUC of 0.865. Six predictors consistently dominated across all assessments: Pseudomonas aeruginosa isolation, hypoxemia (PaO2/FiO2 <200 mmHg), diabetes mellitus, organ failure (Sequential Organ Failure Assessment, SOFA >5), lymphopenia (lymphocyte count <0.8×109/L), and ICU (intensive care unit) admission. Corticosteroid exposure did not rank among the top six predictors. However, its association with IPA diverged sharply by bacterial context: OR below 1 with P. aeruginosa (OR 0.52, 95% CI 0.214–1.283) but OR above 1 with other Gram-negative bacilli (OR 2.17, 95% CI 0.722–6.535); P for interaction = 0.131; a directional pattern that persisted after propensity matching. The six-variable score derived from these predictors showed AUCs of 0.869 (training), 0.774 (internal validation), and 0.719 (external validation). Conclusions IPA risk in non-neutropenic ICU patients is shaped more by bacterial co-pathogen context than by corticosteroid exposure alone. The divergent corticosteroid effects across bacterial species challenge the conventional assumption of a uniform steroid-associated risk. The externally validated six-variable score offers a practical, software-free tool to support pathogen-informed, individualized risk stratification and clinical decision-making in the ICU.