Development and Validation of a Machine Learning-Based Bedside Score (IPA-2T2C2DW) for Invasive Pulmonary Aspergillosis in Critically Ill Non-Neutropenic Immunocompromised Hosts.

OBJECTIVE

To develop and validate machine learning-based diagnostic models for IPA using data available within 24 hours of ICU admission, construct three model versions for different healthcare settings, and derive a bedside score for rapid risk stratification.

METHODS

This prospective multi-center cohort study enrolled immunocompromised patients with pneumonia complicated by respiratory failure admitted to the ICU at China-Japan Friendship Hospital (formed the derivation cohort) and 15 additional centers nationwide (comprised the external validation cohort).

RESULTS

The derivation cohort included 508 patients (121 IPA) and the external validation cohort 229 patients (53 IPA). Nine independent predictors were identified: CD4+ T-cell count (T), characteristic CT findings (C), dry rales (D), D-dimer, Temperature above 38°C (T), WBC (W), 7-day cumulative corticosteroid dose (C), diabetes mellitus (D), and lymphocyte count. We tested three hierarchical models with six machine learning algorithms. In internal validation, logistic regression showed the best discrimination (AUC 0.915, 95% CI 0.850-0.980) and calibration (Brier score 0.094) for the completed model. In external validation, CatBoost achieved an AUC of 0.844 (0.760-0.928), and logistic regression remained robust in simplified (AUC 0.863) and minimal versions (AUC 0.791). The IPA-2T2C2DW score stratified patients into low- (≤7 points), intermediate- (8-9 points), and high-risk (≥10 points) groups, with observed IPA rates of 8.2%, 28.6%, and 62.4%, respectively.

CONCLUSIONS

The IPA-2T2C2DW score enables rapid bedside IPA risk stratification to guide monitoring intensity and empirical antifungal therapy across diverse healthcare settings. Prospective validation in unselected cohorts is warranted before clinical implementation.