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Detection of Invasive Pulmonary Aspergillosis in Haematological Malignancy Patients by using Lateral-flow Technology
Published on: March 22, 2012
Development and Validation of a Machine Learning-Based Bedside Score (IPA-2T2C2DW) for Invasive Pulmonary
Linna Huang1,2, Ruyi Rong1,2, Xiaoyi Zhou1,3
1National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, P.R. China.
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.
