ArticleInfection and drug resistance2025
A Predictive Model for Pulmonary Aspergillosis in ICU Patients: A Multicenter Retrospective Cohort Study.
Article in Infection and drug resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A machine learning-based risk prediction tool for early identification of invasive pulmonary aspergillosis in immunocompetent patients: a systematic review and meta-analysis.Journal of thoracic disease · 2026Article
- Immune monitoring, immune phenotyping, and precision intervention in severe pneumonia: current evidence and translational challenges.Frontiers in immunology · 2026Review
- Distinct pathogen spectrum in immunocompromised patients with severe pneumonia.Critical care (London, England) · 2025Article
- The Landscape of lower respiratory tract herpesviruses in severe pneumonia patients: a multicenter, retrospective study with prospective validation.Critical care (London, England) · 2025Article
- Development of a Predictive Risk Model for Recurrence of Chronic Pulmonary Aspergillosis in Post-Tuberculosis Patients: A Retrospective Observational Study.International journal of general medicine · 2025Article
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Authors and funding
25 authors.
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Abstract
Background: Several predictive models for invasive pulmonary aspergillosis (IPA) based on clinical characteristics have been reported. Nevertheless, the significance of other concurrently detected microorganisms in IPA patients is equally noteworthy. This study aimed to develop a risk prediction model for IPA by integrating clinical and microbiological characteristics. Methods: This retrospective study was conducted in adult intensive care units (ICUs) of 17 medical centers in China. Clinical data were collected from patients with severe pneumonia who underwent clinical metagenomics of bronchoalveolar lavage fluid between January 1, 2019, and June 30, 2023. Subsequently, patients were randomly assigned to training and validation cohorts in a 7:3 ratio. In the training cohort, potential influencing factors were identified through univariate analysis, clinical practice, and existing literature, and a risk prediction model was constructed using multivariate logistic regression analysis. The performance of this model was then assessed and validated in the validation cohort. Results: Out of 1737 patients initially included in the study, 898 were ultimately analyzed, of which 100 (11%) were diagnosed with IPA. The risk prediction model for IPA, incorporating microbiological characteristics, identified six independent risk factors, namely age, immunosuppression, chronic kidney disease, connective tissue disease, liver failure, and cytomegalovirus positivity. The model demonstrated a superior discriminative ability, with area under the curve (AUC) values of 0.791 and 0.792 in the training and validation cohorts, respectively. Sensitivity and specificity reached 73.1% and 74.9%, respectively, and the model demonstrated good calibration. Conclusion: This study developed a novel risk prediction model for IPA incorporating microbiological characteristics based on clinical metagenomics. The model exhibited good discriminative ability and calibration.
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