ArticleFrontiers in medicine2025
Machine learning-based prediction model for bronchoalveolar lavage efficacy in severe pneumonia.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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1 citing paper in PubMed, 1 synthesis or guideline pooled it.
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3 authors.
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Abstract
Objective: The objective of the study was to construct and validate a predictive model for the clinical efficacy of bronchoalveolar lavage (BAL) with fiberoptic bronchoscopy in patients with community-acquired severe pneumonia based on inflammatory response indicators and blood gas analysis results. Methods: A total of 206 patients with severe pneumonia who underwent BAL treatment in our hospital from November 2020 to November 2024 were enrolled and randomly divided into a training set ( Results: There were 32 cases (22.22%) and 13 cases (20.97%) with poor clinical efficacy in the training set and the validation set, respectively. In the training set, the multivariate logistic regression analysis showed that age ≥ 60 years, comorbid chronic obstructive pulmonary disease (COPD), procalcitonin (PCT) ≥ 2 ng/mL, C-reactive protein (CRP) ≥ 100 mg/L, partial pressure of arterial oxygen (PaO₂) < 60 mmHg, and partial pressure of arterial carbon dioxide (PaCO₂) ≥ 50 mmHg were independent risk factors for poor efficacy (all Conclusion: An RF-based predictive model was successfully developed and validated to predict the efficacy of BAL treatment in patients with severe pneumonia. This model effectively identifies and quantifies key factors influencing BAL treatment outcomes in severe pneumonia, providing basic data support for subsequent hypothesis testing and clinical efficacy prediction research. However, as this is a single-center, hypothesis-generating study, the application of the model in individualized treatment planning still requires multicenter external validation and further analysis including a non-BAL control group.
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