Optimizing Intubation Prediction in Pneumonia Patients: A Systematic Review and Meta‐Analysis of Machine Learning Algorithms
Abstract
Background Pneumonia, including influenza, COVID‐19, and community‐acquired pneumonia, is a major global health burden associated with high morbidity, mortality, and frequent progression to respiratory failure requiring intubation. Early identification of patients at risk of endotracheal intubation is essential to improve outcomes and optimize ICU resource allocation, yet existing prognostic tools remain limited in predicting this need. This study evaluated the performance of machine learning (ML) algorithms in predicting endotracheal intubation among patients with pneumonia during hospital stay. Methods We systematically searched five databases to evaluate the diagnostic accuracy of ML models. Pooled estimates of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity were calculated. Subgroup analysis and meta‐regression were conducted. Risk of bias was assessed using PROBAST+AI and certainty of evidence with GRADE. Results This systematic review of 34 studies (26 in meta‐analysis) included 195,214 pneumonia patients. The pooled AUROC was 0.79 (95% CI: 0.75–0.82), with sensitivity of 0.74 (95% CI: 0.61–0.84), specificity of 0.71 (95% CI: 0.50–0.86), and a DOR of 7 (95% CI: 2–20), indicating moderate diagnostic accuracy. Heterogeneity was substantial across analyses (I2 = 90.45% for sensitivity and 94.58% for specificity). Risk of bias was lowest in development (59%) and highest in application domains (41% high risk). Despite a…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 195,214 participants or records.
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