SynthesisPulmonary medicine2026
Optimizing Intubation Prediction in Pneumonia Patients: A Systematic Review and Meta-Analysis of Machine Learning Algorithms.
Synthesis in Pulmonary medicine, 2026. 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.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Optimizing Intubation Prediction in Pneumonia Patients: A Systematic Review and Meta-Analysis of Machine Learning Algorithms.Pulmonary medicine · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPneumonia, 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.
methodsWe 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.
resultsThis 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 (I
conclusionML algorithms demonstrate a modest and highly variable accuracy in predicting the need for endotracheal intubation among pneumonia patients. High heterogeneity and methodological variability highlight the need for standardized ML approaches before clinical adoption.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.