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Pneumonia III: Complications and Assessment01:30

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Pneumonia V: Nursing management and Prevention01:30

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Optimizing Intubation Prediction in Pneumonia Patients: A Systematic Review and Meta-Analysis of Machine Learning

Elham Abdoli1, Pooya Eini1, Sajjad Farashi2

  • 1Infectious Disease Research Center, Hamadan University of Medical Sciences, Hamadan, Iran, umsha.ac.ir.

Pulmonary Medicine
|March 18, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models show moderate accuracy in predicting endotracheal intubation for pneumonia patients. Further standardization is needed before clinical use due to high variability and heterogeneity.

Keywords:
COVID-19endotracheal intubationinfluenzamachine learningpneumonia

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Pulmonology

Background:

  • Pneumonia poses a significant global health threat, causing high mortality and frequent respiratory failure requiring intubation.
  • Early identification of patients needing endotracheal intubation is crucial for improved outcomes and resource allocation.
  • Current prognostic tools for predicting intubation in pneumonia patients have limitations.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of machine learning (ML) algorithms in predicting endotracheal intubation in pneumonia patients.
  • To assess the performance of ML models using systematic review and meta-analysis.

Main Methods:

  • Systematic search of five databases for studies on ML models predicting endotracheal intubation in pneumonia.
  • Calculation of pooled estimates for area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.
  • Assessment of risk of bias using PROBAST+AI and certainty of evidence with GRADE.

Main Results:

  • The meta-analysis included 195,214 pneumonia patients from 34 studies, with 26 contributing to the meta-analysis.
  • Pooled AUROC was 0.79, indicating moderate diagnostic accuracy, but substantial heterogeneity (I² > 90%) was observed.
  • Risk of bias was higher in application domains, and evidence certainty was rated as moderate to low.

Conclusions:

  • Machine learning algorithms demonstrate modest and highly variable accuracy in predicting the need for endotracheal intubation in pneumonia patients.
  • Significant heterogeneity and methodological variability underscore the need for standardized ML approaches.
  • Further research and standardization are required before widespread clinical adoption of ML for this purpose.