PhD Scientific Days 2025

Pharmaceutical Sciences and Health Technologies III.

Investigating the Accuracy of Machine Learning Models in Predicting Extubation Success in Mechanically Ventilated Patients - A Systematic Review and Meta-Analysis

Előadó neve

Dr. Bakos Péter

Neptun code

AEKI6Z

Előadó munkahelye

Csolnoky Ferenc Kórház, Veszprém

Előadó telefonszáma

+36207752886

Előadó e-mail címe

ifjbakospeti@gmail.com

Az előadás címe

Investigating the Accuracy of Machine Learning Models in Predicting Extubation Success in Mechanically Ventilated Patients - A Systematic Review and Meta-Analysis

Szerző(k) neve és munkahelye

Péter Bakos¹, Shir Galin¹, Dávid Laczkó¹, Caner Turan¹, Bence Szabó¹, Péter Hegyi¹, András Lovas¹, Zsolt Molnár¹

1: Semmelweis University, Centre for Translational Medicine

Bemutatás módja

Szóbeli

Szekció

Pharmaceutical Sciences and Health Technologies III.

Language of the presentation

English

Preferred session

Pharmaceutical Sciences and Health Technologies

Összefoglaló szövege

Introduction

Optimal timing of extubation in mechanically ventilated patients remains a challenge in intensive care. Both failed and delayed extubation contribute to increased morbidity and mortality. Currently no gold standard exists for predicting succesful extubation. Machine learning (ML) models show promise in outcome prediction, but their application to extubation success lacks standardized validation.

Aims

To systematically review and quantitatively evaluate the performance of ML models in predicting extubation success in critically ill, mechanically ventilated patients.

Methods

We searched PubMed, Embase, and the Cochrane Library for studies involving adult ICU patients undergoing planned extubation, where ML models were used to predict extubation success or failure. Clinical scoring systems were also included for comparison. Performance metrics were meta-analyzed and subgroup analysis was conducted. The review followed Cochrane Handbook methodology. Risk of bias was assessed using modified versions of QUADAS-2 and QUADAS-C, adapted for ML studies.

Results

Twenty-six studies were eligible for systematic review, 43 ML models from 13 studies were included in the meta-analysis. The most commonly reported metric was the area under the ROC curve (AUC). The rapid shallow breathing index (RSBI) was the most frequently reported clinical score.
Model performance varied, with the best AUCs ranging from 0.66 to 0.97. Pooled AUCs by model type were 0.88 (95% CI: 0.77–0.94) for traditional ML models and 0.85 (95% CI: 0.66–0.94) for deep learning models. Tree-based models showed the highest performance (AUC: 0.92, 95% CI: 0.78–0.97), while linear models performed the worst (AUC: 0.66, 95% CI: 0.23–0.93). RSBI alone demonstrated poor predictive value (AUC: 0.58, 95% CI: 0.17–0.90). Study heterogeneity was high, driven by differences in predictor selection and model design.

Conclusion

ML models show good accuracy in predicting extubation success and may support clinical decision-making. Further research is needed to develop generalizable tools suitable for real-world implementation.

Funding

EKÖP-KDP 2024-2028

University

Semmelweis University

Supervisor

Prof. Molnár Zsolt, Dr. Lovas András

Publication of my abstract

I give consent to the publication of my abstract on the website of the congress.

phd.section.field

in doctoral studies before complex exam (PhD)

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

jóváhagyta

Előadó

9159

Start

17:00

End

17:15