Machine learning predicts lung recruitment in acute respiratory distress syndrome using single lung CT scan

Author:

Pennati Francesca,Aliverti Andrea,Pozzi Tommaso,Gattarello Simone,Lombardo Fabio,Coppola Silvia,Chiumello DavideORCID

Abstract

Abstract Background To develop and validate classifier models that could be used to identify patients with a high percentage of potentially recruitable lung from readily available clinical data and from single CT scan quantitative analysis at intensive care unit admission. 221 retrospectively enrolled mechanically ventilated, sedated and paralyzed patients with acute respiratory distress syndrome (ARDS) underwent a PEEP trial at 5 and 15 cmH2O of PEEP and two lung CT scans performed at 5 and 45 cmH2O of airway pressure. Lung recruitability was defined at first as percent change in not aerated tissue between 5 and 45 cmH2O (radiologically defined; recruiters: Δ45-5non-aerated tissue  > 15%) and secondly as change in PaO2 between 5 and 15 cmH2O (gas exchange-defined; recruiters: Δ15-5PaO2  > 24 mmHg). Four machine learning (ML) algorithms were evaluated as classifiers of radiologically defined and gas exchange-defined lung recruiters using different models including different variables, separately or combined, of lung mechanics, gas exchange and CT data. Results ML algorithms based on CT scan data at 5 cmH2O classified radiologically defined lung recruiters with similar AUC as ML based on the combination of lung mechanics, gas exchange and CT data. ML algorithm based on CT scan data classified gas exchange-defined lung recruiters with the highest AUC. Conclusions ML based on a single CT data at 5 cmH2O represented an easy-to-apply tool to classify ARDS patients in recruiters and non-recruiters according to both radiologically defined and gas exchange-defined lung recruitment within the first 48 h from the start of mechanical ventilation.

Funder

Università degli Studi di Milano

Publisher

Springer Science and Business Media LLC

Subject

Critical Care and Intensive Care Medicine

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction;Critical Care Medicine;2024-08-12

2. Monitoring lung recruitment;Current Opinion in Critical Care;2024-03-27

3. Lung Imaging and Artificial Intelligence in ARDS;Journal of Clinical Medicine;2024-01-05

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