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

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: Δ_45-5non-aerated tissue  > 15%) and secondly as change in PaO₂ between 5 and 15

SUBMITTER: Pennati F 

PROVIDER: S-EPMC10322807 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature