Albert Leng, Benjamin Shou, Olivia Liu, Preetham Bachina, Andrew Kalra, Errol L Bush, Glenn J R Whitman, Sung-Min Cho
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引用次数: 0
Abstract
Background: Neurological complications are common in patients receiving veno-venous extracorporeal membrane oxygenation (VV-ECMO) support. We used machine learning (ML) algorithms to identify predictors for neurological outcomes for these patients.
Methods: All demographic, clinical, and circuit-related variables were extracted for adults with VV-ECMO support at a tertiary care center from 2016 to 2022. The primary outcome was good neurological outcome (GNO) at discharge defined as a modified Rankin Scale of 0-3.
Results: Of 99 total VV-ECMO patients (median age = 48 years; 65% male), 37% had a GNO. The best performing ML model achieved an area under the receiver operating characteristic curve of 0.87. Feature importance analysis identified down-trending gas/sweep/blender flow, FiO2, and pump speed as the most salient features for predicting GNO.
Conclusion: Utilizing pre- as well as post-initiation variables, ML identified on-ECMO physiologic and pulmonary conditions that best predicted neurological outcomes.
期刊介绍：
Lung publishes original articles, reviews and editorials on all aspects of the healthy and diseased lungs, of the airways, and of breathing. Epidemiological, clinical, pathophysiological, biochemical, and pharmacological studies fall within the scope of the journal. Case reports, short communications and technical notes can be accepted if they are of particular interest.
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