Machine Learning from Veno-Venous Extracorporeal Membrane Oxygenation Identifies Factors Associated with Neurological Outcomes.
Lung
; 202(4): 465-470, 2024 Aug.
Article
in En
| MEDLINE
| ID: mdl-38814448
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Extracorporeal Membrane Oxygenation
/
Machine Learning
Limits:
Adult
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Female
/
Humans
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Male
/
Middle aged
Language:
En
Journal:
Lung
Year:
2024
Type:
Article
Affiliation country:
United States