Utilizing Machine Learning to Predict Neurological Injury in Venovenous Extracorporeal Membrane Oxygenation Patients: An Extracorporeal Life Support Organization Registry Analysis.
Andrew KalraPreetham BachinaBenjamin L ShouJaeho HwangMeylakh BarshayShreyas KulkarniIsaac SearsCarsten EickhoffChristian A BermudezDaniel BrodieCorey E VentetuoloGlenn J R WhitmanAdeel AbbasiSung-Min ChoPublished in: Research square (2023)
This is the first study to use machine learning to predict ABI in a large cohort of VV-ECMO patients. Performance was sub-optimal due to the low reported prevalence of ABI with lack of standardization of neuromonitoring/imaging protocols and data granularity in the ELSO Registry. Standardized neurological monitoring and imaging protocols may improve machine learning performance to predict ABI.
Keyphrases
- extracorporeal membrane oxygenation
- machine learning
- acute respiratory distress syndrome
- end stage renal disease
- ejection fraction
- newly diagnosed
- high resolution
- prognostic factors
- artificial intelligence
- respiratory failure
- risk factors
- patient reported outcomes
- mass spectrometry
- electronic health record
- photodynamic therapy
- mechanical ventilation