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Predicting Acute Brain Injury in Venoarterial Extracorporeal Membrane Oxygenation Patients with Tree-Based Machine Learning: Analysis of the Extracorporeal Life Support Organization Registry.
Kalra, Andrew; Bachina, Preetham; Shou, Benjamin L; Hwang, Jaeho; Barshay, Meylakh; Kulkarni, Shreyas; Sears, Isaac; Eickhoff, Carsten; Bermudez, Christian A; Brodie, Daniel; Ventetuolo, Corey E; Kim, Bo Soo; Whitman, Glenn J R; Abbasi, Adeel; Cho, Sung-Min.
Afiliação
  • Kalra A; Johns Hopkins University School of Medicine.
  • Bachina P; Johns Hopkins University School of Medicine.
  • Shou BL; Johns Hopkins University School of Medicine.
  • Hwang J; Johns Hopkins University School of Medicine.
  • Barshay M; Warren Alpert Medical School of Brown University.
  • Kulkarni S; Warren Alpert Medical School of Brown University.
  • Sears I; Warren Alpert Medical School of Brown University.
  • Eickhoff C; University of Tübingen.
  • Bermudez CA; Perelman School of Medicine at the University of Pennsylvania, Philadelphia.
  • Brodie D; Johns Hopkins University School of Medicine.
  • Ventetuolo CE; Warren Alpert Medical School of Brown University.
  • Kim BS; Johns Hopkins University School of Medicine.
  • Whitman GJR; Johns Hopkins University School of Medicine.
  • Abbasi A; Warren Alpert Medical School of Brown University.
  • Cho SM; Johns Hopkins University School of Medicine.
Res Sq ; 2024 Jan 11.
Article em En | MEDLINE | ID: mdl-38260374
ABSTRACT

Objective:

To determine if machine learning (ML) can predict acute brain injury (ABI) and identify modifiable risk factors for ABI in venoarterial extracorporeal membrane oxygenation (VA-ECMO) patients.

Design:

Retrospective cohort study of the Extracorporeal Life Support Organization (ELSO) Registry (2009-2021).

Setting:

International, multicenter registry study of 676 ECMO centers. Patients Adults (≥18 years) supported with VA-ECMO or extracorporeal cardiopulmonary resuscitation (ECPR).

Interventions:

None. Measurements and Main

Results:

Our primary outcome was ABI central nervous system (CNS) ischemia, intracranial hemorrhage (ICH), brain death, and seizures. We utilized Random Forest, CatBoost, LightGBM and XGBoost ML algorithms (10-fold leave-one-out cross-validation) to predict and identify features most important for ABI. We extracted 65 total features demographics, pre-ECMO/on-ECMO laboratory values, and pre-ECMO/on-ECMO settings.Of 35,855 VA-ECMO (non-ECPR) patients (median age=57.8 years, 66% male), 7.7% (n=2,769) experienced ABI. In VA-ECMO (non-ECPR), the area under the receiver-operator characteristics curves (AUC-ROC) to predict ABI, CNS ischemia, and ICH was 0.67, 0.67, and 0.62, respectively. The true positive, true negative, false positive, false negative, positive, and negative predictive values were 33%, 88%, 12%, 67%, 18%, and 94%, respectively for ABI. Longer ECMO duration, higher 24h ECMO pump flow, and higher on-ECMO PaO2 were associated with ABI.Of 10,775 ECPR patients (median age=57.1 years, 68% male), 16.5% (n=1,787) experienced ABI. The AUC-ROC for ABI, CNS ischemia, and ICH was 0.72, 0.73, and 0.69, respectively. The true positive, true negative, false positive, false negative, positive, and negative predictive values were 61%, 70%, 30%, 39%, 29% and 90%, respectively, for ABI. Longer ECMO duration, younger age, and higher 24h ECMO pump flow were associated with ABI.

Conclusions:

This is the largest study predicting neurological complications on sufficiently powered international ECMO cohorts. Longer ECMO duration and higher 24h pump flow were associated with ABI in both non-ECPR and ECPR VA-ECMO.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article