Predicting Acute Brain Injury in Venoarterial Extracorporeal Membrane Oxygenation Patients with Tree-Based Machine Learning: Analysis of the Extracorporeal Life Support Organization Registry.
| Autor: | 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. |
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| Jazyk: | angličtina |
| Zdroj: | Research square [Res Sq] 2024 Jan 11. Date of Electronic Publication: 2024 Jan 11. |
| DOI: | 10.21203/rs.3.rs-3848514/v1 |
| Abstrakt: | 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 PaO 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. Competing Interests: Financial/nonfinancial disclosures: Dr. Brodie receives research support from and consults for LivaNova. He has been on the medical advisory boards for Xenios, Medtronic, Inspira and Cellenkos. He is the President-elect of the Extracorporeal Life Support Organization (ELSO) and the Chair of the Executive Committee of the International ECMO Network (ECMONet), and he writes for UpToDate. Dr. Ventetuolo has been a consultant or served on advisory boards for Merck, Janssen, and Regeneron, outside of the submitted work. The authors do not have any additional conflicts of interest to declare. SMC is supported by NHLBI (1K23HL157610) and Hyperfine (SAFE MRI ECMO study). Additional Declarations: The authors declare no competing interests. |
| Databáze: | MEDLINE |
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