Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023

The George B. Moody PhysioNet Challenge 2023 invited teams to develop algorithmic approaches for predicting the recovery of comatose patients after cardiac arrest. A patient's prognosis after the return of spontaneous circulation informs treatment, including the continuation or withdrawal of life support. Brain monitoring with an electroencephalogram (EEG) can improve the objectivity of a prognosis, but EEG interpretation requires clinical expertise. The algorithmic analysis of EEGs can potentially improve the accuracy and accessibility of prognoses, but existing work is limited by small and homogeneous datasets. The PhysioNet Challenge 2023 contributed to addressing these problems. It introduced the International Cardiac Arrest REsearch consortium (I-CARE) dataset, which is a large, multi-center collection of EEGs, other physiological data, and clinical outcomes, with over 57,000 hours of data from 1,020 patients from seven hospitals. It required teams to submit their complete training and inference code to improve the reproducibility and generalizability of their research. A total of 111 teams participated in the Challenge, contributing diverse approaches from academic, clinical, and industry participants worldwide.

[1]  MD Edilberto Amorim,et al.  The International Cardiac Arrest Research Consortium Electroencephalography Database , 2023, Critical care medicine.

[2]  MD Edilberto Amorim,et al.  The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database , 2023, medRxiv.

[3]  Jimeng Sun,et al.  Predicting Neurological Outcome in Comatose Patients after Cardiac Arrest with Multiscale Deep Neural Networks. , 2021, Resuscitation.

[4]  Hiba A. Haider,et al.  American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology: 2021 Version. , 2021, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[5]  B. Healy,et al.  The impact of false positive COVID-19 results in an area of low prevalence. , 2020, Clinical medicine.

[6]  M. V. van Putten,et al.  Early electroencephalography for outcome prediction of postanoxic coma: A prospective cohort study , 2019, Annals of neurology.

[7]  C. Callaway,et al.  Continuous EEG monitoring enhances multimodal outcome prediction in hypoxic-ischemic brain injury. , 2016, Resuscitation.

[8]  M. V. van Putten,et al.  Early EEG contributes to multimodal outcome prediction of postanoxic coma , 2015, Neurology.

[9]  I. Rosén,et al.  Continuous amplitude-integrated electroencephalogram predicts outcome in hypothermia-treated cardiac arrest patients , 2010, Critical care medicine.

[10]  B. Jennett,et al.  ASSESSMENT OF OUTCOME AFTER SEVERE BRAIN DAMAGE A Practical Scale , 1975, The Lancet.

[11]  E. Wagenmakers,et al.  UvA-DARE ( Digital Academic Repository ) Detecting and avoiding likely false-positive findings , 2017 .