Predicting neurological outcomes after in-hospital cardiac arrests for patients with Coronavirus Disease 2019. (September 2022)

• Record Type: Journal Article
• Authors: Mayampurath, Anoop; Bashiri, Fereshteh; Hagopian, Raffi; Venable, Laura; Carey, Kyle; Edelson, Dana; Churpek, Matthew
• Is Part Of: Resuscitation. Volume 178(2022)
• Page Start: 55
• Page End: 62
• Publication Date: 2022-09
• Subjects: Cardiac arrest -- Prediction -- Neurological outcomes -- Machine learning; Resuscitation -- Periodicals; Resuscitation -- Periodicals; Réanimation -- Périodiques; Electronic journals; 616.025
• DOI: 10.1016/j.resuscitation.2022.07.018
• Languages: English
• ISSNs: 0300-9572

Abstract

Abstract: Background: Machine learning models are more accurate than standard tools for predicting neurological outcomes in patients resuscitated after cardiac arrest. However, their accuracy in patients with Coronavirus Disease 2019 (COVID-19) is unknown. Therefore, we compared their performance in a cohort of cardiac arrest patients with COVID-19. Methods: We conducted a retrospective analysis of resuscitation survivors in the Get With The Guidelines®-Resuscitation (GWTG-R) COVID-19 registry between February 2020 and May 2021. The primary outcome was a favorable neurological outcome, indicated by a discharge Cerebral Performance Category score ≤ 2. Pre- and peri-arrest variables were used as predictors. We applied our published logistic regression, neural network, and gradient boosted machine models developed in patients without COVID-19 to the COVID-19 cohort. We also updated the neural network model using transfer learning. Performance was compared between models and the Cardiac Arrest Survival Post-Resuscitation In-Hospital (CASPRI) score. Results: Among the 4, 125 patients with COVID-19 included in the analysis, 484 (12 %) patients survived with favorable neurological outcomes. The gradient boosted machine, trained on non-COVID-19 patients was the best performing model for predicting neurological outcomes in COVID-19 patients, significantly better than the CASPRI score (c-statistic: 0.75 vs 0.67, P < 0.001). While calibration improved for the neural network with transfer learning, it did not surpass the gradient boosted machine in terms of discrimination. Conclusion: Our gradient boosted machine model developed in non-COVID patients had high discrimination and adequate calibration in COVID-19 resuscitation survivors and may provide clinicians with important information for these patients.