• Sung Jin Bae, Yoon Hee Choi, Seok Jin Ryu, Dong Hun Lee, Yunhyung Choi, Minsoo Chun, Youngwoo Kim, and Dong Hoon Lee.
• Department of Emergency Medicine, Chung-Ang University Gwangmyeong Hospital, College of Medicine, Seoul, Chung-Ang University, 110, Deokan-ro, Gwangmyeong-si, Gyeonggi-do, Republic of Korea.
• Am J Emerg Med. 2024 Nov 6; 87: 123129123-129.

BackgroundPost-cardiac arrest care advancements have improved resuscitation outcomes, but many survivors still face severe neurological deficits or death from brain injury. Herein, we propose a consistent prognosis prediction approach using magnetic resonance imaging (MRI) to analyze anatomical regions represented by the gray and white matter, and subsequently apply it on computed tomography (CT) to calculate the gray-white matter ratio (GWR). We compared this novel method with traditional measures to validate its ability to predict the prognosis of patients resuscitated after cardiac arrest.MethodsConducted retrospectively at two South Korean tertiary university hospitals from January 2018 to December 2022, the study included adult cardiac arrest survivors treated with therapeutic target temperature management. Patients underwent brain CT within 2 h and brain MRI within 3 days of return of spontaneous circulation. The outcome was the neurological status at discharge. Statistical analyses included receiver operating characteristic curve analysis and determining cutoff values to predict poor neurological outcomes.ResultsOverall, 51 of the 421 adult comatose cardiac arrest survivors examined met the inclusion criteria. Among these, 33 and 18 exhibited good and poor neurological outcomes, respectively. Demographic and cardiac arrest characteristics were compared between the two groups, revealing significant differences. Analyses of gray and white matter attenuation and GWR measurements highlighted significant differences between the good and poor outcome groups.ConclusionOur study introduces a novel method for measuring GWR using MRI-based brain CT, demonstrating superior prognostic accuracy in predicting neurological outcomes in patients with post-cardiac arrest syndrome compared to traditional methods.Copyright © 2024 Elsevier Inc. All rights reserved.

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