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Preliminary Prognostication for Good Neurological Outcomes in the Early Stage of Post-Cardiac Arrest Care.

Authors :
Lee, Sunghyuk
Park, Jung Soo
You, Yeonho
Min, Jin Hong
Jeong, Wonjoon
Ahn, Hong Joon
In, Yong Nam
Cho, Yong Chul
Lee, In Ho
Lee, Jae Kwang
Kang, Changshin
Source :
Diagnostics (2075-4418); Jul2023, Vol. 13 Issue 13, p2174, 11p
Publication Year :
2023

Abstract

We investigated prognostic strategies for predicting good outcomes in the early stage of post-cardiac-arrest care using multiple prognostic tests that are available until 24 h after the return of spontaneous circulation (ROSC). A retrospective analysis was conducted on 138 out-of-hospital cardiac-arrest patients who underwent prognostic tests, including the gray–white-matter ratio (GWR-BG), the Glasgow Coma Scale motor (GCS-M) score before sedative administration, and the neuron-specific enolase (NSE) level measured at 24 h after the ROSC. We investigated the prognostic performances of the tests as single predictors and in various combination strategies. Classification and regression-tree analysis were used to provide a reliable model for the risk stratification. Out of all the patients, 55 (44.0%) had good outcomes. The NSE level showed the highest prognostic performance as a single prognostic test and provided improved specificities (>70%) and sensitivities (>98%) when used in combination strategies. Low NSE levels (≤32.1 ng/mL) and high GCS-M (≥4) scores identified good outcomes without misclassification. The overall accuracy for good outcomes was 81.8%. In comatose patients with low NSE levels or high GCS-M scores, the premature withdrawal of life-sustaining therapy should be avoided, thereby complying with the formal prognostication-strategy algorithm after at least 72 h from the ROSC. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20754418
Volume :
13
Issue :
13
Database :
Complementary Index
Journal :
Diagnostics (2075-4418)
Publication Type :
Academic Journal
Accession number :
164925901
Full Text :
https://doi.org/10.3390/diagnostics13132174