Prediction of neurological outcomes following the return of spontaneous circulation in patients with out-of-hospital cardiac arrest: Retrospective fast-and-frugal tree analysis. (December 2018)

• Record Type: Journal Article
• Authors: Shin, So Mi; Kim, Kyung Su; Suh, Gil Joon; Kim, Kyuseok; Kwon, Woon Yong; Shin, Jonghwan; Jo, You Hwan; Lee, Jae Hyuk; Lee, Huijai; Kim, Joonghee; Jung, Yoon Sun; Kim, Taegyun; You, Kyoung Min
• Is Part Of: Resuscitation. Volume 133(2018)
• Page Start: 65
• Page End: 70
• Publication Date: 2018-12
• Subjects: Cardiac arrest -- Prognosis -- Outcome; Resuscitation -- Periodicals; Resuscitation -- Periodicals; Réanimation -- Périodiques; Electronic journals; 616.025
• DOI: 10.1016/j.resuscitation.2018.10.002
• Languages: English
• ISSNs: 0300-9572

Abstract

Abstract: Aim: Although various quantitative methods have been developed for predicting neurological prognosis in patients with out-of-hospital cardiac arrest (OHCA), they are too complex for use in clinical practice. We aimed to develop a simple decision rule for predicting neurological outcomes following the return of spontaneous circulation (ROSC) in patients with OHCA using fast-and-frugal tree (FFT) analysis. Methods: We performed a retrospective analysis of prospectively collected data archived in a multi-centre registry. Good neurological outcomes were defined as cerebral performance category (CPC) values of 1 or 2 at 28-day. Variables used for FFT analysis included age, sex, witnessed cardiac arrest, bystander cardiopulmonary resuscitation, initial shockable rhythm, prehospital defibrillation, prehospital ROSC, no flow time, low flow time, cause of arrest (cardiac or non-cardiac), pupillary light reflex, and Glasgow Coma Scale score after ROSC. Results: Among the 456 patients enrolled, 86 (18.9%) experienced good neurological outcomes. Prehospital ROSC (true = good), prompt or sluggish light reflex response after ROSC (true = good), and presumed cardiac cause (true = good, false = poor) were selected as nodes for the decision tree. Sensitivity, specificity, positive predictive value, and negative predictive value of the decision tree for predicting good neurological outcomes were 100% (42/42), 64.0% (119/186), 38.5% (42/109), and 100% (119/119) in the training set and 95.5% (42/44), 57.6% (106/184), 35.0% (42/120), and 98.1% (106/108) in the test set, respectively. Conclusion: A simple decision rule developed via FFT analysis can aid clinicians in predicting neurological outcomes following ROSC in patients with OHCA.