Updated information on neuro-prognosticative tools to predict outcomes for patients with hypoxic-ischemic encephalopathy induced by cardiac arrest

Abstract

Hypoxic-ischemic encephalopathy (HIE), caused by cardiac arrest (CA) is a refractory condition in clinical settings. The clinician and family members have to make a hard decision: continue expensive life-sustaining therapy or withdraw the expensive intervention. The core problem lies in "whether this patient can still be awakened and achieve neurological recovery". This study briefly summarizes the use of mainstream neuro-prognosticative tools thus far with the latest available evidence. To gain a better understanding of the pathophysiological state of patients with HIE, comprehensive use of these tools and repeated assessments are recommended. The final decision should be made cautiously and comprehensively in light of the patient's medical history, pathophysiological state, results of neuro-prognosticative evaluations, and the clinician's clinical experience per se. Novel computerized technologies such as artificial intelligence, big data, and machine learning should be used to develop neuro-prognosticative tools for refractory CA-induced HIE.