Resuscitation
-
The primary aim of this systematic review was to investigate the most common electroencephalogram (EEG)-based machine learning (ML) model with the highest Area Under Receiver Operating Characteristic Curve (AUC) in two ML categories, conventional ML and Deep Neural Network (DNN), to predict the neurologic outcomes after cardiac arrest; the secondary aim was to investigate common EEG features applied to ML models. ⋯ RF and CNN were the two most common ML models with the highest AUCs for predicting the neurologic outcomes after cardiac arrest. Using a multimodal model that combines EEG features and electronic health record data may further improve prognostic performance.
-
Observational Study
Trends in community response and long term outcomes from paediatric cardiac arrest: A retrospective observational study.
This study aimed to investigate trends over time in pre-hospital factors for pediatric out-of-hospital cardiac arrest (pOHCA) and long-term neurological and neuropsychological outcomes. These have not been described before in large populations. ⋯ Long-term favorable neurological outcome, assessed at a median 2.5 years follow-up, improved significantly over the study period. Total IQ scores did not significantly change over time. Furthermore, AED use (OR 1.21, 95%CI 1.10-1.33) and shockable rhythms among adolescents (OR1.15, 95%CI 1.02-1.29) increased over time.
-
Cardiac arrest leaves witnesses, survivors, and their relatives with a multitude of questions. When a young or a public figure is affected, interest around cardiac arrest and cardiopulmonary resuscitation (CPR) increases. ChatGPT allows everyone to obtain human-like responses on any topic. Due to the risks of accessing incorrect information, we assessed ChatGPT accuracy in answering laypeople questions about cardiac arrest and CPR. ⋯ ChatGPT provided largely accurate, relevant, and comprehensive answers to questions about cardiac arrest commonly asked by survivors, their relatives, and lay rescuers, except CPR-related answers that received the lowest scores. Large language model will play a significant role in the future and healthcare-related content generated should be monitored.