Resuscitation
-
While intravenous (IV) vascular access for out-of-hospital cardiac arrest (OHCA) resuscitation is standard, humeral-intraosseous (IO) access is commonly used, despite few supporting data. We investigated the association between IV vs. humeral-IO and outcomes. ⋯ An IV-first approach, compared to humeral-IO, for intra-arrest resuscitation was associated with an improved odds of favorable neurological outcomes and survival to hospital discharge. This association was seen among an initial shockable rhythm, but not non-shockable rhythm, subgroups.
-
Understanding the impact of social determinants of health (SDOH) on CA, including access to care pre-cardiac arrest (CA) can improve outcomes. Large databases, such as Epic Cosmos, can help identify trends in patient demographics and SDOH that identify gaps in care. The purpose of this study was to determine the incidence of CA and subsequent mortality in a large national database across patient demographics and social determinants and characterize pre-arrest care patterns. ⋯ SDOH have a significant impact on the risk of CA, pre-arrest care patterns, and post-arrest mortality. Determining the impact that SDOH have on the CA care continuum provides can provide actionable targets to prevent CA and subsequent mortality.
-
This study aimed to predict blood pressure during CPR using chest compression waveform information obtained from a CPR feedback device. ⋯ Blood pressure generated by chest compressions can be predicted with high accuracy by a machine learning method using chest compression waveform information obtained from a CPR feedback device and the patient's demographic characteristics. Real-time provision of the predicted blood pressure can be used to monitor the quality and efficacy of CPR.
-
Observational Study
Artificial intelligence for predicting shockable rhythm during cardiopulmonary resuscitation: In-hospital setting.
This study aimed to develop an artificial intelligence (AI) model capable of predicting shockable rhythms from electrocardiograms (ECGs) with compression artifacts using real-world data from emergency department (ED) settings. Additionally, we aimed to explore the black box nature of AI models, providing explainability. ⋯ This study was the first to accurately predict shockable rhythms during compression using an AI model trained with actual patient ECGs recorded during resuscitation. Furthermore, we demonstrated the explainability of the AI. This model can minimize interruption of cardiopulmonary resuscitation and potentially lead to improved outcomes.
-
Historically in Singapore, all out-of-hospital cardiac arrests (OHCA) were transported to hospital for pronouncement of death. A 'Termination of Resuscitation' (TOR) protocol, implemented from 2019 onwards, enables emergency responders to pronounce death at-scene in Singapore. This study aims to evaluate the cost-effectiveness of the TOR protocol for OHCA management. ⋯ The application of the TOR protocol for the management of OHCA was found to be cost-effective within acceptable willingness-to-pay thresholds, providing some justification for sustainable adoption.