Resuscitation
-
Randomized Controlled Trial Observational Study
Combined use of the Montreal Cognitive Assessment and Symbol Digit Modalities Test improves neurocognitive screening accuracy after cardiac arrest: A validation sub-study of the TTM2 trial.
To assess the merit of clinical assessment tools in a neurocognitive screening following out-of-hospital cardiac arrest (OHCA). ⋯ gov Identifier: NCT03543371.
-
In neonates with birth asphyxia (BA) and hypoxic-ischemic encephalopathy, therapeutic hypothermia (TH), initiated within six hours, is the only safe and established neuroprotective measure to prevent secondary brain injury. Infants born outside of TH centers have delayed access to cooling. ⋯ This comprehensive nationwide study found increased odds for adverse outcomes in neonates with BA who were transferred to another facility within 24 h of hospital admission. Closely linking obstetrical units to a pediatric department and balancing geographical coverage of different levels of care facilities might help to minimize risks for postnatal emergency transfer and optimize perinatal care.
-
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.
-
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.