Resuscitation
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Magnetic Resonance Imaging (MRI) is an important prognostic tool in cardiac arrest (CA) survivors given its sensitivity for detecting hypoxic-ischemic brain injury (HIBI), however, it is limited by poorly defined objective thresholds. To address this limitation, we evaluated a qualitative MRI score for predicting neurological outcome in CA survivors. ⋯ A simplified, qualitative MRI score had excellent reliability and good discrimination for poor neurologic outcome. Further work is necessary to externally validate our findings in an independent, ideally prospective, cohort.
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Cognitive function is often impaired for cardiac arrest (CA) survivors due to hypoxic-ischemic brain injury. Whether cognitive impairment at hospital discharge is associated with recovery defined as functional status and fatigue measured at 1-month post-discharge is not known. ⋯ Cognitive function at discharge after CA was significantly and independently associated with functional outcome 1 month after hospital discharge. Psychological distress contributed to fatigue severity. This highlights the need for screening and addressing cognitive and emotional problems pre-hospital discharge.
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Patients experiencing cardiac arrest are often burdened with comorbidities that increase mortality. This study examined the impact of comorbidity burden on cardiac arrest mortality by quantifying biological interaction. ⋯ Comorbidity burden interacted with cardiac arrest to increase mortality beyond that explained by their separate effects.
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During out-of-hospital cardiac arrest (OHCA), an automatic external defibrillator (AED) analyzes the cardiac rhythm every two minutes; however, 80% of refibrillations occur within the first minute post-shock. We have implemented an algorithm for Analyzing cardiac rhythm While performing chest Compression (AWC). When AWC detects a shockable rhythm, it shortens the time between analyses to one minute. We investigated the effect of AWC on cardiopulmonary resuscitation quality. ⋯ OHCA patients treated with AWC had higher CCF, shorter time spent in ventricular fibrillation, but no survival difference, except for OHCA that occurred in public places with short intervention time.
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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.