Resuscitation
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The Utstein Based-ROSC (UB-ROSC) score has been developed to predict ROSC in OHCA victims. Aim of the study was to validate the UB-ROSC score using two Utstein-based OHCA registries: the SWiss REgistry of Cardiac Arrest (SWISSRECA) and the Lombardia Cardiac Arrest Registry (Lombardia CARe), northern Italy. ⋯ UB-ROSC score represents a reliable tool to predict ROSC probability in OHCA patients. Its application may help the medical decision-making process, providing a realistic stratification of the probability for ROSC.
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Extracorporeal cardiopulmonary resuscitation (ECPR) for selected refractory out-of-hospital cardiac arrest (OHCA) is increasingly used. Detailed knowledge of health-related quality of life (HRQoL) and long-term cognitive function is limited. HRQoL and cognitive function were assessed in ECPR-survivors and OHCA-survivors with prehospital return of spontaneous circulation after standard advanced cardiac life support (sACLS). ⋯ Despite substantially longer low flow times with thrice as high lactate levels, ECPR-survivors were similar in cognitive and physical function compared to sACLS-survivors. Nonetheless, ECPR-survivors reported lower HRQoL overall and related to mental health, pain management, and the perception of limitations in physical role.
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Prehospital identification of futile resuscitation efforts (defined as a predicted probability of survival lower than 1%) for out-of-hospital cardiac arrest (OHCA) may reduce unnecessary transport. Reliable prediction variables for OHCA 'termination of resuscitation' (TOR) rules are needed to guide treatment decisions. The Universal TOR rule uses only three variables (Absence of Prehospital ROSC, Event not witnessed by EMS and no shock delivered on the scene) has been externally validated and is used by many EMS systems. Deep learning, an artificial intelligence (AI) platform is an attractive model to guide the development of TOR rule for OHCA. The purpose of this study was to assess the feasibility of developing an AI-TOR rule for neurologically favorable outcomes using general purpose AI and compare its performance to the Universal TOR rule. ⋯ The accuracy of prediction models using AI software to determine outcomes in OHCA was excellent and the AI-TOR rule's variables from prediction model performed better than the Universal TOR rule. External validation of our findings as well as further research into the utility of using AI platforms for TOR prediction in clinical practice is needed.
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Long-term cognitive decline after out-of-hospital cardiac arrest (OHCA) is still poorly understood. This study describes long-term observer-reported cognitive decline among Danish OHCA survivors, including differences in years since the event, and investigates characteristics and self-reported outcomes associated with observer-reported cognitive decline. ⋯ Nearly half of OHCA survivors may suffer long-term cognitive decline. Worse self-reported mental and physical outcomes among survivors and their relatives are associated with potential cognitive decline emphasising the need for post-OHCA care to include systematic neurocognitive assessment, tailored support and effective rehabilitation.