Resuscitation
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Machine learning models are more accurate than standard tools for predicting neurological outcomes in patients resuscitated after cardiac arrest. However, their accuracy in patients with Coronavirus Disease 2019 (COVID-19) is unknown. Therefore, we compared their performance in a cohort of cardiac arrest patients with COVID-19. ⋯ Our gradient boosted machine model developed in non-COVID patients had high discrimination and adequate calibration in COVID-19 resuscitation survivors and may provide clinicians with important information for these patients.
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While out-of-hospital cardiac arrest (OHCA) is associated with poor survival, early bystander CPR (B-CPR) and telephone CPR (T-CPR) improves survival from OHCA. American Heart Association (AHA) Scientific Statements outline recommendations for T-CPR. We assessed these recommendations and hypothesized that meeting performance standards is associated with increased likelihood of survival. Additional variables were analyzed to identify future performance measurements. ⋯ AHA scientific statements on T-CPR programs serve as ideal starting points for increasing the quality of T-CPR systems and patient outcomes. More work is needed to identify other system performance measures.
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Recent guidelines suggest that coronary angiography (CAG) should be considered for out-of-hospital cardiac arrest (OHCA) survivors, including those without ST elevation (STE) and without shockable rhythms. However, there is no prospective data to support CAG for survivors with nonshockable rhythms and no STE post resuscitation. ⋯ OHCA survivors presenting with nonshockable rhythms and no STE post resuscitation had similar prevalence of culprit coronary lesions to those with shockable rhythms. CAG may be considered in patients with OHCA without STE regardless of initial presenting rhythm. There was no benefit of emergent CAG both in shockable and non-shockable rhythms.