Incidence and Long-Term Outcomes of Acute Myocardial Infarction Among Survivors of Out-of-Hospital Cardiac Arrest.

BACKGROUND: Despite the increasing long-term survival after out-of-hospital cardiac arrest (OHCA), the risk of subsequent acute myocardial infarction (AMI) remains poorly understood. We aimed to determine the incidence, predictors, and long-term outcomes of AMI among survivors of OHCA. METHODS AND RESULTS: We assembled a retrospective cohort of 882 patients with OHCA who survived to 30 days or discharge from the hospital between 2010 and 2019. Survivors of OHCA had an increased risk of subsequent AMI, defined as AMI occurring 30 days after index OHCA or following discharge from the hospital after OHCA, compared with the general population when matched for age and sex (standardized incidence ratio, 4.64 [95% CI, 3.52-6.01]). Age-specific risks of subsequent AMI for men (standardized incidence ratio, 3.29 [95% CI, 2.39-4.42]) and women (standardized incidence ratio, 6.15 [95% CI, 3.27-10.52]) were significantly increased. A total of 7.2%, 8.3%, and 14.3% of survivors of OHCA had a subsequent AMI at 3 years, 5 years, and end of follow-up, respectively. Age at OHCA (hazard ratio [HR], 1.04 [95% CI, 1.02-1.06]) and past medical history of prior AMI, defined as any AMI preceding or during the index OHCA event (HR, 1.84 [95% CI, 1.05-3.22]), were associated with subsequent AMI, while an initial shockable rhythm was not (HR, 1.00 [95% CI, 0.52-1.94]). Survivors of OHCA with subsequent AMI had a higher risk of death (HR, 1.58 [95% CI, 1.12-2.22]) than those without. CONCLUSIONS: Survivors of OHCA are at an increased risk of subsequent AMI compared with the general population. Prior AMI, but not an initial shockable rhythm, increases this risk, while subsequent AMI predicts death. Preventive measures for AMI including cardiovascular risk factor control and revascularization may thus improve outcomes in selected patients with cardiac pathogenesis.

Long-Term Outcomes after Non-Traumatic Out-of-Hospital Cardiac Arrest in Pediatric Patients: A Systematic Review.

Long-term outcomes after non-traumatic pediatric out-of-hospital cardiac arrest (OHCA) are not well understood. This systematic review aimed to summarize long-term outcomes (1 year and beyond), including overall survival, survival with favorable neurological outcomes, and health-related quality of life (HRQoL) outcomes) amongst pediatric OHCA patients who survived to discharge. Embase, Medline, and The Cochrane Library were searched from inception to October 6, 2021. Studies were included if they reported outcomes at 1 year or beyond after pediatric OHCA. Data abstraction and quality assessment was conducted by three authors independently. Qualitative outcomes were reported systematically. Seven studies were included, and amongst patients that survived to hospital discharge or to 30 days, longer-term survival was at least 95% at 24 months of follow up. A highly variable proportion (range 10-71%) of patients had favorable neurological outcomes at 24 months of follow up. With regard to health-related quality of life outcomes, at a time point distal to 1 year, at least 60% of pediatric non-traumatic OHCA patients were reported to have good outcomes. Our study found that at least 95% of pediatric OHCA patients, who survived to discharge, survived to a time point distal to 1 year. There is a general paucity of data surrounding the pediatric OHCA population.

Impact of Cardiac Arrest Centers on the Survival of Patients With Nontraumatic Out-of-Hospital Cardiac Arrest: A Systematic Review and Meta-Analysis.

Background The role of cardiac arrest centers (CACs) in out-of-hospital cardiac arrest care systems is continuously evolving. Interpretation of existing literature is limited by heterogeneity in CAC characteristics and types of patients transported to CACs. This study assesses the impact of CACs on survival in out-of-hospital cardiac arrest according to varying definitions of CAC and prespecified subgroups. Methods and Results Electronic databases were searched from inception to March 9, 2021 for relevant studies. Centers were considered CACs if self-declared by study authors and capable of relevant interventions. Main outcomes were survival and neurologically favorable survival at hospital discharge or 30 days. Meta-analyses were performed for adjusted odds ratio (aOR) and crude odds ratios. Thirty-six studies were analyzed. Survival with favorable neurological outcome significantly improved with treatment at CACs (aOR, 1.85 [95% CI, 1.52-2.26]), even when including high-volume centers (aOR, 1.50 [95% CI, 1.18-1.91]) or including improved-care centers (aOR, 2.13 [95% CI, 1.75-2.59]) as CACs. Survival significantly increased with treatment at CACs (aOR, 1.92 [95% CI, 1.59-2.32]), even when including high-volume centers (aOR, 1.74 [95% CI, 1.38-2.18]) or when including improved-care centers (aOR, 1.97 [95% CI, 1.71-2.26]) as CACs. The treatment effect was more pronounced among patients with shockable rhythm (P=0.006) and without prehospital return of spontaneous circulation (P=0.005). Conclusions were robust to sensitivity analyses, with no publication bias detected. Conclusions Care at CACs was associated with improved survival and neurological outcomes for patients with nontraumatic out-of-hospital cardiac arrest regardless of varying CAC definitions. Patients with shockable rhythms and those without prehospital return of spontaneous circulation benefited more from CACs. Evidence for bypassing hospitals or interhospital transfer remains inconclusive.

Predicting Clinical Outcomes in Acute Ischemic Stroke Patients Undergoing Endovascular Thrombectomy with Machine Learning : A Systematic Review and Meta-analysis.

PURPOSE: Conventional predictive models are based on a combination of clinical and neuroimaging parameters using traditional statistical approaches. Emerging studies have shown that the machine learning (ML) prediction models with multiple pretreatment clinical variables have the potential to accurately prognosticate the outcomes in acute ischemic stroke (AIS) patients undergoing thrombectomy, and hence identify patients suitable for thrombectomy. This article summarizes the published studies on ML models in large vessel occlusion AIS patients undergoing thrombectomy. METHODS: We searched electronic databases including PubMed from 1 January 2000 up to 14 October 2019 for studies that evaluated ML algorithms for the prediction of outcomes in stroke patients undergoing thrombectomy. We then used random-effects bivariate meta-analysis models to summarize the studies. RESULTS: We retained a total of five studies that evaluated ML (4 support vector machine, 1 decision tree model) with a combined cohort of 802 patients. The prevalence of good functional outcome defined by 90-day mRS of 0-2 when available. Random effects model demonstrated that the AUC was 0.846 (95% confidence interval, CI 0.686-0.902). A pooled diagnostic odds ratio of 12.6 was computed. The pooled sensitivity and specificity were 0.795 (95% CI 0.651-0.889) and 0.780 (95% CI 0.634-0.879), respectively. CONCLUSION: ML may be useful as an adjunct to clinical assessment to predict functional outcomes in AIS patients undergoing thrombectomy, and hence identify suitable patients for treatment. Further studies validating ML models in large multicenter cohorts are necessary to explore this further.