Human genetic variation and ventricular fibrillation resuscitation outcomes

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DESCRIPTION (provided by applicant): Although resuscitation is attempted in hundreds of thousands of cardiac arrest victims in the United States annually, survival following ventricular fibrillation arrest is less than 20% in most communities. Mortality following arrest is due largely to overwhelming/ irreversible injury to either the heart or brain. Although the predictors of heart and brain resuscitation have been identified, they collectively account for less than 25% of the variance related to prognosis, indicating that other factors substantially influence successful resuscitation. Importantly, genetic variation exists in many of the interrelated biological pathways that may potentially influence clinical outcomes. However, the influence of genetic variation on heart and brain outcomes following ventricular fibrillation arrest has not been evaluated. A better understanding of the genetic determinants of heart and brain resuscitation will advance knowledge regarding the pathophysiology of ventricular fibrillation, which in turn may have clinical implications for resuscitation care. The Specific Aims of the application are to examine the association of cardiac and brain resuscitation outcomes of ventricular fibrillation with common genetic variation in candidate genes in pathways related to the three phases of resuscitation -- electrical, hemodynamic, and metabolic phases. To undertake the investigation, we will assemble a biorepository of approximately 2500 subjects with out-of-hospital ventricular fibrillation arrest due to heart disease who receive care from comparably-trained paramedics. The biorepository includes biological information (WBC and DNA) that has been linked to clinical and outcome characteristics. We propose to complete nongenetic covariate data collection (years 1 and 2), assess common genetic variation in select candidate pathways (years 1 and 2), and assemble and analyze the data (years 3 and 4). Genotyping will use advanced, high-throughput methods to efficiently process samples. Analyses will incorporate standard and novel approaches of genetic association to assess if and how genetic variation influences resuscitation outcome.