Derivation of Innovative Treatments for Cardiac Death

University Of Pittsburgh At Pittsburgh, Pittsburgh PA

Investigators

Linked publications & trials

Abstract

DESCRIPTION (provided by applicant): We are submitting this proposal in repsonse to PA-03-015, which uses the R21 mechanism. This program requires the utilization of existing human data, encourages new collaborations, does not require preliminary results, and limits the Research Plan to only 10 pages. Sudden cardiac arrest occurs approximately 250,000 times per year in the US, with most of these occurring in the out-of-hospital setting. The treatment of cardiac arrest follows guidelines established by the American Heart Association, known as advanced cardiac life support (ACLS). These guidelines use simplified universal treatment algorithms and have changed very little in the 30 years since their inception; and niether have survival rates. The focus of ACLS is on the treatment of ventricular fibrillation (VF), which was thought to be the predominant ECG rhythm but the incidence of which has been shown to be in decline. Pulseless electrical activity (PEA) and asystole are treated with the same algorithm. We believe that readily available, easily obtained clinical information can be useful in guiding clinical decision making. We will form a new collaboration between researchers at the University of Pittsburgh, the University of California, Los Angeles (UCLA), and the William Beaumont Hospital in Royal Oak, Michigan. Using existing data from each of the three centers, we will apply rigorous statistical model building techniques to explore new treatment schemes suggested by successful resuscitation outcomes. The specific aims of this proposal are to: (1) Derive prediction modeling of outcomes for victims of sudden cardiac death using readily available clinical information. (2) Cross-validate the predictive modeling with a variety of techniques. These analyses will allow us to derive innovative treatment approaches to the treatment of out-of-hospital cardiac arrest, based on validated predictive modeling of successful outcomes. We will determine whether changes in the recommended sequence of resuscitation interventions can be ustified in certain clinical situations, and which should receive future prospective study.