Pitt Researchers Receive $1.8M to Build Unique ECG Database, Study Effectiveness of CPR

PITTSBURGH – University of Pittsburgh researchers have received $1.8 million from the National Heart, Lung, and Blood Institute (NHLBI), part of the National Institutes of Health, to create a unique database of electrocardiogram (ECG) information that could one day be used to better guide real-time decision making during cardiopulmonary resuscitation (CPR) for cardiac arrest. The database would be the largest repository of its kind and could lead to new ways to evaluate CPR and patient outcomes.

“We will make use of this information to better understand how the quality of CPR might change the ECG patterns, and then link that to the outcomes of the patient all the way to discharge. If we can see what works best, we can further refine CPR interventions and save more lives,” said lead investigator James Menegazzi, Ph.D., Endowed Professor of Resuscitation Research, Department of Emergency Medicine, University of Pittsburgh School of Medicine.

Nearly 351,000 Americans experience a sudden cardiac arrest outside the hospital, and fewer than 7 percent survive to hospital discharge. Many of those patients suffer from ventricular fibrillation (VF), a condition in which erratic contraction of the cardiac muscle of the ventricles in the heart impairs the pumping of blood to the lungs and the body. VF is a common cause of cardiac arrest and typically is treated with defibrillation, or a shock, to correct the rhythm, which can look like irregular waves or spikes on the ECG.

The multi-center study will examine data from 10,000 ECG reports collected by the Resuscitation Outcomes Consortium (ROC), a clinical research network funded by the NHLBI. The research team has created a data management and analytic platform that converts ECG information into a uniform format, which will allow them to identify and measure waveform differences and assess which VF episodes respond better to defibrillation than others.

Menegazzi and his team will examine ECG and clinical data from the ROC to identify associations between specific VF patterns and clinical presentations, CPR quality and patient survival rates.

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