Where: Toronto Rehab – University Centre, Basement Theatre, 550 University Ave., Toronto, M5G 2A2
When: Tuesday, Feb 7th, 2017, 12:15pm – 1:00pm
Speaker: Muammar Kabir, PhD
Affiliation: Toronto Rehabilitation Institute
Abstract: Sudden cardiac death (SCD) is a leading cause of mortality with an incidence of 3 million cases per year worldwide, despite advances in the treatment and prevention of cardiovascular diseases. It is therefore necessary to develop effective, non-invasive and low-cost readily available tool to identify individuals at increased risk of SCD. Participants from the Atherosclerosis Risk in Communities study and Cardiovascular Health Study (n=20 177; age, 59.3±10.1 years; age range, 44–100 years; 56% female; 77% white) were followed up for 14.0 years (median). Five ECG markers of global electric heterogeneity (GEH; sum absolute QRST integral, spatial QRST angle, spatial ventricular gradient [SVG] magnitude, SVG elevation, and SVG azimuth) were measured on standard 12-lead ECGs. Cox proportional hazards and competing risks models evaluated associations between GEH electrocardiographic parameters and SCD. After multivariable adjustment, baseline GEH parameters and large increases in GEH parameters over time were independently associated with SCD. Final SCD risk scores included age, sex, race, diabetes mellitus, hypertension, coronary heart disease, stroke, and GEH parameters as continuous variables. When GEH parameters were added to clinical/demographic factors, the C statistic increased from 0.777 to 0.790 (P=0.008), the risk score classified 10-year SCD risk as high (>5%) in 7.2% of participants, 10% of SCD victims were appropriately reclassified into a high-risk category, and only 1.4% of SCD victims were inappropriately reclassified from high to intermediate risk. The net reclassification index was 18.3%. In conclusion, abnormal electrophysiological substrate quantified by GEH parameters is independently associated with SCD in the general population. The addition of GEH parameters to clinical characteristics improves SCD risk prediction.
Biography: Muammar Kabir is a Postdoctoral Fellow with Azadeh Yadollahi in the Sleep Laboratory. Muammar received his PhD in Biomedical Engineering from the University of Adelaide, Australia. His thesis focused on the development of a novel approach for the quantification of cardiorespiratory interaction using the concept of Joint Symbolic Dynamics, which provided a simple technique for diagnosis of obstructive sleep apnea and other cardiorespiratory system disturbances. He developed skills in analysis of long electrocardiographic recordings, respiration and blood pressure, and is familiar with holistic systems biology approach in joint analysis of several signals. His major interests lie in the field of electrophysiology, sleep disorders, cardiac and respiratory signal processing for extraction of vital physiological signal characteristics that will help diagnose rare cardiac arrhythmias for further understanding and improving diagnostics of cardiovascular disorders in patients.