Risk stratification for arrhythmic death in an emergency department cohort: a new method of nonlinear PD2i analysis of the ECG

Abstract

James E Skinner1, Michael Meyer2, William C Dalsey3, Brian A Nester4, George Ramalanjaona5, Brian J O’Neil6, Antoinette Mangione7, Carol Terregino8, Abel Moreyra9, Daniel N Weiss10, Jerry M Anchin1, Una Geary11, Pamela Taggart121Vicor Technologies, Inc, Boca Raton, FL, USA; 2Max Planck Institute for Experimental Physiology, Goettingen, Germany; 3Kimbal Medical Center, Lakewood, NJ, USA; 4Lehigh Valley Hospital and Health Network, Allentown, PA, USA; 5St. Michaels Hospital, Newark, NJ, USA; 6William Beaumont Hospital, Royal Oak, MI, USA; 7Luitpolb Pharmaceuticals, Inc., Norristown, PA, USA; 8Cooper Medical Center, Camden, NJ, USA; 9Robert Wood Johnson Medical School, New Brunswick, NJ, USA; 10Florida Arrhythmia Consultants, Ft. Lauderdale, FL, USA; 11St. James Hospital, Dublin, Ireland; 12Albert Einstein Medical Center, Philadelphia, PA, USAAbstract: Heart rate variability (HRV) reflects both cardiac autonomic function and risk of sudden arrhythmic death (AD). Indices of HRV based on linear stochastic models are independent risk factors for AD in postmyocardial infarction (MI) cohorts. Indices based on nonlinear deterministic models have a higher sensitivity and specificity for predicting AD in retrospective data. A new nonlinear deterministic model, the automated Point Correlation Dimension (PD2i), was prospectively evaluated for prediction of AD. Patients were enrolled (N = 918) in 6 emergency departments (EDs) upon presentation with chest pain and being determined to be at risk of acute MI (AMI) >7%. Brief digital ECGs (>1000 heartbeats, ∼15 min) were recorded and automated PD2i results obtained. Out-of-hospital AD was determined by modified Hinkle-Thaler criteria. All-cause mortality at 1 year was 6.2%, with 3.5% being ADs. Of the AD fatalities, 34% were without previous history of MI or diagnosis of AMI. The PD2i prediction of AD had sensitivity = 96%, specificity = 85%, negative predictive value = 99%, and relative risk >24.2 (p ≤ 0.001). HRV analysis by the time-dependent nonlinear PD2i algorithm can accurately predict risk of AD in an ED cohort and may have both life-saving and resource-saving implications for individual risk assessment.Keywords: heart rate variability, sudden death, ventricular arrhythmias, chaos, non-linear