OBJECTIVE: Comparative algorithmic evaluation of heartbeat series in low-to-high risk cardiac patients for the prospective prediction of risk of arrhythmic death (AD).
BACKGROUND: Heartbeat variation reflects cardiac autonomic function and risk of AD. Indices based on linear stochastic models are independent risk factors for AD in post-myocardial infarction (post-MI) cohorts. Indices based on nonlinear deterministic models have superior predictability in retrospective data.
METHODS: Patients were enrolled (N = 397) in three emergency departments upon presenting with chest pain and were determined to be at low-to-high risk of acute MI (>7%). Brief ECGs were recorded (15 min) and R-R intervals assessed by three nonlinear algorithms (PD2i, DFA, and ApEn) and four conventional linear-stochastic measures (SDNN, MNN, 1/f-Slope, LF/HF). Out-of-hospital AD was determined by modified Hinkle-Thaler criteria.
RESULTS: All-cause mortality at one-year follow-up was 10.3%, with 7.7% adjudicated to be AD. The sensitivity and relative risk for predicting AD was highest at all time-points for the nonlinear PD2i algorithm (p 100 (p 11.4 (p
CONCLUSIONS: Heartbeat analysis by the time-dependent nonlinear PD2i algorithm is comparatively the superior test.
Published In/Presented At
Skinner JE, Meyer M, Nester BA, Geary U, Taggart P, Mangione A, Ramalanjaona G, Terregino C, Dalsey WC. Comparison of linear-stochastic and nonlinear-deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death. Ther Clin Risk Manag. 2009 Jun;5(3):671-82. doi: 10.2147/tcrm.s5568. Epub 2009 Aug 20. PMID: 19707283; PMCID: PMC2731023.
Medicine and Health Sciences
Department of Emergency Medicine