Comparison of linear-stochastic and nonlinear-deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death.

Publication/Presentation Date

6-1-2009

Abstract

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.

Volume

5

Issue

3

First Page

671

Last Page

682

ISSN

1176-6336

Disciplines

Medicine and Health Sciences

PubMedID

19707283

Department(s)

Department of Emergency Medicine

Document Type

Article

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