Comparison of linear-stochastic and nonlinear-deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death.
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 </=0.001). The sensitivity at 30 days was 100%, specificity 58%, and relative risk >100 (p </=0.001); sensitivity at 360 days was 95%, specificity 58%, and relative risk >11.4 (p </=0.001).


CONCLUSIONS
Heartbeat analysis by the time-dependent nonlinear PD2i algorithm is comparatively the superior test.
Skinner
James E
JE
Vicor Technologies, Inc., Boca Raton, FL, USA;
Meyer
Michael
M
Nester
Brian A
BA
Geary
Una
U
Taggart
Pamela
P
Mangione
Antoinette
A
Ramalanjaona
George
G
Terregino
Carol
C
Dalsey
William C
WC
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