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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 in...

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Autores principales: Skinner, James E, Meyer, Michael, Nester, Brian A, Geary, Una, Taggart, Pamela, Mangione, Antoinette, Ramalanjaona, George, Terregino, Carol, Dalsey, William C
Formato: Texto
Lenguaje:English
Publicado: Dove Medical Press 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2731023/
https://www.ncbi.nlm.nih.gov/pubmed/19707283
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author Skinner, James E
Meyer, Michael
Nester, Brian A
Geary, Una
Taggart, Pamela
Mangione, Antoinette
Ramalanjaona, George
Terregino, Carol
Dalsey, William C
author_facet Skinner, James E
Meyer, Michael
Nester, Brian A
Geary, Una
Taggart, Pamela
Mangione, Antoinette
Ramalanjaona, George
Terregino, Carol
Dalsey, William C
author_sort Skinner, James E
collection PubMed
description 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.
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spelling pubmed-27310232009-08-25 Comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death Skinner, James E Meyer, Michael Nester, Brian A Geary, Una Taggart, Pamela Mangione, Antoinette Ramalanjaona, George Terregino, Carol Dalsey, William C Ther Clin Risk Manag Original Research 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. Dove Medical Press 2009 2009-08-20 /pmc/articles/PMC2731023/ /pubmed/19707283 Text en © 2009 Skinner et al, publisher and licensee Dove Medical Press Ltd. This is an Open Access article which permits unrestricted noncommercial use, provided the original work is properly cited.
spellingShingle Original Research
Skinner, James E
Meyer, Michael
Nester, Brian A
Geary, Una
Taggart, Pamela
Mangione, Antoinette
Ramalanjaona, George
Terregino, Carol
Dalsey, William C
Comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death
title Comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death
title_full Comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death
title_fullStr Comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death
title_full_unstemmed Comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death
title_short Comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death
title_sort comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ecgs to predict risk of arrhythmic death
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2731023/
https://www.ncbi.nlm.nih.gov/pubmed/19707283
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