Nonlinear indices of heart rate variability in chronic heart failure patients : redundancy and comparative clinical value

Aims: we aimed to assess the mutual interrelationships and to compare the prognostic value of a comprehensive set of nonlinear indices of heart rate variability (HRV) in a population of chronic heart failure (CHF) patients. Methods and Results: twenty nonlinear HRV indices, representative of symbolic dynamics, entropy, fractality-multifractality, predictability, empirical mode decomposition, and Poincar\ue9 plot families, were computed from 24-hour Holter recordings in 200 stable CHF patients in sinus rhythm (median age [interquartile range]: 54 [47\u201358] years, LVEF: 23 [19\u201328] %, NYHA class II\u2013III: 88%). End point for survival analysis (Cox model) was cardiac death or urgent transplantation. Homogeneous variables were grouped by cluster analysis, and in each cluster redundant variables were discarded. A prognostic model including only known clinical and functional risk factors was built and the ability of each selected HRV variable to add prognostic information to this model assessed. Bootstrap resampling was used to test the models stability. Four nonlinear variables showed a correlation >0.90 with classical linear ones and were discarded. Correlations >0.80 were found between several nonlinear variables. Twelve clusters were obtained and from each cluster a candidate predictor was selected. Only two variables (from empirical mode decomposition and symbolic dynamics families) added prognostic information to the clinical model. Conclusion: this exploratory study provides evidence that, despite some redundancies in the informative content of nonlinear indices and strong differences in their prognostic power, quantification of nonlinear properties of HRV provides independent information in risk stratification of CHF patients

Linear and Non-Linear Indices of Heart Rate Variability in Chronic Heart Failure: Mutual Interrelationships and Prognostic Value

We computed 3 linear and 20 nonlinear HRV indexes on 24-h Holter recordings from 200 stable CHF patients (age 52 +/- 9 yrs, NYHA II-III, LVEF 24 +/- 6%) with the aim to assess i) the mutual interrelationships between these indexes and ii) their prognostic value towards cardiac death. We found high correlations between variables, with potential bias in fitting survival models. To overcome this problem a clustering procedure was used, obtaining 11 clusters. Cox analysis showed that seven clusters were significantly associated with the study outcome (p < 0.05) but, after adjustment for major clinical prognostic parameters, significance persisted only in 2 of them (both composed by nonlinear variables). Our results indicate that composite scores derived from nonlinear indices contain significant prognostic information independent of classical clinical predictors, highlighting the importance of non linear HRV parameters in prognostic stratification of CHF patients

Nonlinear indices of heart rate variability in chronic heart failure patients: redundancy and comparative clinical value

Aims: We aimed to assess the mutual interrelationships and to compare the prognostic value of a comprehensive set of nonlinear indices of heart rate variability (HRV) in a population of chronic heart failure (CHF) patients. Methods and Results: Twenty nonlinear HRV indices, representative of symbolic dynamics, entropy, fractality-multifractality, predictability, empirical mode decomposition, and Poincar\ue9 plot families, were computed from 24-hour Holter recordings in 200 stable CHF patients in sinus rhythm (median age [interquartile range]: 54 [47\u201358] years, LVEF: 23 [19\u201328] %, NYHA class II\u2013III: 88%). End point for survival analysis (Cox model) was cardiac death or urgent transplantation. Homogeneous variables were grouped by cluster analysis, and in each cluster redundant variables were discarded. A prognostic model including only known clinical and functional risk factors was built and the ability of each selected HRV variable to add prognostic information to this model assessed...

Nonlinear indices of heart rate variability in chronic heart failure patients: redundancy and comparative clinical value

Aims: We aimed to assess the mutual interrelationships and to compare the prognostic value of a comprehensive set of nonlinear indices of heart rate variability (HRV) in a population of chronic heart failure (CHF) patients. Methods and Results: Twenty nonlinear HRV indices, representative of symbolic dynamics, entropy, fractality-multifractality, predictability, empirical mode decomposition, and Poincar´e plot families, were computed from 24-hour Holter recordings in 200 stable CHF patients in sinus rhythm (median age [interquartile range]: 54 [47–58] years, LVEF: 23 [19–28] %, NYHA class II–III: 88%). End point for survival analysis (Cox model) was cardiac death or urgent transplantation. Homogeneous variables were grouped by cluster analysis, and in each cluster redundant variables were discarded. A prognostic model including only known clinical and functional risk factors was built and the ability of each selectedHRVvariable to add prognostic information to this model assessed. Bootstrap resampling was used to test the models stability. Four nonlinear variables showed a correlation >0.90 with classical linear ones and were discarded. Correlations >0.80 were found between several nonlinear variables. Twelve clusters were obtained and from each cluster a candidate predictor was selected. Only two variables (from empirical mode decomposition and symbolic dynamics families) added prognostic information to the clinical model. Conclusion: This exploratory study provides evidence that, despite some redundancies in the informative content of nonlinear indices and strong differences in their prognostic power, quantification of nonlinear properties of HRV provides independent information in risk stratification of CHF patients