Voss A, Fischer C, Schroeder R, Figulla H R, Goernig M
Department of Medical Engineering and Biotechnology, University of Applied Sciences Jena, Carl-Zeiss-Promenade 2, 07745 Jena, Germany.
Methods Inf Med. 2010;49(5):511-5. doi: 10.3414/ME09-02-0050. Epub 2010 Jun 7.
The prognostic value of heart rate variability in patients with dilated cardiomyopathy (DCM) is limited and does not contribute to risk stratification although the dynamics of ventricular repolarization differs considerably between DCM patients and healthy subjects. Neither linear nor nonlinear methods of heart rate variability analysis could discriminate between patients at high and low risk for sudden cardiac death.
The aim of this study was to analyze the suitability of the new developed segmented Poincaré plot analysis (SPPA) to enhance risk stratification in DCM.
In contrast to the usual applied Poincaré plot analysis the SPPA retains nonlinear features from investigated beat-to-beat interval time series. Main features of SPPA are the rotation of cloud of points and their succeeded variability depended segmentation.
Significant row and column probabilities were calculated from the segments and led to discrimination (up to p<0.005) between low and high risk in DCM patients.
For the first time an index from Poincaré plot analysis of heart rate variability was able to contribute to risk stratification in patients suffering from DCM.
尽管扩张型心肌病（DCM）患者与健康受试者之间心室复极动力学存在显著差异，但心率变异性对DCM患者的预后价值有限，且无助于风险分层。心率变异性分析的线性和非线性方法均无法区分心脏性猝死高风险和低风险患者。
本研究旨在分析新开发的分段庞加莱图分析（SPPA）在增强DCM风险分层方面的适用性。
与通常应用的庞加莱图分析不同，SPPA保留了所研究的逐搏间期时间序列的非线性特征。SPPA的主要特征是点云的旋转及其后续的变异性依赖分割。
从各段计算出显著的行和列概率，从而实现了DCM患者低风险和高风险之间的区分（p值低至<0.005）。
心率变异性的庞加莱图分析首次得出的一个指标能够有助于DCM患者的风险分层。
