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Segmented Symbolic Dynamics for Risk Stratification in Patients with Ischemic Heart Failure

Abstract

Chronic heart failure (CHF) is recognized as major and escalating public health problem. Approximately 69% of CHF patients suffer from cardiac death within 5 years after the initial diagnosis. Until now, no generally accepted ECG risk predictors in CHF patients are available. The objective of this study was to investigate the suitability of the new developed non-linear method segmented symbolic dynamics (SSD) for risk stratification in patients with ischemic cardiomyopathy (ICM) in comparison to other indices from time and frequency domain, non-linear dynamics, and clinical markers. Twenty-four hour Holter ECGs were recorded from 256 ICM patients. Heart rate variability (HRV) was analyzed from the filtered beat-to-beat interval time series. For calculating SSD, NN interval time series were segmented in 1 min overlapping windows with a window length of 30 min. For each window a symbol- and word-transformation was performed and probabilities of word type occurrences were calculated. Several indices from frequency domain and non-linear dynamics revealed high univariate significant differences (p < 0.01) discriminating low (n = 221) and high risk ICM patients (n = 35). For multivariate risk stratification in ICM patients the two optimal mixed parameter sets consisting of either two clinical and three non-clinical indices (two from SSD) or three clinical and two non-clinical indices (one from SSD) achieved 74 and 75% sensitivity and 79 and 76% specificity, respectively. These results suggest that the new SSD enhances considerably risk stratification in ICM patients. The multivariate analysis including SSD leads to an optimum accuracy of 81%.

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References

  1. 1.

    Austin, B. A., Y. Wang, G. L. Smith, V. Vaccarine, H. M. Krumholz, and R. L. McNamara. Systolic function as a predictor of mortality and quality of life in long-term survivors with heart failure. Clin. Cardiol. 31(3):119–124, 2008. doi:10.1002/clc.20118.

    Article  Google Scholar 

  2. 2.

    Bayes-Genis, A., R. Vazquez, T. Puig, C. Fernandez-Palomeque, J. Fabregat, A. Bardaji, et al. Left atrial enlargement and NT-proBNP as predictors of sudden cardiac death in patients with heart failure. Eur. J. Heart Fail. 9(8):802–807, 2007. doi:10.1016/j.ejheart.2007.05.001.

    Article  Google Scholar 

  3. 3.

    Bruch, C., M. Gotzmann, J. Stypmann, F. Wenzelburger, M. Rothenburger, M. Grude, et al. Electrocardiography and Doppler echocardiography for risk stratification in patients with chronic heart failure: incremental prognostic value of QRS duration and a restrictive mitral filling pattern. J. Am. Coll. Cardiol. 45(7):1072–1075, 2005. doi:10.1016/j.jacc.2004.12.064.

    Article  Google Scholar 

  4. 4.

    Cooley, R. L., N. Montano, C. Cogliati, P. van de Borne, W. Richenbacher, R. Oren, et al. Evidence for a central origin of the low-frequency oscillation in RR-interval variability. Circulation 98(6):556–561, 1998.

    Google Scholar 

  5. 5.

    Cygankiewicz, I., W. Zareba, R. Vazquez, A. Bayes-Genis, D. Pascual, C. Macaya, et al. Risk stratification of mortality in patients with heart failure and left ventricular ejection fraction >35%. Am. J. Cardiol. 103(7):1003–1010, 2009. doi:10.1016/j.amjcard.2008.11.061.

    Article  Google Scholar 

  6. 6.

    Ding, L., W. Hua, H. Niu, K. Chen, and S. Zhang. Primary prevention of sudden cardiac death using implantable cardioverter defibrillators. Europace 10(9):1034–1041, 2008. doi:10.1093/europace/eun150.

    Article  Google Scholar 

  7. 7.

    Goode, K. M., S. Nabb, J. G. Cleland, and A. L. Clark. A comparison of patient and physician-rated New York Heart Association Class in a community-based heart failure clinic. J. Card. Fail. 14(5):379–387, 2008. doi:10.1016/j.cardfail.2008.01.014.

    Article  Google Scholar 

  8. 8.

    Guzzetti, S., M. T. La Rovere, G. D. Pinna, R. Maestri, E. Borroni, A. Porta, et al. Different spectral components of 24 h heart rate variability are related to different modes of death in chronic heart failure. Eur. Heart J. 26(4):357–362, 2005. doi:10.1093/eurheartj/ehi067.

    Article  Google Scholar 

  9. 9.

    Ho, K. K., K. M. Anderson, W. B. Kannel, W. Grossman, and D. Levy. Survival after the onset of congestive heart failure in Framingham heart study subjects. Circulation 88(1):107–115, 1993.

    Google Scholar 

  10. 10.

    Kenchaiah, S., S. J. Pocock, D. Wang, P. V. Finn, L. A. Zornoff, H. Skali, et al. Body mass index and prognosis in patients with chronic heart failure: insights from the Candesartan in heart failure: assessment of reduction in mortality and morbidity (charm) program. Circulation 116(6):627–636, 2007. doi:10.1161/CIRCULATIONAHA.106.679779.

    Article  Google Scholar 

  11. 11.

    Kleiger, R. E., J. P. Miller, J. T. Bigger, J. T. Bigger, Jr., and A. J. Moss. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am. J. Cardiol. 59(4):256–262, 1987. doi:10.1016/0002-9149(87)90795-8.

    Article  Google Scholar 

  12. 12.

    La Rovere, M. T., G. D. Pinna, R. Maestri, A. Mortara, S. Capomolla, O. Febo, et al. Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients. Circulation 107(4):565–570, 2003.

    Article  Google Scholar 

  13. 13.

    Lane, R. E., M. R. Cowie, and A. W. Chow. Prediction and prevention of sudden cardiac death in heart failure. Heart 91(5):674–680, 2005. doi:10.1136/hrt.2003.025254.

    Article  Google Scholar 

  14. 14.

    Makikallio, T. H., H. V. Huikuri, A. Makikallio, L. B. Sourander, R. D. Mitrani, A. Castellanos, et al. Prediction of sudden cardiac death by fractal analysis of heart rate variability in elderly subjects. J. Am. Coll. Cardiol. 37(5):1395–1402, 2001. doi:10.1016/S0735-1097(01)01171-8.

    Article  Google Scholar 

  15. 15.

    Malliani, A., M. Pagani, F. Lombardi, and S. Cerutti. Cardiovascular neural regulation explored in the frequency domain. Circulation 84(2):482–492, 1991.

    Google Scholar 

  16. 16.

    Mrowka, R., B. Schluter, A. Patzak, and P. B. Persson. Symbolic dynamics approach in the analysis of heart rate in premature babies at high risk for sudden infant death syndrome (SIDS). Comput. Cardiol. 24:37–40, 1997. doi:10.1109/CIC.1997.647822.

    Google Scholar 

  17. 17.

    Peng, C. K., S. Havlin, H. E. Stanley, and A. L. Goldberger. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5(1):82–87, 1995. doi:10.1063/1.166141.

    Article  Google Scholar 

  18. 18.

    Peng, C. K., J. Mietus, J. M. Hausdorff, S. Havlin, H. E. Stanley, and A. L. Goldberger. Long-range anticorrelations and non-gaussian behavior of the heartbeat. Phys. Rev. Lett. 70(9):1343–1346, 1993.

    Article  Google Scholar 

  19. 19.

    Salo, T. M., J. Sundell, J. Knuuti, J. Kemppainen, K. Stolen, P. Nuutila, et al. Fractal scaling properties of heart rate dynamics and myocardial efficiency in dilated cardiomyopathy. Clin. Res. Cardiol. 98(11):725–730, 2009. doi:10.1007/s00392-009-0060-y.

    Article  Google Scholar 

  20. 20.

    Stein, P. K., J. I. Barzilay, P. H. Chaves, S. Q. Mistretta, P. P. Domitrovich, J. S. Gottdiener, et al. Novel measures of heart rate variability predict cardiovascular mortality in older adults independent of traditional cardiovascular risk factors: the cardiovascular health study (CHS). J. Cardiovasc. Electrophysiol. 19(11):1169–1174, 2008. doi:10.1111/j.1540-8167.2008.01232.x.

    Article  Google Scholar 

  21. 21.

    Stein, P. K., and A. Reddy. Non-linear heart rate variability and risk stratification in cardiovascular disease. Indian Pacing Electrophysiol. J. 5(3):210–220, 2005.

    Google Scholar 

  22. 22.

    Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 93(5):1043–1065, 1996.

    Google Scholar 

  23. 23.

    Tulppo, M. P., H. V. Huikuri, E. Tutungi, D. S. Kimmerly, A. W. Gelb, R. L. Hughson, et al. Feedback effects of circulating norepinephrine on sympathetic outflow in healthy subjects. Am. J. Physiol. Heart Circ. Physiol. 288(2):H710–H715, 2005. doi:10.1152/ajpheart.00540.2004.

    Article  Google Scholar 

  24. 24.

    Vazquez, R., A. Bayes-Genis, I. Cygankiewicz, D. Pascual-Figal, L. Grigorian-Shamagian, R. Pavon, et al. The MUSIC risk score: a simple method for predicting mortality in ambulatory patients with chronic heart failure. Eur. Heart J. 30(9):1088–1096, 2009. doi:10.1093/eurheartj/ehp032.

    Article  Google Scholar 

  25. 25.

    Voss, A., K. Hnatkova, N. Wessel, J. Kurths, A. Sander, A. Schirdewan, et al. Multiparametric analysis of heart rate variability used for risk stratification among survivors of acute myocardial infarction. Pacing Clin. Electrophysiol. 21(1 Pt 2):186–192, 1998.

    Article  Google Scholar 

  26. 26.

    Voss, A., J. Kurths, H. J. Kleiner, A. Witt, N. Wessel, P. Saparin, et al. The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death. Cardiovasc. Res. 31(3):419–433, 1996. doi:10.1016/S0008-6363(96)00008-9.

    Google Scholar 

  27. 27.

    Voss, A., R. Schroeder, S. Truebner, M. Goernig, H. R. Figulla, and A. Schirdewan. Comparison of nonlinear methods symbolic dynamics, detrended fluctuation, and Poincaré plot analysis in risk stratification in patients with dilated cardiomyopathy. Chaos 17(1):015–120, 2007. doi:10.1063/1.2404633.

    Article  MathSciNet  Google Scholar 

  28. 28.

    Voss, A., S. Schulz, R. Schroeder, M. Baumert, and P. Caminal. Methods derived from nonlinear dynamics for analysing heart rate variability. Philos. Trans. A Math. Phys. Eng. Sci. 367(1887):277–296, 2009. doi:10.1098/rsta.2008.0232.

    MATH  Article  Google Scholar 

  29. 29.

    Woo, M. A., W. G. Stevenson, D. K. Moser, and H. R. Middlekauff. Complex heart rate variability and serum norepinephrine levels in patients with advanced heart failure. J. Am. Coll. Cardiol. 23(3):565–569, 1994. doi:10.1016/0735-1097(94)90737-4.

    Article  Google Scholar 

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Acknowledgments

This study was partly supported by grants from the DAAD program “Acciones Integradas Hispano-Alemanas 2006–2007” and the Deutsche Forschungsgemeinschaft DFG (DFG Vo 505/8-1), by the framework of the Comisión Interministerial de Ciencia y Tecnología (CICYT) grant TEC2004-02274 and by the Center for International Business and Education Research of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) which is an initiative of ISCIII. We gratefully acknowledge the co-operation from cardiological departments of the Spanish hospitals participating in the MUSIC Study: Hospital Santa Creu i Sant Pau—ICCC, Barcelona; Hospital de Valme, Seville; Hospital Son Dureta, Palma de Mallorca; Hospital Joan XXIII, Tarragona; Hospital Virgen Arrixaca, Murcia; Hospital Universitario, Santiago de Compostela; Hospital Gregorio Marañon, Madrid and Hospital Insular, Las Palmas.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Correspondence to Andreas Voss.

Additional information

I. Cygankiewicz, R. Vázquez, and A. B. de Luna are from the MUSIC Trial.

Associate Editor Joseph H Gorman III oversaw the review of this article.

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Voss, A., Schroeder, R., Caminal, P. et al. Segmented Symbolic Dynamics for Risk Stratification in Patients with Ischemic Heart Failure. Cardiovasc Eng Tech 1, 290–298 (2010). https://doi.org/10.1007/s13239-010-0025-3

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Keywords

  • Risk stratification
  • Non-linear dynamics
  • Cardiovascular diseases
  • Symbolic dynamics