Spatial Repolarization Heterogeneity and Survival in Chagas Disease

R. Sassi; M. W. Rivolta; L. T. Mainardi; R. C. Reis; M. O. C. Rocha; A. L. P. Ribeiro; F. Lombardi

doi:10.3414/ME14-01-0002

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2014; 53(06): 464-468
DOI: 10.3414/ME14-01-0002

Original Articles

Schattauer GmbH

Spatial Repolarization Heterogeneity and Survival in Chagas Disease

Authors

R. Sassi

¹Dipartimento di Informatica, Università degli Studi di Milano, Crema, Italy
M. W. Rivolta

¹Dipartimento di Informatica, Università degli Studi di Milano, Crema, Italy
L. T. Mainardi

²Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
R. C. Reis

³Faculdade de Medicina e Hospital das Clínicas, Universidade Federal de Minas Gerais, Minas Gerais, Brazil
M. O. C. Rocha

³Faculdade de Medicina e Hospital das Clínicas, Universidade Federal de Minas Gerais, Minas Gerais, Brazil
A. L. P. Ribeiro

³Faculdade de Medicina e Hospital das Clínicas, Universidade Federal de Minas Gerais, Minas Gerais, Brazil
F. Lombardi

⁴UOC Malattie Cardiovascolari, Fondazione IRCCS Ca’ Granda – Ospedale Maggiore Policlinico, Dipartimento di Scienze Cliniche e di Comunità, Università degli Studi di Milano, Milano, Italy

Abstract

Summary

Objectives: We investigated if cardiac spatial repolarization heterogeneity might be associated with an increased risk of death in patients with chronic Chagas disease.

Methods: Repolarization heterogeneity was assessed using the V-index, a recently introduced metric founded on a biophysical model of the ECG. This metric provides an estimate of the standard deviation of the repolarization times across the heart. We analyzed 113 patients (aged 21– 67 years) enrolled between 1998 and 1999 who had a known serological status showing positive reactions to Trypanosoma cruzi. Fourteen subjects died during a 10-year follow-up period.

Results: The V-index was significantly lower in survivor (S) than in non-survivor (NS) subjects (S: 31.2 ± 13.3 ms vs NS: 41.2 ± 18.6 ms, single-tail t-test: p = 0.009, single-tail Wilcoxon rank sum test: p = 0.029). A V-index larger than 36.3 ms was related to a significantly higher risk of death in a univariate Cox proportional-hazards analysis (hazard ratio, HR = 5.34, p = 0.0046). In addition, V-index > 36.3 ms retained its prognostic value in a multivariate Cox proportional-hazards analysis after adjustment for other three clinical variables (left ventricular ejection factor < 0.50, QRS duration > 133 ms, ventricular tachycardia during stress testing or 24 hours Holter) and for T-wave amplitude variability > 30 μV, even using shrinkage, a statistical procedure that protects against over-fitting due to small sample size.

Conclusions: The study showed that an increased dispersion of repolarization times in patients with Chagas disease, as measured by the V-index, is significantly correlated with the risk of death in a univariate survival analysis. The V-index captures prognostic information not immediately available from the analysis of other established risk factors.

Keywords

Chagas disease - Chagas cardiomyopathy - ambulatory electrocardiography - digital signal processing - electrophysiological processes - ventricular fibrillation

Full Text

References

References
1 Laranja FS, Dias E, Nobrega G, Miranda A. Chagas’ Disease: A Clinical, Epidemiologic, and Pathologic Study. Circulation 1956; 14 (06) 1035-1060.
2 Rassi Jr A, Rassi SG, Rassi A. Sudden death in Chagas’ disease. Arq Bras Cardiol 2001; 76 (01) 86-96.
3 Amorim DS, Godoy RA, Manço JC, Tanaka A, Gallo L. Effects of Acute Elevation in Blood Pressure and of Atropine on Heart Rate in Chagas’ Disease: A Preliminary Report. Circulation 1968; 38 (02) 289-294.
4 Nunes MCP, Dones W, Morillo CA, Encina JJ, Ribeiro AL. Chagas Disease: An Overview of Clinical and Epidemiological Aspects. J Am Coll Cardiol 2013; 62 (09) 767-776.
5 Rassi Jr A, Rassi A, Little WC, Xavier SS, Rassi SG, Rassi AG. et al. Development and Validation of a Risk Score for Predicting Death in Chagas’ Heart Disease. N Engl J Med 2006; 355 (08) 799-808.
6 Rassi Jr A, Rassi A, Rassi SG. Predictors of Mortality in Chronic Chagas Disease A Systematic Review of Observational Studies. Circulation 2007; 115 (09) 1101-1108.
7 Ribeiro ALP, Cavalvanti PS, Lombardi F, Nunes MDCP, Barros MVL, Rocha MODC. Prognostic Value of Signal-Averaged Electrocardiogram in Chagas Disease. J Cardiovasc Electrophysiol 2008; 19 (05) 502-509.
8 Ribeiro ALP, Rocha MODC, Terranova P, Cesarano M, Nunes MDCP, Lombardi F. T-Wave Amplitude Variability and the Risk of Death in Chagas Disease. J Cardiovasc Electrophysiol 2011; 22 (07) 799-805.
9 Kuo CS, Munakata K, Reddy CP, Surawicz B. Characteristics and possible mechanism of ventricular arrhythmia dependent on the dispersion of action potential durations. Circulation 1983; 67 (06) 1356-1367.
10 Haigney MC, Zareba W, Gentlesk PJ, Goldstein RE, Illovsky M, McNitt S. et al. QT interval variability and spontaneous ventricular tachycardia or fibrillation in the Multicenter Automatic Defibrillator Implantation Trial (MADIT) II patients. J Am Coll Cardiol 2004; 44 (07) 1481-1487.
11 Couderc J-P. Zareba W, McNitt S, Maison-Blanche P, Moss AJ. Repolarization variability in the risk stratification of MADIT II patients. Europace 2007; 9 (09) 717-723.
12 Sassi R, Mainardi LT. An Estimate of the Dispersion of Repolarization Times Based on a Biophysical Model of the ECG. IEEE Trans Biomed Eng 2011; 58 (12) 3396-3405.
13 Van Oosterom A. Genesis of the T wave as based on an equivalent surface source model. J Electrocardiol 2001; 34 (4 Part B) 217-227.
14 Hamilton P. Open Source ECG Analysis software (OSEA) . www.eplimited.com; 2003
15 Lepeschkin E, Surawicz B. The Measurement of the Q-T Interval of the Electrocardiogram. Circulation 1952; 6 (03) 378-388.
16 Badilini F, Maison-Blanche P, Childers R, Coumel P. QT interval analysis on ambulatory electrocardiogram recordings: a selective beat averaging approach. Med Biol Eng Comput 1999; 37 (01) 71-79.
17 Therneau T. A Package for Survival Analysis in S. R package version. 2012: 2.36-12.
18 Harrell FE. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer; 2001
19 Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer; 2009
20 Sassi R, Mainardi LT. Quantification of spatial repolarization heterogeneity: Testing the robustness of a new technique. Computing in Cardiology (CinC) 2012; 39: 69-72.
21 Mainardi LT, Sassi R. Some theoretical results on the observability of repolarization heterogeneity on surface ECG. J Electrocardiol 2013; 46 (03) 270-275.
22 Malik M, Batchvarov VN. Measurement, interpretation and clinical potential of QT dispersion. J Am Coll Cardiol 2000; 36 (06) 1749-1766.
23 Day CP, McComb JM, Campbell RW. QT dispersion: an indication of arrhythmia risk in patients with long QT intervals. Br Heart J 1990; 63 (06) 342-344.