Key result
Nonlinear characterization of ECGs revealed significant differences between healthy controls and Chagas' disease patients, suggesting early autonomic disturbances.
Why the study?
Nonlinear modeling of ECGs may allow the definition of early risk markers in patients with Chagas disease who lack evidence of cardiac involvement on standard tests.
Does nonlinear characterization of ECGs improve early detection of cardiac involvement in patients with Chagas' disease?
Observational
Does nonlinear characterization of ECGs improve early detection of cardiac involvement in patients with Chagas' disease?
Nonlinear ECG analysis may detect early autonomic disturbances in Chagas' disease patients before standard ECG alterations appear, potentially enhancing risk stratification.
Nonlinear ECG analysis may flag preclinical autonomic changes in Chagas disease; leaves open prospective validation before clinical use.
According to the World-Wide Organization of the Health, the number of people infected with the Tripanosoma Cruzi is considered between 6 and 8 million, causal agent of the Chagas’ disease , and in 550000 the people are expected to be exposed to the affectation risk. When concluding in 1983 a longitudinal epidemiologist study in patients with the disease evaluated every 3 years, the cardiac affectation: chronic Chagasic myocarditis (MCHC) increased from a 17% at the beginning of the study to a 49, 4% after 15 years. Previous studies of the variability of cardiac frequency (HRV) in patients with the Chagas’ disease, show alterations in the spectral indices of the HRV. A nonlinear modeling technique allows the definition of early risk markers in patients with Chagas’s disease and without any evidence of cardiac involvement evaluated by standard diagnostic test (CH1). We analyze ECGs by Holter recordings in patients with ECG alterations (CH2), patients without ECG alterations (CH1) who had positive serological findings for Chagas’ disease and healthy (Control) matched for sex and age. Two ECGs were digitized for each volunteer, one begins at supine position and the other at orthostatic position. The technique applied is based upon quantitative comparison of the evolution from similar ECGs Sub segments, and a measure of the predictability decay rate were calculated for each register. We did find significant differences among Control and CH2 during supine and also standing with high sensibility and specificity in the early detection of cardiac involvement. It is suggested that these alterations are very early autonomic disturbances in patients without ECG alterations (CH1) that could be useful to enhance risk stratification.
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Vizcardo et al. (2019) conducted an observational in Chagas' disease. Nonlinear characterization of ECGs vs. Healthy controls was evaluated on Differences in predictability decay rate of ECG sub-segments. Nonlinear characterization of ECGs revealed significant differences between healthy controls and Chagas' disease patients, suggesting early autonomic disturbances.
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