Prediction of the Evolution of Chagas' Disease through Heart Rate Variability

• Grant number: 18/21212-0
• Support type: Scholarships in Brazil - Post-Doctorate
• Effective date (Start): November 01, 2018
• Effective date (End): October 10, 2021
• Field of knowledge: Health Sciences - Medicine - Medical Clinics
• Principal researcher: José Antonio Marin-Neto
• Grantee: Luiz Eduardo Virgilio da Silva
• Home Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Associated research grant: 16/25403-9 - Investigations on the pathogenesis, pathophysiology and therapy in humans and in an experimental model with the chronic cardiomyopathy of Chagas Disease, AP.TEM
• Associated scholarship(s): 19/21566-9 - Heart rate variability in the prognostic of Chagas Disease, BE.EP.PD

Abstract

One of the major challenges associated with Chagas' disease (CD) is the identification of the factors that lead individuals in the indeterminate form (IFCD) of the disease to evolve to the cardiac (CFCD) or digestive (DFCD) forms. The Rassi score, in spite of identifying the risk of death in chronically infected patients, is applicable only for patients already affected by the CFCD. On the other hand, studies show that disorders of the autonomic nervous system are responsible for several of the clinical complications present in CD. It is possible that these autonomic alterations are already present in the IFCD, which would represent a potential source of information to identify the individuals most likely to develop the clinical symptoms of the disease.There is a consensus, supported by extensive literature published in recent decades, that spontaneous heart rate fluctuations, known as heart rate variability (HRV), contain important information on autonomic modulation to the heart. However, more recently, it has been shown that other factors, such as the properties of cardiac pacemaker cells and humoral factors, are also of vital importance for the genesis of HRV. Several indices derived from HRV have been proposed and used as risk predictors for heart and systemic diseases.In this study, we will investigate which HRV indices present the higher prognostic value in patients with IFCD and CFCD. For this, we will use HRV indices from different families of methods, with proven relevance and applicability demonstrated in recent studies in the literature. The ECG will be collected from chagasic patients at the beginning and at the end (5 years) of the project. The differences in the HRV indexes observed during this period will be correlated with the clinical evolution of the disease. At the end, we expected to create a predictive model for the evolution of CD using the HRV indexes associated with machine learning techniques, which are able to combine and weigh the most relevant indexes for predicting the evolution of the disease

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

VIRGILIO SILVA, LUIZ EDUARDO; FAZAN JR, RUBENS; MARIN-NETO, JOSE ANTONIO. PyBioS: A freeware computer software for analysis of cardiovascular signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v. 197, DEC 2020. Web of Science Citations: 0.