Incidence and Predictors of Progression to Chagas Cardiomyopathy: Long-Term Follow-Up of Trypanosoma cruzi-Seropositive Individuals.

BACKGROUND: There are few contemporary cohorts of Trypanosoma cruzi-seropositive individuals, and the basic clinical epidemiology of Chagas disease is poorly understood. Herein, we report the incidence of cardiomyopathy and death associated with T. cruzi seropositivity. METHODS: Participants were selected in blood banks at 2 Brazilian centers. Cases were defined as T. cruzi-seropositive blood donors. T. cruzi-seronegative controls were matched for age, sex, and period of donation. Patients with established Chagas cardiomyopathy were recruited from a tertiary outpatient service. Participants underwent medical examination, blood collection, ECG, and echocardiogram at enrollment (2008-2010) and at follow-up (2018-2019). The primary outcomes were all-cause mortality and development of cardiomyopathy, defined as the presence of a left ventricular ejection fraction <50% or QRS complex duration ≥120 ms, or both. To handle loss to follow-up, a sensitivity analysis was performed using inverse probability weights for selection. RESULTS: We enrolled 499 T. cruzi-seropositive donors (age 48±10 years, 52% male), 488 T. cruzi-seronegative donors (age 49±10 years, 49% male), and 101 patients with established Chagas cardiomyopathy (age 48±8 years, 59% male). The mortality in patients with established cardiomyopathy was 80.9 deaths/1000 person-years (py) (54/101, 53%) and 15.1 deaths/1000 py (17/114, 15%) in T. cruzi-seropositive donors with cardiomyopathy at baseline. Among T. cruzi-seropositive donors without cardiomyopathy at baseline, mortality was 3.7 events/1000 py (15/385, 4%), which was no different from T. cruzi-seronegative donors with 3.6 deaths/1000 py (17/488, 3%). The incidence of cardiomyopathy in T. cruzi-seropositive donors was 13.8 (95% CI, 9.5-19.6) events/1000 py (32/262, 12%) compared with 4.6 (95% CI, 2.3-8.3) events/1000 py (11/277, 4%) in seronegative controls, with an absolute incidence difference associated with T. cruzi seropositivity of 9.2 (95% CI, 3.6-15.0) events/1000 py. T. cruzi antibody level at baseline was associated with development of cardiomyopathy (adjusted odds ratio, 1.4 [95% CI, 1.1-1.8]). CONCLUSIONS: We present a comprehensive description of the natural history of T. cruzi seropositivity in a contemporary patient population. The results highlight the central importance of anti-T. cruzi antibody titer as a marker of Chagas disease activity and risk of progression.

Impact of incorporating echocardiographic screening into a clinical prediction model to optimise utilisation of echocardiography in primary care.

INTRODUCTION: Access to public subspecialty healthcare is limited in underserved areas of Brazil, including echocardiography (echo). Long waiting lines and lack of a prioritisation system lead to diagnostic lag and may contribute to poor outcomes. We developed a prioritisation tool for use in primary care, aimed at improving resource utilisation, by predicting those at highest risk of having an abnormal echo, and thus in highest need of referral. METHODS: All patients in the existing primary care waiting list for echo were invited for participation and underwent a clinical questionnaire, simplified 7-view echo screening by non-physicians with handheld devices, and standard echo by experts. Two derivation models were developed, one including only clinical variables and a second including clinical variables and findings of major heart disease (HD) on echo screening (cut point for high/low-risk). For validation, patients were risk-classified according to the clinical score. High-risk patients and a sample of low-risk underwent standard echo. Intermediate-risk patients first had screening echo, with a standard echo if HD was suspected. Discrimination and calibration of the two models were assessed to predict HD in standard echo. RESULTS: In derivation (N = 603), clinical variables associated with HD were female gender, body mass index, Chagas disease, prior cardiac surgery, coronary disease, valve disease, hypertension and heart failure, and this model was well calibrated with C-statistic = 0.781. Performance was improved with the addition of echo screening, with C-statistic = 0.871 after cross-validation. For validation (N = 1526), 227 (14.9%) patients were classified as low risk, 1082 (70.9%) as intermediate risk and 217 (14.2%) as high risk by the clinical model. The final model with two categories had high sensitivity (99%) and negative predictive value (97%) for HD in standard echo. Model performance was good with C-statistic = 0.720. CONCLUSION: The addition of screening echo to clinical variables significantly improves the performance of a score to predict major HD.

Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients-The SaMi-Trop cohort.

BACKGROUND: Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm showed excellent accuracy to detect LVSD in a general population, but its accuracy in ChD has not been tested. OBJECTIVE: To analyze the ability of AI to recognize LVSD in patients with ChD, defined as a left ventricular ejection fraction determined by the Echocardiogram ≤ 40%. METHODOLOGY/PRINCIPAL FINDINGS: This is a cross-sectional study of ECG obtained from a large cohort of patients with ChD named São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) Study. The digital ECGs of the participants were submitted to the analysis of the trained machine to detect LVSD. The diagnostic performance of the AI-enabled ECG to detect LVSD was tested using an echocardiogram as the gold standard to detect LVSD, defined as an ejection fraction <40%. The model was enriched with NT-proBNP plasma levels, male sex, and QRS ≥ 120ms. Among the 1,304 participants of this study, 67% were women, median age of 60; there were 93 (7.1%) individuals with LVSD. Most patients had major ECG abnormalities (59.5%). The AI algorithm identified LVSD among ChD patients with an odds ratio of 63.3 (95% CI 32.3-128.9), a sensitivity of 73%, a specificity of 83%, an overall accuracy of 83%, and a negative predictive value of 97%; the AUC was 0.839. The model adjusted for the male sex and QRS ≥ 120ms improved the AUC to 0.859. The model adjusted for the male sex and elevated NT-proBNP had a higher accuracy of 0.89 and an AUC of 0.874. CONCLUSION: The AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD.