ABSTRACT
INTRODUCTION: Thrombin generation assay (TGA) is a laboratory method that provides the global evaluation of hemostasis. The association between thrombin generation and all-cause mortality is poorly investigated and results are contradictory. This study evaluated whether TGA parameters are associated with all-cause mortality in a prospective cohort. METHODS: This study was conducted in 2,588 participants enrolled at baseline of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). TGA was performed using the Calibrated Automated Thrombogram (CAT) method, and its parameters lagtime, time-to-peak, peak, Endogenous Thrombin Potential (ETP) and normalized ETP (nETP) were evaluated according to the reference interval (RI). The association between TGA parameters and all-cause mortality was estimated by Cox regression and adjusted for confounders. RESULTS: The mean follow-up time was 6.6 ± 2.7 years and 85 deaths occurred. After adjustment, time-to-peak values above the RI at low and high tissue factor (TF) concentrations were associated with higher risk of death [HR = 2.45 (95 % CI: 1.17-5.13) and HR = 2.24 (95 % CI: 1.02-4.93), respectively] and nETP and peak values below RI at high TF concentration were associated with higher risk of death [HR = 3.85 (95 % CI: 1.39-10.68) and HR = 2.56 (95 % CI: 1.17-5.61), respectively]. CONCLUSIONS: Delayed thrombin generation was associated with higher risk of all-cause mortality.
Subject(s)
Thrombin , Adult , Humans , Blood Coagulation Tests , Brazil , Prospective Studies , Longitudinal StudiesABSTRACT
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.
