Archivos de cardiologia de Mexico最新文献

Objective: The objective is to determinate the association between the degree of aortic valve calcification and the presence of paravalvular leakage (PVL) in Mexican patients who underwent transcatheter aortic valve replacement (TAVR).

Methods: We conducted a retrospective, analytic, cohort. Pooled data were retrospectively analyzed from the patient's files from January 2014 to July 2022. With a median follow-up of 6 months.

Results: We included 83 patients. 31 (37.3%) developed residual PVL. Several factors as male gender (men 58.1% versus women 41.9% p = 0.01), higher gradients previous TAVR (mean 57 mmHg in the group with versus mean 53 mmHg in the group without PVL, p = 0.01), bigger annulus diameters and perimeters as well as reduce left ventricular ejection fraction and a degree of aortic regurgitation previous TAVR were present more frequently in the group of residual PVL. Aortic valve calcification was the only predictor after the bivariate and multivariate analysis that showed an association with the presence of PVL after TAVR. The calculated cut-off value of calcium score was 2970 Agatston units, with a sensitivity of 70% and a specificity of 60% as a predictor for PVL.

Conclusions: The results are consistent with the previous data and there are no greater differences in the Mexican population. The severity of the aortic valve calcification is an independent predictor of PVL in patients who underwent TAVR.

Objectives: Patient body size and sex are significant factors in determining aortic dimensions. While females generally have smaller aortic dimensions, the criteria for surgical intervention in thoracic aortic aneurysms primarily rely on absolute diameters, disregarding sex-specific differences. The aim of this study was to compare sex differences in the upper limit of normal (ULN) and Z score in the population of a prospective nationwide multicenter registry and to determine the usefulness and fairness of guideline recommendations regarding aortic diameters in females.

Materials and methods: Transthoracic echocardiograms were performed on all patients enrolled measuring aortic dimensions at six levels following the current standard recommendations. Absolute diameters and indexed diameters by body surface area (BSA) and height were compared between males and females.

Results: A total of 1,000 healthy adults were included, with an average age of 38.3 ± 12.7 years. Among them, 553 were females, and the majority were either Caucasian or Native American. Females exhibited lower values in all anthropometric parameters, echocardiographic measurements, and blood pressure. Analysis of aortic measurements revealed that females had lower absolute aortic diameters across all segments. However, when indexed parameters were examined in the aortic root and Sino tubular Junction, females demonstrated lower height-indexed diameters but higher BSA-indexed diameters. The ULN for females, correlating with a Z-score of 2.5, was determined to be 3.62 cm.

Conclusion: Our study demonstrates the need for sex-specific considerations in defining aortic dilation, as females exhibit lower absolute aortic diameters but variations in indexed parameters, highlighting the limitations of using a universal cutoff value.

Objectives: We aimed to assess the performance of an artificial intelligence-electrocardiogram (AI-ECG)-based model capable of detecting acute coronary occlusion myocardial infarction (ACOMI) in the setting of patients with acute coronary syndrome (ACS).

Methods: This was a prospective, observational, longitudinal, and single-center study including patients with the initial diagnosis of ACS (both ST-elevation acute myocardial infarction [STEMI] & non-ST-segment elevation myocardial infarction [NSTEMI]). To train the deep learning model in recognizing ACOMI, manual digitization of a patient's ECG was conducted using smartphone cameras of varying quality. We relied on the use of convolutional neural networks as the AI models for the classification of ECG examples. ECGs were also independently evaluated by two expert cardiologists blinded to clinical outcomes; each was asked to determine (a) whether the patient had a STEMI, based on universal criteria or (b) if STEMI criteria were not met, to identify any other ECG finding suggestive of ACOMI. ACOMI was defined by coronary angiography findings meeting any of the following three criteria: (a) total thrombotic occlusion, (b) TIMI thrombus grade 2 or higher + TIMI grade flow 1 or less, or (c) the presence of a subocclusive lesion (> 95% angiographic stenosis) with TIMI grade flow < 3. Patients were classified into four groups: STEMI + ACOMI, NSTEMI + ACOMI, STEMI + non-ACOMI, and NSTEMI + non-ACOMI.

Results: For the primary objective of the study, AI outperformed human experts in both NSTEMI and STEMI, with an area under the curve (AUC) of 0.86 (95% confidence interval [CI] 0.75-0.98) for identifying ACOMI, compared with ECG experts using their experience (AUC: 0.33, 95% CI 0.17-0.49) or under universal STEMI criteria (AUC: 0.50, 95% CI 0.35-0.54), (p value for AUC receiver operating characteristic comparison < 0.001). The AI model demonstrated a PPV of 0.84 and an NPV of 1.0.

Conclusion: Our AI-ECG model demonstrated a higher diagnostic precision for the detection of ACOMI compared with experts and the use of STEMI criteria. Further research and external validation are needed to understand the role of AI-based models in the setting of ACS.