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Few centers routinely report aortic arch calcification (AAC) due to the lack of an easy and effective evaluation method. The association between AAC and the clinical prognosis of patients who undergo transcatheter aortic valve replacement (TAVR) is unclear. We aimed to develop a rapid method to evaluate AAC in patients who underwent TAVR and to further assess their prognosis. We enrolled 464 consecutive patients with aortic stenosis who underwent TAVR. Patients with severe (11.2%), moderate (18.5%), mild (58.2%), and no (12.1%) AAC had an estimated 3-year all-cause mortality incidence of 39.6%, 20.8%, 13.4%, and 6.7% (log rank p < 0.001), respectively. Patients with severe AAC had a significantly higher incidence of both cardiovascular (log rank p = 0.002) and non-cardiovascular mortality (log rank p = 0.009), whereas patients with moderate AAC had a higher incidence of only non-cardiovascular mortality (p = 0.003) compared with patients with no/mild AAC. Moderate/severe AAC was an independent predictor of 3-year all-cause mortality in univariate (hazard ratio [HR]: 2.39, 95% confidence interval [CI]: 1.41–4.03; p = 0.001) and multivariate COX regression analyses (HR: 1.78, 95%CI: 1.04–3.06; p = 0.037). Our rapid semi-quantitative method to evaluate AAC is highly reproducible and can be used to assess AAC in patients who undergo TAVR. [ABSTRACT FROM AUTHOR]

Background: There are limited data available on the development of arrhythmias in patients at risk of high-degree atrioventricular block (HAVB) or complete heart block (CHB) following transcatheter aortic valve replacement (TAVR). Objective: This study aimed to explore the incidence and evolution of arrhythmias by monitoring patients at risk of HAVB or CHB after TAVR using smartwatches. Methods: We analyzed 188 consecutive patients in the prospective SMART TAVR (smartwatch-facilitated early discharge in patients undergoing TAVR) trial. Patients were divided into 2 groups according to the risk of HAVB or CHB. Patients were required to trigger a single-lead electrocardiogram (ECG) recording and send it to the Heart Health App via their smartphone. Physicians in the central ECG core lab would then analyze the ECG. The incidence and timing of arrhythmias and pacemaker implantation within a 30-day follow-up were compared. All arrhythmic events were adjudicated in a central ECG core lab. Results: The mean age of the patients was 73.1 (SD 7.3) years, of whom 105 (55.9%) were men. The mean discharge day after TAVR was 2.0 (SD 1.8) days. There were no statistically significant changes in the evolution of atrial fibrillation or atrial flutter, Mobitz I, Mobitz II, and third-degree atrial ventricular block over time in the first month after TAVR. The incidence of the left bundle branch block (LBBB) increased in the first week and decreased in the subsequent 3 weeks significantly (P <.001). Patients at higher risk of HAVB or CHB received more pacemaker implantation after discharge (n=8, 9.6% vs n=2, 1.9%; P =.04). The incidence of LBBB was higher in the group with higher HAVB or CHB risk (n=47, 56.6% vs n=34, 32.4%; P =.001). The independent predictors for pacemaker implantation were age, baseline atrial fibrillation, baseline right bundle branch block, Mobitz II, and third-degree atrioventricular block detected by the smartwatch. Conclusions: Except for LBBB, no change in arrhythmias was observed over time in the first month after TAVR. A higher incidence of pacemaker implantation after discharge was observed in patients at risk of HAVB or CHB. However, Mobitz II and third-degree atrioventricular block detected by the smartwatch during follow-ups were more valuable indicators to predict pacemaker implantation after discharge from the index TAVR. Trial Registration: ClinicalTrials.gov NCT04454177; https://clinicaltrials.gov/study/NCT04454177 [ABSTRACT FROM AUTHOR]

Background: Quadricuspid aortic valve (QAV) is a rare congenital cardiac anomaly associated with symptomatic aortic regurgitation (AR) or aortic stenosis (AS). Transcatheter aortic valve replacement (TAVR) for QAV remains uncertain. Methods: We retrospectively reviewed prospectively collected data from patients with QAV undergoing TAVR in our center and conducted a systematic literature review for further investigation. Results: Five patients with QAV were treated with TAVR between April 2016 and December 2023. The median age was 67 years (range: 59-86), and the median Society of Thoracic Surgeons score (STS-score) was 3.750% (range: 0.916%-11.823%). Procedural success was achieved in all cases. The median follow-up period was 3 years (from 30 days to 7 years). Four of the patients exhibited no serious complications, while one experienced delayed coronary obstruction. Our systematic review included 31 cases from 21 publications and our center. The median age of patients was 79 years (range: 57-90), including 18 males. The median STS score was 7.835%. Severe AS was present in 64.5% of the patients and severe AR in 41.9%. The most common QAV subtype was type B (48.4%). Technical success was achieved in 100% of the cases, with two cases reporting coronary obstruction and one required a permanent pacemaker implantation. During a median follow-up period of 1 year (from 30 days to 7 years), one case experienced serious complications of delayed coronary obstruction. Conclusion: The TAVR may be an alternative treatment for patients with QAV, preliminarily demonstrating feasible early and long-term results from current experience. However, extra precautions regarding coronary artery obstruction complications are necessary due to the rarity and anatomical complexity of QAV., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2025 Wenjing Sheng et al. Cardiology Research and Practice published by John Wiley & Sons Ltd.)

Objectives: The high incidence of coronary artery heart disease (CHD) poses a significant burden and challenge to public health systems globally. Effective prevention and early diagnosis of CHD have become key strategies to alleviate this burden. This study aims to explore the application of advanced machine learning techniques to enhance the accuracy of early screening and risk assessment for CHD., Methods: A total of 49 490 study subjects from the National Health and Nutrition Examination Survey (NHANES) database spanning from 1999 to 2018 were included. The dataset was randomly divided into training (70%) and testing (30%) sets. The dependent variable (outcome variable) was whether the subjects were informed of a CHD diagnosis, categorizing them into a CHD group and a non-CHD group. We reviewed the literature on risk factors associated with CHD, ultimately including 68 independent variables. The variable characteristics of the study subjects were analyzed, comparing differences between the CHD and non-CHD groups. Machine learning algorithms, specifically random forest (randomForest&#95;4.7-1.1) and XGBoost (xgboost&#95;1.7.7.1) were utilized for variable selection. A comprehensive analysis of the top 10 variables identified by these 2 algorithms were conducted, selecting those mutually recognized by both. A generalized linear model was used to analyze the relationships between variables and CHD, and classical logistic regression was used to construct the CHD risk prediction model. The model's ability to distinguish between CHD and non-CHD individuals was assessed using the area under the receiver operating characteristic curve (AUC); calibration measurements were conducted with the Hosmer-Lemeshow goodness-of-fit test to evaluate the consistency between predicted values and actual CHD proportions; and decision curve analysis was applied to evaluate the clinical benefits of the model's risk prediction. Finally, a nomogram was constructed to visually present the risk scoring of the final model., Results: The mean age of the overall population was (49.53±18.31) years, with males comprising 51.8%. Compared to the non-CHD group, the CHD group was older [(69.05± 11.32) years vs (48.67±18.07) years, P <0.001], had a higher proportion of females (67.1% vs 47.4%, P <0.001), and exhibited statistically significant differences in classical cardiovascular risk factors such as body mass index, systolic blood pressure, diastolic blood pressure, and smoking (all P <0.001). Additionally, there were statistically significant differences in non-classical cardiovascular factors, such as energy intake, vitamins E, vitamin K, calcium, phosphorus, magnesium, zinc, copper, sodium, potassium, and selenium (all P <0.05). Six key variables most associated with CHD occurrence were ultimately identified. The CHD risk prediction model constructed was as follows: logit(p)= -7.783+0.074×age+0.003×creatinine-0.003×platelets+0.257×glycated hemoglobin+0.003× uric acid+0.101×coefficient of variation of red cell distribution width. The model demonstrated excellent discriminative ability in predicting CHD, with an accuracy of 0.712 and an AUC of 0.841. Calibration curves indicated good consistency between predicted probabilities and actual values in both the training and testing sets, demonstrating model stability and reliability. Decision curve analysis suggested that the model provided net benefits across a range of threshold probabilities, supporting its potential application in clinical decision-making., Conclusions: This study successfully identified potential risk factors for CHD using machine learning techniques and developed a concise and practical clinical prediction model. Further prospective clinical cohort studies are needed to validate its potential for clinical application, enabling effective cardiovascular disease prevention and intervention strategies in real-world healthcare settings.

Transcatheter aortic valve replacement (TAVR) is not the preferred therapy for pure aortic regurgitation (AR). Extremely horizontal aorta (aorta root angle ≥70°) is regarded as an "off-label" use in self-expanding TAVR. This case series enrolled 7 consecutive pure AR patients who had extremely horizontal aorta and underwent self-expanding TAVR from the TORCH registry. To the best of our knowledge, this is the first study to report successful self-expanding TAVR for pure AR with extremely horizontal aorta, revealing the feasibility of both transapical and transfemoral TAVR in this challenging population.