Long-term efficacy and safety of cryoballoon ablation of atrial fibrillation: A systematic review and meta-analysis.

BACKGROUND: This meta-analysis evaluated long-term efficacy and safety of cryoballoon ablation (CB) of atrial fibrillation (AF). METHODS: PubMed, Cochrane Library, and Web of Science were searched until July 31, 2023, for published works investigating efficacy and safety of CB of AF in which mean/median follow-up time was not less than 36 months. Safety was assessed by adverse events. Efficacy was assessed by AF recurrence, defined as any atrial arrhythmias lasting more than 30 s. RESULTS: A total of 19 clinical studies were included. After an average of 58.1 months of follow-up, the overall AF recurrence rate was about 37%. The predictors of recurrence were duration of AF (HR 1.00; 95% CI [1.00 ∼ 1.01]), early recurrence of atrial fibrillation (HR 3.96; 95%CI [1.12 ∼ 14.02]), left atrial diameter (HR 1.04; 95%CI [1.02 ∼ 1.06]), and persistent AF (HR1.47; 95% CI [1.19 ∼ 1.82]). In terms of safety, the incidence of transient phrenic paralysis (PNP) was the highest, about 3%; followed by vascular complications (about 2%); pseudoaneurysm, permanent PNP, and all-cause death was (about 1%); and pericardial effusion and stroke / TIA was very low. CONCLUSION: CB is associated with low rates of severe complications and reasonable success rates.

LDL-C: An Important Independent Risk Factor for New-Onset Heart Block in Patients with Severe Aortic Stenosis and Heart Failure after TAVR.

Background: Transcatheter aortic valve replacement (TAVR) is an effective alternative treatment for patients with aortic stenosis (AS) who have intermediate to high surgical risk or who are inoperable. However, the incidence of conduction abnormalities is high after TAVR, which can reduce the effectiveness of the surgery. Our research objective is to explore the risk factors of new-onset conduction abnormalities after TAVR, providing reference value for clinical doctors to better prevent and treat conduction abnormalities. Methods: Patients who underwent TAVR were divided into those who developed heart block and those who did not. Baseline clinical characteristics, cardiac structural parameters, procedural characteristics, electrocardiogram (ECG) changes before and after TAVR ( △ = postoperative minus preoperative), and surgical complications were compared. Logistic regression was applied to identify significant risk factors for new-onset heart block. Results: We studied 93 patients, of whom 34.4% developed heart blocks. Univariate logistic regression showed that prior history of malignancy, atrial fibrillation, preoperative high-level total cholesterol and low-density lipoprotein cholesterol (LDL-C), △ HR, △ QRS interval, △ QT interval, and △ QTc interval were risk factors of new-onset heart block after TAVR. Multivariate analysis showed that preoperative high-level LDL-C and △ QRS interval remained significant independent risk factors after adjusting for potential confounds. Conclusions: Heart block is the most common complication of TAVR, and its significant independent risk factors include high-level LDL-C and △ QRS interval.

Prognostic value of cardiovascular magnetic resonance in immune checkpoint inhibitor-associated myocarditis: A systematic review and meta-analysis.

Background: Immune checkpoint inhibitors (ICI) are increasingly used in the first-line treatment of malignant tumors. There is increasing recognition of their cardiotoxicity and, in particular, their potential to lead to myocarditis. Cardiovascular magnetic resonance (CMR) can quantify pathological changes, such as myocardial edema and fibrosis. The purpose of this systematic review and meta-analysis was to examine the evidence for the roles of CMR in predicting prognosis in ICI-associated myocarditis. Methods: PubMed, Cochrane Library, and Web of Science databases were searched until October 2023 for published works investigating the relationship between CMR parameters and adverse events in patients with ICI-associated myocarditis. The analysis included studies reporting the incidence of late gadolinium enhancement (LGE), T1 values, T2 values, and CMR-derived left ventricular ejection fraction (LVEF). Odds ratios (OR) and weighted mean differences (WMD) were combined for binary and continuous data, respectively. Newcastle-Ottawa Scale was used to assess the methodological quality of the included studies. Results: Five cohort studies were included (average age 65-68 years; 25.4% female). Of these, four studies were included in the meta-analysis of LGE-related findings. Patients with major adverse cardiovascular events (MACE) had a higher incidence of LGE compared with patients without MACE (OR = 4.18, 95% CI: 1.72-10.19, p = 0.002). A meta-analysis, incorporating data from two studies, showed that patients who developed MACE exhibited significantly higher T1 value (WMD = 36.16 ms, 95% CI: 21.43-50.89, p < 0.001) and lower LVEF (WMD = - 8.00%, 95% CI: -13.60 to -2.40, p = 0.005). Notably, T2 value (WMD = -0.23 ms, 95% CI: -1.86 to -1.39, p = 0.779) was not associated with MACE in patients with ICI-related myocarditis. Conclusions: LGE, T1 value, and LVEF measured by CMR imaging have potential prognostic value for long-term adverse events in patients with ICI-related myocarditis.

Automated valvular heart disease detection using heart sound with a deep learning algorithm.

Background: Insufficient clinicians' auscultation ability delays the diagnosis and treatment of valvular heart disease (VHD); artificial intelligence provides a solution to compensate for the insufficiency in auscultation ability by distinguishing between heart murmurs and normal heart sounds. However, whether artificial intelligence can automatically diagnose VHD remains unknown. Our objective was to use deep learning to process and compare raw heart sound data to identify patients with VHD requiring intervention. Methods: Heart sounds from patients with VHD and healthy controls were collected using an electronic stethoscope. Echocardiographic findings were used as the gold standard for this study. According to the chronological order of enrollment, the early-enrolled samples were used to train the deep learning model, and the late-enrollment samples were used to validate the results. Results: The final study population comprised 499 patients (354 in the algorithm training group and 145 in the result validation group). The sensitivity, specificity, and accuracy of the deep-learning model for identifying various VHDs ranged from 71.4 to 100.0%, 83.5-100.0%, and 84.1-100.0%, respectively; the best diagnostic performance was observed for mitral stenosis, with a sensitivity of 100.0% (31.0-100.0%), a specificity of 100% (96.7-100.0%), and an accuracy of 100% (97.5-100.0%). Conclusions: Based on raw heart sound data, the deep learning model effectively identifies patients with various types of VHD who require intervention and assists in the screening, diagnosis, and follow-up of VHD.

Association Between Atherosclerosis-Related Cardiovascular Disease and Uveitis: A Systematic Review and Meta-Analysis.

BACKGROUND: Uveitis is not only an intraocular inflammatory disease, but also an indicator of systemic inflammation. It is unclear whether uveitis can increase the risk of cardiovascular disease (CVD) through the atherosclerotic pathway. METHODS: PubMed and Embase databases were searched until 5 September, 2022. Original studies investigating uveitis and cardiovascular events were selected. The random-effects model was used to calculate the difference of groups in pooled estimates. RESULTS: A total of six observational studies that included mainly ankylosing spondylitis (AS) patients were included. Of these, three studies reported data on carotid plaques and carotid intima-media thickness (cIMT) and the other three studies provided data on atherosclerosis-related CVD. No significant difference was found in cIMT between uveitis and controls (MD = 0.01, 95% CI = -0.03-0.04, p = 0.66), consistent with the findings of carotid plaque incidence (OR = 1.30, 95% CI = 0.71-2.41, p = 0.39). However, uveitis was associated with a 1.49-fold increase in atherosclerosis-related CVD (HR = 1.49, 95% CI = 1.20-1.84, p = 0.0002). CONCLUSIONS: Uveitis is a predictor of atherosclerosis-related CVD in AS patients. For autoimmune disease patients with uveitis, earlier screening of cardiovascular risk factors and the implementation of corresponding prevention strategies may be associated with a better prognosis.