Pereyra Pietri, Milagros

Key Points Atrial fibrillation after kidney transplantation is common and is associated with poor prognosis. Artificial intelligence applied to 12-lead electrocardiograms pretransplant accurately predicts the risk of new-onset atrial fibrillation in our cohort. High-risk artificial intelligence electrocardiography score was independently associated with increased risk of mortality and allograft failure. Background Incident atrial fibrillation (AF) is common after kidney transplantation (KTx) and is associated with worse clinical outcomes. Artificial intelligence electrocardiography (AI-ECG) algorithms have demonstrated efficacy in predicting risk of new-onset AF in the general population; however, their prognostic value in KTx recipients is relatively unknown. Methods Retrospective analysis was conducted on KTx recipients without AF, with at least one pretransplant ECG between 2011 and 2021 across three tertiary centers in the United States (Mayo Clinic sites in MN, AZ, and FL). A previously validated AI-ECG algorithm estimated the probability of incident AF for each patient. Based on AI-ECG probabilities, patients were categorized into high-risk and low-risk groups, with the optimal AI-ECG score cutoff determined. The incidence of new-onset AF, allograft failure, and mortality was compared between groups. Results Overall, 6246 patients (age 53.5±13.8 years; 58.9% male) were included. Pretransplant AI-ECG probability of AF ≥5% was the optimal cutoff for high risk of incident AF (sensitivity 72%, specificity 62%). High-risk scores were associated with true new-onset AF at 30 days (adjusted hazard ratio [aHR], 2.89; 95% confidence intervals [CI], 2.05 to 4.09; P < 0.001), 3 years (aHR, 2.54; 95% CI, 1.99 to 3.26, P < 0.001), and 5 years post-transplant (aHR, 2.48; 95% CI, 1.99 to 3.09, P < 0.001). High-risk AI-ECG scores were also associated with increased mortality (aHR, 1.56; 95% CI, 1.30 to 1.88, P < 0.001) and overall allograft failure (aHR, 1.50; 95% CI, 1.30 to 1.75, P < 0.001) through the 5-year follow-up. Conclusions This pretransplant AI-ECG parameter identified patients at increased risk of new-onset AF post-KTx and provided prognostic utility. Overall, this easy to obtain tool allows for risk stratification of patients who may benefit from closer monitoring, targeted risk factor modification, and early intervention.

Abstract Aims Significant advancements have been made in the management of obstructive hypertrophic cardiomyopathy (oHCM), yet the extent of left ventricular outflow tract (LVOT) gradient reduction achieved with commonly used pharmacological therapies [beta-blockers (BBs), calcium channel blockers (CCBs), disopyramide, and cardiac myosin inhibitors (CMIs)] relative to each other is still unclear. Methods and results PubMed and Scopus were searched up to September 2024. Clinical trials or observational studies that assessed the changes associated with BBs, CCBs, disopyramide, or CMIs in LVOT gradient at rest or with provocation in patients with oHCM were included. Mean changes in LVOT gradients were pooled as mean differences (MD) with 95% confidence intervals (CIs) in a random-effects model. Thirty-seven studies, with 44 arms and 1898 patients, were included in the analysis. At the therapeutic class level, pooled analysis showed that disopyramide was associated with the highest reduction in LVOT gradient at rest [MD: −43.5 (95% CI, −51.6 to −35.3)], followed by CMIs [MD: −34.8 (95% CI, −40.6 to −29.0)], BBs [MD: −20.7 (95% CI, −29.4 to −12.0)], and then CCBs [MD: −14.7 (95% CI, −23.3 to −6.1)], inter-action P &lt; 0.01. Within CMIs, mavacamten had a higher effect than aficamten on gradient reduction; among the included BBs, metoprolol showed the highest gradient reduction, while among CCBs, verapamil was the most effective (inter-action P &lt; 0.01). Similar results were observed for provocable LVOT gradients. Conclusion Pharmacological therapies effectively reduced LVOT gradients in oHCM patients to varying degrees, with disopyramide and CMIs showing the highest effect, followed by BBs and CCBs.

Abstract Aims Atrial fibrillation (AF) is the most common cardiac arrhythmia. Although lipoprotein(a) [Lp(a)] is known to be a well-established risk factor for atherosclerotic cardiovascular disease, its role in the development of AF, independent of this association, remains unclear. Methods and results Adult patients from the three Mayo Clinic sites with a baseline Lp(a) and without AF history were included. Patients were categorized into two groups based on their Lp(a) levels: high Lp(a) (≥50 mg/dL) and low Lp(a) (&lt;50 mg/dL). Survival probabilities free from incident AF were compared between Lp(a) groups, during a follow-up period up to 15 years, using the Kaplan–Meier curve and the log-rank test. Multivariable Cox regression analysis was also conducted. A total of 75 376 patients were included (median age: 55 years, 59% males), with a median follow-up duration of 8.8 (inter-quartile range: 3.4, 14.8) years. Incident AF was detected in 5738 (7.6%) patients. Survival probability free from incident AF was significantly lower in patients with elevated Lp(a) (86%) compared with those with low Lp(a) (88%, log-rank P &lt; 0.001). Multivariable analysis adjusted for potential risk factors of AF showed a statistically significant association of elevated Lp(a) with an 11% increase in AF risk (adjusted hazard ratio: 1.11, 95% confidence interval: 1.05–1.18). Conclusion Our study suggests that elevated Lp(a) (≥50 mg/dL) is an independent risk factor for incident AF. Future prospective studies are warranted to validate our results and to test if reducing Lp(a) could mitigate the burden of AF.