Citations

Abstract Aims Artificial intelligence electrocardiography (AI-ECG) algorithms are emerging tools for identifying individuals at risk of atrial fibrillation (AF). We evaluated the predictive performance of a validated AI-ECG algorithm for incident AF in UK Biobank participants with AF risk factors, irrespective of prevalent cardiovascular disease, and its incremental value when added to clinical predictors. Methods and results The AI-ECG tool was applied to sinus rhythm ECGs from UK-Biobank participants with risk factors for AF but no AF. Model performance was evaluated using time-dependent ROC-AUC and Harrell’s C-index. Multivariable Cox regression was used to identify clinical risk factors associated with incident AF and to quantify the contribution of AI-ECG. A total of 21 842 participants (56% male) were included. The median follow-up time was 3.7 years (IQR 0.5–5.4) The ECG-AI tool achieved a ROC-AUC of 0.73 (95% CI 0.68–0.78) at 1 and 0.69 (95% CI 0.66–0.72) at 3 years. A multivariable Cox regression model using clinical parameters achieved a ROC-AUC of 0.71 (95% CI 0.66–0.75) at 1 and 0.71 (95% CI 0.68–0.74) at 3 years. By adding ECG-AI to the clinical Cox regression model, the ROC-AUC increased to 0.75 (95%CI 0.71–0.80) at 1 and 0.74 (95% CI 0.71–0.77) at 3 years. AI-ECG showed a hazard ratio of 1.23 per decile increase (95% CI 1.18–1.27). Conclusion An AI-ECG algorithm improved the prediction of incident AF when added to a clinical parameter-based model over a median follow-up time of 3.7 years among individuals with comorbidities predisposing to AF who may benefit from targeted screening and preventive strategies.

Abstract: Autosomal dominant polycystic kidney disease (ADPKD) is a common, inherited disorder characterized by the progressive enlargement of fluid-filled cysts in the kidneys and liver. Since the cystic fibrosis transmembrane conductance regulator (CFTR) Cl– channel may mediate the secretion of Cl– and fluid into the cysts, it is conceivable that coexisting cystic fibrosis (CF) in patients with ADPKD could attenuate their development. We previously reported that two patients with ADPKD coexisting with cystic fibrosis (CF) had a milder cystic phenotype than that of kindred without CFTR mutations. A subsequent report failed to confirm this protective effect. We now have identified another family with coexisting type 1 ADPKD and CF. The kidney volumes and the number and size of renal and hepatic cysts were markedly less in a member of this family with ADPKD (PKD1 mutation C508R) and CF (homozygous ΔF508 mutation) than in her sister with ADPKD alone at comparable ages.

BACKGROUND: Bicuspid aortic valve (BAV) is a frequent congenital heart defect with a high heritability. Despite this, only a limited number of genes have been associated with the disease, and the molecular mechanisms remain unexplained in most cases. This study aimed to further understand the genetic architecture of BAV. METHODS: A genome-wide association study meta-analysis including 9631 cases among 65 677 participants was performed. Genes were prioritized using transcriptomic analyses based on RNA sequencing in relevant tissues, including human fetal and adult aortic valves. The impact of the knockdown or knockout of 4 candidate genes on cardiac development was verified in zebrafish. A polygenic risk score was developed, its association with BAV was evaluated in an independent cohort, and its association with a wide range of phenotypes (n=976) was evaluated in UK Biobank (n=355 618 individuals). RESULTS: Thirty-six genomic loci were identified, including 32 that were not described previously. Among the prioritized genes, KANK2 and ERBB4 were identified as potentially causal through transcriptomic analyses, colocalization, and Mendelian randomization based on gene expression in human aortic valves (n=484), whereas PRDM6 and STRN were prioritized using similar analyses from aortic (n=326) and left ventricular tissues (n=326), respectively. Targeting 4 candidate genes ( WNT4 , LEF1 , STRN , and KANK2 ) in zebrafish led to disruption in cardiac development. A polygenic risk score was associated with an odds ratio of 2.07 (95% CI, 1.90–2.25; P =5.43×10 -62 ) per SD for BAV and significantly associated with thoracic aortic aneurysm and atrial fibrillation in UK Biobank. CONCLUSIONS: This study supports a significant polygenic contribution to BAV, where the combination of multiple common variants in genes involved in heart morphogenesis disrupts aortic valve development.

Abstract Introduction Atrial fibrillation (AF) is a major contributor to stroke risk, yet many cases remain undiagnosed due to its silent nature. Deep learning-based electrocardiogram (DL-ECG) analysis can streamline AF risk stratification, providing a more reliable and accessible method to stratify risk using only a standard resting ECG. However, this tool has been validated only on AF detected clinically or with short ECG event monitors. Purpose This study aimed to validate the predictive accuracy of a DL-ECG model for identifying AF in normal sinus rhythm (NSR) ECGs among patients undergoing continuous cardiac monitoring with implantable loop recorders (ILRs). Methods We conducted a historical cohort study across three major U.S. academic medical centers, enrolling patients with an ILR and no prior diagnosis or documented ECG evidence of AF. Pre-implantation 10-second, 12-lead ECGs in NSR were analyzed using the DL-ECG model. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with the CHARGE-AF score. Kaplan-Meier survival analysis and Cox proportional hazards models assessed the association between DL-ECG risk categories and incident AF, defined as any episode lasting ≥6 minutes. Results We included 2,523 patients (mean age 66 ± 17 years, 52% male). AF was detected in 658 (26%) over a median follow-up of 19 months (IQR 8-37), Table 1. The DL-ECG model classified 1,634 patients (65%) as at low-risk and 889 (35%) as at high-risk. AF developed in 242 (14.8%) of the low-risk group and 416 (46.8%) of the high-risk group, yielding an AUC of 0.73 (95% CI 0.70-0.75), surpassing the CHARGE-AF AUC of 0.69 (95% CI 0.66-0.71). A combined DL-ECG and CHARGE-AF model yielded an AUC of 0.74 (95% CI 0.72-0.76), Figure 1 presents the AUC, Net Reclassification Index, and DeLong test comparisons among the three models. High-risk patients had a significantly higher incidence of AF, which remained significant after adjusting for CHARGE-AF variables, Figure 2. AF incidence increased as DL-ECG quartiles increased, with 61% of patients at the top quartile developing AF during follow-up, Figure 3. False negatives (patients initially classified as low-risk but in whom later AF was detected) had a longer median time to AF detection (127 days [IQR 28-425] vs. 101 days [IQR 20-318] for true positives, p = 0.01), and where found to have at least one positive ECG during follow-up in 36%, compared to 32% of true negatives, indicating eventual risk reclassification. Conclusions Among patients undergoing ILR monitoring, the DL-ECG model demonstrated superior predictive performance compared to CHARGE-AF, offering a promising and accessible alternative for early detection of silent AF. This approach could aid early AF detection, particularly in resource-limited settings where access to specialized rhythm monitoring services is limited.Table 1 and Figure 1 Figure 2 and 3

Abstract Background Left ventricular diastolic function (LVDF) and left bundle branch block (LBBB) are both linked with increased risk of LV systolic dysfunction, heart failure, and mortality. Assessing LVDF, which is typically done through echocardiography, becomes challenging in the presence of LBBB due to the intraventricular conduction delay that disrupts the reliability of tissue Doppler imaging. Recently, we validated an artificial intelligence-enabled electrocardiogram (AI-ECG) model to assess LVDF and filling pressures. Methods We performed a retrospective study among patients with ECG-confirmed LBBB who also underwent comprehensive echocardiography within 14 days of the index ECG between September 2001 and June 2022. Using multivariable survival models, we assessed the risk of all-cause mortality across AI-ECG-derived LVDF grades. Results Of 2,554 patients with LBBB (mean age 72.0+12.6, 54.6% women), 252 (9.8%), 656 (25.7%), and 1,436 (56.2%), and 210 (8.2%) had normal LVDF, G1 LVDF, G2 LVDF, and G3 LVDF, respectively. The median LV ejection fraction (LVEF) was 54% and 49.3% of the patients had heart failure. Worse LVDF was associated with older age, lower LVEF, and higher rates of diabetes, hypertension, chronic pulmonary disease, renal failure, cardiovascular disease, and heart failure (p-of-trend<0.001 for all). During a median follow-up of 2.6 years (interquartile range 0.5-4.0 years), 847 (33.2%) patients died. In multivariable survival analysis, adjusted to multiple risk factors including baseline LVEF and heart failure, worse LVDF was independently associated with a higher risk of death [G1 LVDF: adjusted hazard ratio (aHR) 2.15 (95% confidence interval (95%CI) 0.75-1.77), G2 LVDF: aHR 2.25 (95%CI 1.50-3.38) G3 LVDF: aHR 2.96 (95%CI 1.88-4.65); p<0.001 for both)]. These findings were consistent also in a subgroup of patients with baseline LVEF > 50%. Conclusion Among patients with LBBB, worse LVDF, as derived by a dedicated AI-ECG algorithm is independently associated with all-cause mortality beyond baseline LVEF and clinical heart failure.survival across AI-ECG LVDF grades

Abstract Background Artificial intelligence (AI) models trained on 12-lead ECGs effectively detect left ventricular systolic dysfunction (LVSD; left ventricular ejection fraction [LVEF] <=40%). Continuous ECG monitoring via Holter recordings provides an opportunity for opportunistic screening for structural heart disease beyond rhythm disorders. We hypothesized that a lead-invariant version of the 12-lead AI model would enable a Holter monitor to screen for both arrhythmias and ventricular dysfunction. Methods We retrospectively analyzed continuous Holter ECGs from 17,665 patients who underwent a Holter and transthoracic echocardiogram (TTE) within 30 days of each other at Mayo Clinic. From each Holter, a random 20-minute of valid (non-flatline/lead disconnect) ECG segment was extracted and analyzed for LVSD detection using the adapted lead-invariant AI model. To evaluate stability, we examined model performance across different time points of the day, presenting results as area under the receiver operating characteristic curve (AUC) over time. Moreover, we illustrated the model’s robustness to noisy data by comparing its performance on raw ECG signals with that on bandpass-filtered inputs. Results Among 17,665 patients (mean age 59 years, 48.57% female), 4.96% had an LVEF <=40%. The AI model demonstrated strong predictive performance (20-minute segment AUC 0.90, mean prediction of 24-hour AUC 0.92). Analysis of results over time (Figure) revealed temporal patterns in predictive accuracy, with specific time periods showing greater stability. Despite modest variability, model performance remained consistently high throughout the day, confirming robustness across different physiological states. The predictions remained robust with noisy input. We did not observe performance improvement when the baseline wander and high frequency noise are removed by the bandpass filter. Conclusion Applying a 12-lead AI ECG model with a lead-invariant framework to a continuous Holter ECG enables effective screening for left ventricular dysfunction. This suggests that AI-based analysis of Holter-monitors can facilitate opportunistic screening of ventricular dysfunction and may enable assessment of an arrhythmia’s impact on LVEF, as well as the relationship between arrhythmia burden and LVEF.Figure 1.Mean prediction AUC of the day Figure 2.AUC for different time point

Background and Aims: Cholangiocarcinoma (CCA) is a rare but aggressive malignancy with poorly understood genetic susceptibility. To date, genome-wide association studies (GWAS) investigating germline variants associated with CCA risk remain limited. We aimed to identify genetic risk loci for CCA and its clinical subtypes through comprehensive GWAS and post-GWAS analyses. Approach and Results: We conducted a GWAS of 2366 CCA cases and 11,750 controls of European ancestry. Genome-wide significant loci ( p <5×10 −8 ) were identified and further examined through fine-mapping, functional annotation, and HLA imputation. Subgroup analyses were conducted by CCA subtypes and primary sclerosing cholangitis (PSC) status. Cross-trait linkage disequilibrium score regression and Mendelian randomization were employed to investigate the shared genetic architecture and potential causal relationships with a diverse range of traits. We identified 1 new genome-wide significant variant, rs535777 (OR=1.44), near HLA-DRB1/DQA1 associated with CCA, and 2 variants associated with extrahepatic CCA: rs116224263 (OR=0.17) in LINC02506 at 4p15.1 and rs6914950 (OR=1.63) near HLA-DRB1/DQB1 . Stratified analyses revealed rs2395184 (OR=3.51) near HLA-DRA/DRB5 associated with PSC-related CCA, and rs142674434 (OR=2.98) in THSD7A at 7p21.3 associated with non-PSC-related CCA. HLA imputation uncovered new amino acid residues associated with disease risk. Cross-trait analyses identified shared genetic signals between CCA and anthropometric, lipidemic, lifestyle, and medical traits. Mendelian randomization supported putative causal associations for 12 traits with CCA or its subtypes. Conclusions: Our large-scale GWAS highlights new genetic variants and HLA-linked mechanisms underlying CCA susceptibility. Integrating multi-step post-GWAS approaches enhances understanding of CCA pathogenesis and may facilitate the development of risk biomarkers for early detection and precision prevention strategies.

Objective Age of symptom onset is highly variable in familial frontotemporal lobar degeneration (f‐FTLD). Accurate prediction of onset would inform clinical management and trial enrollment. Prior studies indicate that individualized maps of brain atrophy can predict conversion to dementia in f‐FTLD. We used a Bayesian linear mixed‐effect (BLME) prediction method for identifying accelerated brain volume loss to predict conversion to dementia. Methods Participants included 234 asymptomatic or prodromal carriers of C9orf72 , GRN , or MAPT mutations (including 21 dementia converters) with ≥3 longitudinal magnetic resonance imaging (MRI) T1‐weighted scans. The BLME models established individual voxel‐wise gray matter trajectories using the first 2 scans. Person‐specific clusters of accelerated volume loss were estimated in subsequent scans and tested as predictors of dementia conversion compared with other approaches in time‐varying Cox proportional hazard models covarying for age. Receiver‐operating characteristic (ROC) curves estimated utility of cluster volume in discriminating which participants converted to dementia within 24 months. Results The BLME cluster volume predicted conversion to dementia in f‐FTLD mutation carriers overall and separately in C9orf72 , GRN , and MAPT , with comparable hazard ratios observed for atrophy W‐maps and regional volumes. Within a 24‐month timeframe, BLME cluster volume discriminated dementia converters from non‐converters with larger areas under the curve (AUCs) than other approaches. Interpretation Bayesian‐modeled individualized atrophy scores predict dementia progression among asymptomatic f‐FTLD mutation carriers and may have increased utility compared with other structural imaging methods when studying individuals over shorter timeframes that align with clinical trial design. ANN NEUROL 20269999:n/a–n/a