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Background: Contemporary data on trends in survival after incident atrial fibrillation (AF) are limited. Methods: Residents of a 27-county area in southern Minnesota and western Wisconsin with new-onset atrial fibrillation or atrial flutter between 2013 and 2023 were identified. An electronic algorithm requiring 1 inpatient code or 2 outpatient codes separated by >7 days but within 1 year was used to define AF. Patients were followed for all-cause mortality through March 31, 2025. Standardized mortality ratios of observed versus expected survival were calculated, and time trends in survival were examined using Cox regression adjusting for demographics, cardiovascular risk factors, comorbid conditions, and area-level measures of socioeconomic status and rurality. Results: There were 44,930 individuals identified with incident AF from 2013-2023 (56.4% male; mean (SD) age 72.8 (13.5) years; 95.8% were non-Hispanic white). Within the first 90 days, the risk of all-cause mortality was greatly elevated compared with individuals of a similar age and sex distribution in the general population (standardized mortality ratios 12.6, 95% CI, 12.1-13.0 and 3.8, 95% CI, 3.6-4.0 for the first 30 days and 31 to 90 days after diagnosis, respectively). This excess risk was observed for all age groups but was greatest in the youngest age group (standardized mortality ratios among 18-64 year olds for the first 30 days: 49.9, 95% CI, 44.6-55.7 and 31 to 90 days: 11.1, 95% CI: 9.4-13.1; Figure ). In the youngest age group, the excess risk persists through 5 years post AF, however, in the oldest age group the excess risk only persisted through 1 year. Survival within the first 90 days improved over the study period (adjusted hazard ratio [aHR], 0.88; 95% CI, 0.79-0.99 for 2023 vs 2013); likewise, among 90-day survivors, survival improved between 2013 and 2023 (aHR,0.82; 95% CI, 0.76-0.88). These trends were driven by improvements in the younger age groups, particularly for death among 90-day survivors (aHR for 18-64 years, 0.66, 95% CI, 0.52-0.83; 65-74 years: 0.77, 95% CI: 0.65-0.90; 75-84 years, 0.84, 95% CI: 0.75-0.95; 85+ years, 0.99, 95% CI: 0.98-1.01). Conclusion: In a Midwestern community, the excess risk of death is high within the first 90 days after incident AF. Furthermore, survival after AF has improved between 2013 and 2023, with the improvement greatest in younger age groups.

Background Multimorbidity is common in patients with atrial fibrillation (AF); however, the impact of the number and type of comorbid conditions on outcomes remains uncertain. Methods This cohort study included patients with new‐onset AF from a Midwest region between 2013 and 2017. Eighteen chronic conditions at the time of AF were classified into groups: cardiometabolic, other somatic, and mental health. Cox regression determined associations between the number of each condition type with death, ischemic stroke/transient ischemic attack, and congestive heart failure, stratified by age. Results Among 16 509 patients with AF (mean age, 74 years; 43% women), the mean number of cardiometabolic, other somatic, and mental health conditions was 2.7, 1.4, and 0.5, respectively. The number and type of conditions had a varying impact on outcomes and differed by age. A higher number of cardiometabolic conditions were associated with increased risk of death within 90 days only in people aged ≥85 years (≥4 conditions versus 0: hazard ratio [HR], 1.74 [95% CI, 1.05–2.87]), whereas for death after 90 days, associations were strongest in the youngest age group (<65 years: HR, 1.83 [95% CI, 1.35–2.46]; 65–74 years: HR, 1.34 [95% CI, 1.04–1.73]; 75–84 years: HR, 1.32 [95% CI, 1.09–1.60]; ≥85 years: HR, 1.14 [95% CI, 0.96–1.35]). Associations with outcomes were generally strongest in the youngest age group and attenuated with older age for higher number of other somatic conditions, whereas the pattern was less consistent for mental health conditions. Conclusions Along with cardiometabolic‐related conditions, other somatic and mental health conditions are important predictors of outcomes in AF, with effects differing by age, and should be considered when caring for these patients.

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.

Background: Atrial fibrillation (AF) is the most common sustained arrhythmia with prevalence increasing over the last decade. However, data on recent temporal trends in incidence of AF have been inconsistent, with some reports of increasing trends over time and others indicating no change in incidence over time. Therefore, we aimed to determine how the incidence of AF has changed over the last decade in a community in the Midwest USA. Methods: Between 2013 and 2023, adults (aged 18 and older) with incident AF were identified in a 27-county region in the Midwest. AF was identified using diagnostic codes by employing an electronic algorithm requiring 1 inpatient code or 2 outpatient codes separated by >7 days but within 1 year. For total rates, age - and sex-adjusted incidence rates were standardized to the 2010 US total population. Poisson regression was used to calculate the incidence rate ratio of AF for 2023 vs 2013. Results: We identified 44,930 individuals with incident AF from 2013 to 2023. Of these, 56.4% were male, the mean (SD) age was 72.8 (13.5) years, and 95.8% were non-Hispanic white. The age-and sex-adjusted incidence rates (per 1,000) and 95% confidence intervals standardized to the 2010 US total population were 5.24 (5.07-5.41) in 2013, 5.73 (5.56-5.91) in 2018, and 5.44 (5.28-5.61) in 2023. Although a drop in incidence was observed corresponding to the COVID-19 pandemic, a small increase in the overall incidence of AF was observed over time. The overall incidence rate ratio (95% CI) for 2023 vs 2013 was 1.07 (1.02-1.11), P=0.003. The incidence rates were higher in men compared to women and increased with older age ( Figure 1 ). However, differences in AF incidence by sex were greatest in the youngest age group and attenuated with older age (incidence rate ratio (95% CI): 2.18 (2.10-2.28) for ages 18-64 years, 1.76 (1.70-1.83) for ages 65-74 years, 1.38 (1.33-1.42) for ages 75-84 years, and 1.23 (1.18-1.28) for ages 85 years and older). Conclusion: In this 27-county region in the Midwest, although small in magnitude, a statistically significant increase in AF incidence was observed over time from 2013 to 2023. Furthermore, the incidence of AF was higher in men compared with women, especially in younger age groups.

Introduction: Cardiovascular risks following SARS-CoV-2 infection are incompletely defined. We evaluated evolving cardiovascular outcomes across COVID-19 variant eras using a large community-based cohort to inform risk stratification and clinical care. Methods: We conducted a retrospective cohort study of 162,471 adults with COVID-19 between March 2020 and December 2023 using the Rochester Epidemiology Project. Patients were stratified by variant era (Pre-Delta, Delta, and Omicron). Cardiovascular outcomes included major adverse cardiovascular events (MACE: myocardial infarction, stroke, death), ischemic and inflammatory heart disease, thrombotic events, and dysrhythmias. Diagnoses were identified using ICD-10 codes and validated in a subset. Cumulative incidence was estimated using Kaplan-Meier and competing risk models. Cox proportional hazards regression was used to assess differences by era and age. Results: MACE occurred in 4,922 patients, primarily in those ≥80 (2-year incidence: 17%). Event rates peaked within 30 days post-infection, with marked variation by variant. In patients ≥80, 30-day MACE risk was highest in the Pre-Delta (HR 3.85; 95% CI, 3.14–4.71) and Delta (HR 2.72; 95% CI, 2.13–3.47) eras versus Omicron. After 30 days, event rates fell and era differences narrowed. Thrombotic events (n=2,090; 1.4%) decreased across eras. Ischemic and inflammatory heart disease rates were stable. Myocarditis/pericarditis were rare (0.1%) without age trends. Dysrhythmias affected 6,765 patients (2-year incidence: 4.9%) and rose with age (2.0% in <40 vs. 19.9% in ≥80). Within 30 days, risk was higher in the Pre-Delta and Delta eras for age ≥40, especially ≥80 (Delta HR 3.08; 95% CI, 2.08–4.56) and 60–79 (HR 2.23; 95% CI, 1.75–2.85). Among those 40–59, Delta posed the highest risk (HR 2.74; 95% CI, 1.94–3.86 vs. Pre-Delta HR 1.77; 95% CI, 1.26–2.49). No differences were observed in <40. Beyond 30 days, risk converged across variants. Conclusion: Cardiovascular risk after SARS-CoV-2 infection was concentrated in the first 30 days, with attenuation thereafter. While MACE and thrombotic event rates declined over time, early dysrhythmia risk varied by variant and age, especially in older adults. These findings define a time-sensitive, variant-specific risk window and support short-term, risk-stratified post-COVID monitoring. This work informs targeted follow-up protocols by infection era and age, with implications for quality improvement, resource use, and equitable care.

Importance Sleep disordered breathing (SDB) is a well-established contributor to cardiovascular morbidity, mediated by intermittent hypoxemia, autonomic dysregulation, and endothelial dysfunction. Patients with hypertrophic cardiomyopathy (HCM) may be especially at risk for SDB, but the clinical impact of SDB in this population remains unclear. Objective To define the prevalence and subtypes of SDB in HCM and examine their association with echocardiographic parameters and cardiac biomarker expression. Design, Setting, and Participants A prospective cohort study was conducted between April 18, 2018, and January 15, 2024, at a single tertiary referral center specializing in HCM care. Adults with HCM (left ventricular wall thickness ≥15 mm or pathogenic variants) were recruited from an institutional registry. Patients diagnosed with SDB or current pregnancy were excluded. Patients underwent polysomnography, with comparative assessment of echocardiographic, electrocardiographic, and biomarker indices. Observers were blinded to polysomnographic results. Data analysis was performed from April 11, 2024, to July 25, 2024. Exposures SDB classified via polysomnography using apnea-hypopnea index thresholds, with subtypes including obstructive sleep apnea and central sleep apnea and with event severity, hypoxemia, and sleep architecture disruption quantified. Main Outcomes and Measures Echocardiographic indices, cardiac biomarker expression, functional status, apnea-hypopnea index, and overnight hypoxemia. Results Among 154 patients (median [IQR] age, 60 [48-68] years; 102 [66.2%] male), 91 (59.1%) were diagnosed with SDB. Those with SDB, compared with those without SDB, had higher left ventricular mass index (median [IQR], 128 [107-161] vs 109 [96-134] g/m 2 ; P = .03), E/e′ ratio (median [IQR], 12.5 [10.0-15.0] vs 10.0 [8.3-14.5]; P = .04), and baseline troponin-T level (median [IQR], 0.013 [0.009-0.022] vs 0.011 [0.007-0.015] ng/mL [to convert to micrograms per liter, multiply by 1]; P = .04) and greater overnight troponin-T level increases (change in median [IQR], 0.0021 [−0.0029 to 0.0062] vs 0.0002 [−0.0022 to 0.0026] ng/mL; P = .02). New York Heart Association class II or III symptoms were more common in those with SDB (48 [52.7%] vs 17 [27.0%]; P = .005). Hypertension and diabetes were more prevalent among patients with SDB than without SDB (hypertension: 67 [73.6%] vs 36 [57.1%]; P = .03; diabetes: 14 [15.4%] vs 3 [4.8%]; P = .04), whereas rates of atrial fibrillation and prior myectomy did not differ significantly between groups. Conclusions and Relevance This study suggests that undiagnosed SDB is highly prevalent in patients with HCM and that SDB is associated with adverse myocardial remodeling, greater diastolic dysfunction, and elevated troponin-T levels, indicating subclinical myocardial injury. SDB may contribute to HCM pathophysiology and symptom burden, supporting the rationale for randomized clinical trials to determine the impact of treating SDB on symptoms and clinical outcomes in patients with HCM.

Hypertension is the single most important modifiable risk factor for preventable disability and death worldwide and disproportionately affects socially disadvantaged populations. We face a paradox—blood pressure control is low and recent trends suggest it is even declining, despite the availability of inexpensive and effective therapies. A variety of barriers on the system, patient, and healthcare provider side hinder effective drug-based risk factor management. Clinical inertia represents a major barrier on the clinician side, as well as workload and limited education. Common barriers on the patient side include limited English proficiency, low health literacy, and nonadherence with misaligned incentives, limited resources, lack of structured clinical pathways, and reimbursement issues. New innovations in the field of RNA-targeted therapies and device-based interventions could prevent and potentially even cure diseases previously designated as chronic health conditions, such as hypertension. Such novel therapies could potentially overcome several major barriers to effective treatment, including nonadherence. Drug development of novel, long-acting treatments requires consideration of specific clinical trial design aspects, including safety collection, benefit: risk assessment, the development and assessment of novel, qualitative surrogate end points, such as time-in-therapeutic range, the use of representative trial settings as well as the definition of standard of care in placebo-controlled trials, which should be of reasonably high-quality allowing for credible evaluation of effectiveness. Here, we provide an overview on barriers to effective treatment and a framework for trials assessing novel treatments for cardiovascular disease risk factors, including early and broad implementation programs.

Background Multimorbidity is common in patients with atrial fibrillation (AF), yet comorbidity patterns are not well documented. Methods The prevalence of 18 chronic conditions (6 cardiometabolic, 7 other somatic, 5 mental health) was obtained in patients with new-onset AF from 2013-2017 from a 27-county region and controls matched 1:1 on age, sex, and county of residence. For AF patients and controls separately, clustering of conditions and co-occurrence beyond chance was estimated (using the asymmetric Somers’ D statistic), overall and for ages <65, 65-74, 75-84, and ≥85 years. Results Among 16,509 patients with AF (median age 76 years, 57% men), few (4%) did not have any of the 18 chronic conditions, whereas nearly one-quarter of controls (23%) did not have any chronic conditions. Clustering of cardiometabolic conditions was common in both AF patients and controls, but clustering of other somatic conditions was more common in AF. Although the prevalence of most condition pairs was higher in AF patients, controls had a larger number of condition pairs occurring together beyond chance. In persons aged <65 years, AF patients more frequently exhibited concordance of condition pairs that included either pairs of somatic conditions or a combination of conditions from different condition groups. In persons aged 65-74 years, AF patients more frequently had pairs of other somatic conditions. Conclusion Patterns of co-existing conditions differed between patients with AF and controls, particularly in younger ages. A better understanding of the clinical consequences of multimorbidity in AF patients, including those diagnosed at younger ages, is needed.

Abstract Artificial intelligence (AI)–enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can identify patterns predictive of future adverse cardiac events. We hypothesized that such an approach would provide prognostic information for the risk of cardiac complications and mortality in patients undergoing hematopoietic cell transplantation (HCT). We retrospectively subjected ECGs obtained before HCT to an externally trained, deep-learning model designed to predict the risk of atrial fibrillation (AF). Included were 1377 patients (849 autologous [auto] HCT and 528 allogeneic [allo] HCT recipients). The median follow-up was 2.9 years. The 3-year cumulative incidence of AF was 9% (95% confidence interval [CI], 7-12) in patients who underwent auto HCT and 13% (10%-16%) in patients who underwent allo HCT. In the entire cohort, pre-HCT AI-ECG estimate of AF risk correlated highly with the development of clinical AF (hazard ratio [HR], 7.37; 95% CI, 3.53-15.4; P &lt; .001), inferior survival (HR, 2.4; 95% CI, 1.3-4.5; P = .004), and greater risk of nonrelapse mortality (NRM; HR, 95% CI, 3.36; 1.39-8.13; P = .007), without increased risk of relapse. Association with mortality was only noted in allo HCT recipients, where the risk of NRM was greater. The use of cyclophosphamide after transplantation resulted in greater 90-day incidence of AF (13% vs 5%; P = .01) compared to calcineurin inhibitor–based graft-versus-host disease prophylaxis, corresponding to temporal changes in AI-ECG AF prediction after HCT. In summary, AI-ECG can inform risk of posttransplant cardiac outcomes and survival in HCT patients and represents a novel strategy for personalized risk assessment.