Gersh, Bernard J.

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.

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.

Background The study aimed to describe the patterns and trends of initiation, discontinuation, and adherence of oral anticoagulation (OAC) in patients with new‐onset postoperative atrial fibrillation (POAF), and compare with patients newly diagnosed with non‐POAF. Methods and Results This retrospective cohort study identified patients newly diagnosed with atrial fibrillation or flutter between 2012 and 2021 using administrative claims data from OptumLabs Data Warehouse. The POAF cohort included 118 366 patients newly diagnosed with atrial fibrillation or flutter within 30 days after surgery. The non‐POAF cohort included the remaining 315 832 patients who were newly diagnosed with atrial fibrillation or flutter but not within 30 days after a surgery. OAC initiation increased from 28.9% to 44.0% from 2012 to 2021 in POAF, and 37.8% to 59.9% in non‐POAF; 12‐month medication adherence increased from 47.0% to 61.8% in POAF, and 59.7% to 70.4% in non‐POAF. The median time to OAC discontinuation was 177 days for POAF, and 242 days for non‐POAF. Patients who saw a cardiologist within 90 days of the first atrial fibrillation or flutter diagnosis, regardless of POAF or non‐POAF, were more likely to initiate OAC (odds ratio, 2.92 [95% CI, 2.87–2.98]; P <0.0001), adhere to OAC (odds ratio, 1.08 [95% CI, 1.04–1.13]; P <0.0001), and less likely to discontinue (odds ratio, 0.83 [95% CI, 0.82–0.85]; P <0.0001) than patients who saw a surgeon or other specialties. Conclusions The use of and adherence to OAC were higher in non‐POAF patients than in POAF patients, but they increased over time in both groups. Patients managed by cardiologists were more likely to use and adhere to OAC, regardless of POAF or non‐POAF.

Abstract An integral component of the practice of medicine is focused on the initiation of medications, based on clinical practice guidelines and underlying trial evidence, which usually test the addition of novel medications intended for life-long use in short-term clinical trials. Much less attention is given to the question of medication discontinuation, especially after a lengthy period of treatment, during which patients age gets older and diseases may either progress or new diseases may emerge. Given the paucity of data, clinical practice guidelines offer little to no guidance on when and how to deprescribe cardiovascular medications. Such decisions are often left to the discretion of clinicians, who, together with their patients, express concern of potential adverse effects of medication discontinuation. Even in the absence of adverse effects, the continuation of medications without any proven effect may cause harm due to drug–drug interactions, the emergence of polypharmacy, and additional preventable spending to already strained health systems. Herein, several cardiovascular medications or medication classes are discussed that in the opinion of this author group should generally be discontinued, either for the prevention of potential harm, for a lack of benefit, or for the availability of better alternatives.