Screening for Unrecognized HFpEF in Atrial Fibrillation and for Unrecognized Atrial Fibrillation in HFpEF.

Because of the bidirectional relationship between atrial fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF), individuals with either condition require consideration of screening for the other. In this review, we summarize current evidence and rationale for screening for occult HFpEF in adults with clinical AF; and occult AF in patients with clinically recognized HFpEF. Assessment of pretest probability for occult HFpEF in symptomatic AF patients may help guide additional testing such as exercise right heart catheterization to diagnose HFpEF and guide HFpEF-specific therapies. In patients with HFpEF, AF screening will identify cases of occult AF where anticoagulation may decrease stroke risk, and correlation of previously unknown AF episodes with paroxysmal symptoms may prompt consideration for rhythm control. Therefore, screening may help clinicians understand the etiology of the often-overlapping symptoms, and it may help guide treatments to slow progression of both conditions and their complications.

Rationale, design, and participant characteristics of the FAITH! Heart health+ study: An exploration of the influence of the social determinants of health, stress, and structural racism on African American cardiovascular health.

BACKGROUND: African Americans (AAs) face cardiovascular health (CVH) disparities linked to systemic racism. The 2020 police killing of Mr. George Floyd in Minneapolis, Minnesota, alongside the COVID-19 pandemic, exacerbated adverse psychosocial factors affecting CVH outcomes among AAs. This manuscript describes the study protocol and participant characteristics in an ancillary study exploring the relationship between biopsychosocial factors and CVH among AAs. METHODS: Using a community-based participatory approach, a mixed-methods ancillary study of 58 AA participants from an overarching randomized control trial (RCT) was conducted. Baseline RCT health assessments (November 2020) provided sociodemographic, medical, and clinical data. Subsequent health assessments (February-December 2022) measured sleep quality, psychosocial factors (e.g., high-effort coping), biomarkers (e.g., cortisol), and cardiovascular diagnostics (e.g., cardio-ankle vascular index). CVH was assessed using the American Heart Association Life's Simple 7 (LS7) (range 0 to 14, poor to ideal) and Life's Essential 8 (LE8) scores (range 0 to 100, low to high). Correlations between these scores will be examined. Focus group discussions via videoconferencing (March to April 2022) assessed psychosocial and structural barriers, along with the impact of COVID-19 and George Floyd's killing on daily life. RESULTS: Participants were predominantly female (67%), with a mean age of 54.6 [11.9] years, high cardiometabolic risk (93% had overweight/obesity and 70% hypertension), and moderate LE8 scores (mean 56.9, SD 11.6). CONCLUSION: This study will enhance understanding of the associations between biopsychosocial factors and CVH among AAs in Minnesota. Findings may inform risk estimation, patient care, and healthcare policies to address CVD disparities in marginalized populations.

Artificial Intelligence Electrocardiography to Predict Atrial Fibrillation in Patients With Chronic Lymphocytic Leukemia.

Background: The use of an artificial intelligence electrocardiography (AI-ECG) algorithm has demonstrated its reliability in predicting the risk of atrial fibrillation (AF) within the general population. Objectives: This study aimed to determine the effectiveness of the AI-ECG score in identifying patients with chronic lymphocytic leukemia (CLL) who are at high risk of developing AF. Methods: We estimated the probability of AF based on AI-ECG among patients with CLL extracted from the Mayo Clinic CLL database. Additionally, we computed the Mayo Clinic CLL AF risk score and determined its ability to predict AF. Results: Among 754 newly diagnosed patients with CLL, 71.4% were male (median age = 69 years). The median baseline AI-ECG score was 0.02 (range = 0-0.93), with a value ≥0.1 indicating high risk. Over a median follow-up of 5.8 years, the estimated 10-year cumulative risk of AF was 26.1%. Patients with an AI-ECG score of ≥0.1 had a significantly higher risk of AF (HR: 3.9; 95% CI: 2.6-5.7; P < 0.001). This heightened risk remained significant (HR: 2.5; 95% CI: 1.6-3.9; P < 0.001) even after adjusting for the Mayo CLL AF risk score, heart failure, chronic kidney disease, and CLL therapy. In a second cohort of CLL patients treated with a Bruton tyrosine kinase inhibitor (n = 220), a pretreatment AI-ECG score ≥0.1 showed a nonsignificant increase in the risk of AF (HR: 1.7; 95% CI: 0.8-3.6; P = 0.19). Conclusions: An AI-ECG algorithm, in conjunction with the Mayo CLL AF risk score, can predict the risk of AF in patients with newly diagnosed CLL. Additional studies are needed to determine the role of AI-ECG in predicting AF risk in CLL patients treated with a Bruton tyrosine kinase inhibitor.

Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.

Aims: Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results: We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion: The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.

Sudden Cardiac Death or Ventricular Arrythmia in Patients Taking Levetiracetam or Oxcarbazepine.

BACKGROUND AND OBJECTIVES: Levetiracetam is a widely used antiseizure medication. Recent concerns have been raised regarding the potential prolongation of the QT interval by levetiracetam and increased risk of sudden cardiac death. This could have profound implications for patient safety and for prescribing practice. This study assessed the potential association of levetiracetam with cardiac outcomes related to QT interval prolongation. We compared outcomes of patients taking levetiracetam with those taking oxcarbazepine as a comparator medication that has not been associated with prolongation of the QT interval. METHODS: The sample included patients who were newly prescribed levetiracetam or oxcarbazepine from January 31, 2010, to December 31, 2019, using administrative claims data from the OptumLabs Data Warehouse (OLDW). The analysis focused on a combined endpoint of sudden cardiac death or ventricular arrythmia, which are both linked to QT interval prolongation. We used a new user design and selected oxcarbazepine as an active comparator with levetiracetam to minimize bias. We used propensity score weighting to balance the levetiracetam and oxcarbazepine cohorts and then performed weighted Cox regressions to evaluate the association of levetiracetam with the combined endpoint. RESULTS: We identified 104,655 enrollees taking levetiracetam and 39,596 enrollees taking oxcarbazepine. At baseline, enrollees taking levetiracetam were older, more likely to have diagnosed epilepsy, and more likely to have diagnosed comorbidities including hypertension, cerebrovascular disease, and coronary artery disease. In the main analysis, we found no significant difference between levetiracetam and oxcarbazepine in the rate of the combined endpoint for the Cox proportional hazards model (hazard ratio [HR] 0.79, 95% CI 0.42-1.47) or Cox regression with time-varying characteristics (HR 0.78, 95% CI 0.41-1.50). DISCUSSION: When compared with oxcarbazepine, levetiracetam does not correlate with increased risk of ventricular arrythmia and sudden cardiac death. Our finding does not support the concern for cardiac risk to indicate restriction of levetiracetam use nor the requirement of cardiac monitoring when using it. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that sudden cardiac death and ventricular arrythmia are not more frequent in patients older than 17 years newly prescribed levetiracetam, compared with those prescribed oxcarbazepine.

First-in-man report of transsubclavian venous implantation of the Aveir leadless cardiac pacing system.

INTRODUCTION: Transsubclavian venous implantation of the Aveir leadless cardiac pacemaker (LCP) has not been previously reported. METHODS AND RESULTS: Three cases of transsubclavian implantation of the Aveir LCP are reported. Two cases were postbilateral orthotopic lung transplant, without appropriate femoral or jugular access due to recent ECMO cannulation and jugular central venous catheters. In one case, there was strong patient preference for same-day discharge. Stability testing confirmed adequate fixation and electrical testing confirmed stable parameters in all cases. All patients tolerated the procedure well without significant immediate complications. CONCLUSIONS: We demonstrate the feasibility of transsubclavian implantation of the Aveir LCP.

Artificial intelligence-enabled electrocardiogram (AI-ECG) does not predict atrial fibrillation following patent foramen ovale closure.

Background: Atrial fibrillation (AF) is a known complication following patent foramen ovale (PFO) closure. AI-enabled ECG (AI-ECG) acquired during normal sinus rhythm has been shown to identify individuals with AF by noting high-risk ECG features invisible to the human eye. We sought to characterize the value of AI-ECG in predicting AF development following PFO closure and investigate key clinical and procedural characteristics possibly associated with post-procedural AF. Methods: We performed a retrospective analysis of patients who underwent PFO closure at our hospital from January 2011 to December 2022. We recorded the probability (%) of AF using the Mayo Clinic AI-ECG dashboard from pre- and post-procedure ECGs. The cut-off point of ≥ 11 %, which was found to optimally balance sensitivity and specificity in the original derivation paper (the Youden index) was used to label an AI-ECG "positive" for AF. Pre-procedural transesophageal echocardiography (TEE) and pre- and post-procedure transcranial doppler (TCD) data was also recorded. Results: Out of 93 patients, 49 (53 %) were male, mean age was 55 ± 15 years with mean post-procedure follow up of 29 ± 3 months. Indication for PFO closure in 69 (74 %) patients was for secondary prevention of transient ischemic attack (TIA) and/or stroke. Twenty patients (22 %) developed paroxysmal AF post-procedure, with the majority within the first month post-procedure (15 patients, 75 %). Patients who developed AF were not significantly more likely to have a positive post-procedure AI-ECG than those who did not develop AF (30 % AF vs 27 % no AF, p = 0.8).Based on the PFO-Associated Stroke Causal Likelihood (PASCAL) classification, patients who had PFO closure for secondary prevention of TIA and/or stroke in the "possible" group were significantly more likely to develop AF than patients in "probable" and "unlikely" groups (p = 0.034). AF-developing patients were more likely to have post-procedure implantable loop recorder (ILR) (55 % vs 9.6 %, p < 0.001), and longer duration of ILR monitoring (121 vs 92.5 weeks, p = 0.035). There were no significant differences in TCD and TEE characteristics, device type, or device size between those who developed AF vs those who did not. Conclusions: In this small, retrospective study, AI-ECG did not accurately distinguish patients who developed AF post-PFO closure from those who did not. Although AI-ECG has emerged as a valuable tool for risk prediction of AF, extrapolation of its performance to procedural settings such as PFO closure requires further investigation.

Predictive Value of Artificial Intelligence-Enabled Electrocardiography in Patients With Takotsubo Cardiomyopathy.

BACKGROUND: Recent studies have indicated high rates of future major adverse cardiovascular events in patients with Takotsubo cardiomyopathy (TC), but there is no well-established tool for risk stratification. This study sought to evaluate the prognostic value of several artificial intelligence-augmented ECG (AI-ECG) algorithms in patients with TC. METHODS AND RESULTS: This study examined consecutive patients in the prospective and observational Mayo Clinic Takotsubo syndrome registry. Several previously validated AI-ECG algorithms were used for the estimation of ECG- age, probability of low ejection fraction, and probability of atrial fibrillation. Multivariable models were constructed to evaluate the association of AI-ECG and other clinical characteristics with major adverse cardiac events, defined as cardiovascular death, recurrence of TC, nonfatal myocardial infarction, hospitalization for congestive heart failure, and stroke. In the final analysis, 305 patients with TC were studied over a median follow-up of 4.8 years. Patients with future major adverse cardiac events were more likely to be older, have a history of hypertension, congestive heart failure, worse renal function, as well as high-risk AI-ECG findings compared with those without. Multivariable Cox proportional hazards analysis indicated that the presence of 2 or 3 high-risk findings detected by AI-ECG remained a significant predictor of major adverse cardiac events in patients with TC after adjustment by conventional risk factors (hazard ratio, 4.419 [95% CI, 1.833-10.66], P=0.001). CONCLUSIONS: The combined use of AI-ECG algorithms derived from a single 12-lead ECG might detect subtle underlying patterns associated with worse outcomes in patients with TC. This approach might be beneficial for stratifying high-risk patients with TC.

Regional differences in outcomes with ablation versus drug therapy for atrial fibrillation: Results from the CABANA trial.

BACKGROUND: The finding of unexpected variations in treatment benefits by geographic region in international clinical trials raises complex questions about the interpretation and generalizability of trial findings. We observed such geographical variations in outcome and in the effectiveness of atrial fibrillation (AF) ablation versus drug therapy in the Catheter Ablation vs Antiarrhythmic Drug Therapy for Atrial Fibrillation (CABANA) trial. This paper describes these differences and investigates potential causes. METHODS: The examination of treatment effects by geographic region was a prespecified analysis. CABANA enrolled patients from 10 countries, with 1,285 patients at 85 North American (NA) sites and 919 at 41 non-NA sites. The primary endpoint was a composite of death, disabling stroke, serious bleeding, or cardiac arrest. Death and first atrial fibrillation recurrence were secondary endpoints. RESULTS: At least 1 primary endpoint event occurred in 157 patients (12.2%) from NA and 33 (3.6%) from non-NA sites over a median 54.9 and 40.5 months of follow-up, respectively (NA/non-NA adjusted hazard ratio (HR) 2.18, 95% confidence interval (CI) 1.48-3.21, P < .001). In NA patients, 78 events occurred in the ablation and 79 in the drug arm, (HR 0.91, 95% CI 0.66, 1.24) while 11 and 22 events occurred in non-NA patients (HR 0.51, 95% CI 0.25,1.05, interaction P = .154). Death occurred in 53 ablation and 51 drug therapy patients in the NA group (HR 0.96, 95% CI 0.65,1.42) and in 5 ablation and 16 drug therapy patients in the non-NA group (HR 0.32, 95% CI 0.12,0.86, interaction P = .044). Adjusting for baseline regional differences or prognostic risk variables did not account for the regional differences in treatment effects. Atrial fibrillation recurrence was reduced by ablation in both regions (NA: HR 0.54, 95% CI 0.46, 0.63; non-NA: HR 0.44, 95% CI 0.30, 0.64, interaction P = .322). CONCLUSIONS: In CABANA, primary outcome events occurred significantly more often in the NA group but assignment to ablation significantly reduced all-cause mortality in the non-NA group only. These differences were not explained by regional variations in procedure effectiveness, safety, or patient characteristics. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT0091150; https://clinicaltrials.gov/study/NCT00911508.

EDUCATE: An international, randomized controlled trial for teaching electrocardiography.

INTRODUCTION: Despite the critical role of electrocardiograms (ECGs) in patient care, evident gaps exist in ECG interpretation competency among healthcare professionals across various medical disciplines and training levels. Currently, no practical, evidence-based, and easily accessible ECG learning solution is available for healthcare professionals. The aim of this study was to assess the effectiveness of web-based, learner-directed interventions in improving ECG interpretation skills in a diverse group of healthcare professionals. METHODS: In an international, prospective, randomized controlled trial, 1206 healthcare professionals from various disciplines and training levels were enrolled. They underwent a pre-intervention test featuring 30 12-lead ECGs with common urgent and non-urgent findings. Participants were randomly assigned to four groups: (i) practice ECG interpretation question bank (question bank), (ii) lecture-based learning resource (lectures), (iii) hybrid question- and lecture-based learning resource (hybrid), or (iv) no ECG learning resources (control). After four months, a post-intervention test was administered. The primary outcome was the overall change in ECG interpretation performance, with secondary outcomes including changes in interpretation time, self-reported confidence, and accuracy for specific ECG findings. Both unadjusted and adjusted scores were used for performance assessment. RESULTS: Among 1206 participants, 863 (72 %) completed the trial. Following the intervention, the question bank, lectures, and hybrid intervention groups each exhibited significant improvements, with average unadjusted score increases of 11.4 % (95 % CI, 9.1 to 13.7; P<0.01), 9.8 % (95 % CI, 7.8 to 11.9; P<0.01), and 11.0 % (95 % CI, 9.2 to 12.9; P<0.01), respectively. In contrast, the control group demonstrated a non-significant improvement of 0.8 % (95 % CI, -1.2 to 2.8; P=0.54). While no differences were observed among intervention groups, all outperformed the control group significantly (P<0.01). Intervention groups also excelled in adjusted scores, confidence, and proficiency for specific ECG findings. CONCLUSION: Web-based, self-directed interventions markedly enhanced ECG interpretation skills across a diverse range of healthcare professionals, providing an accessible and evidence-based solution.

Promises and Perils of Consumer Mobile Technologies in Cardiovascular Care: JACC Scientific Statement.

Direct-to-consumer (D2C) wearables are becoming increasingly popular in cardiovascular health management because of their affordability and capability to capture diverse health data. Wearables may enable continuous health care provider-patient partnerships and reduce the volume of episodic clinic-based care (thereby reducing health care costs). However, challenges arise from the unregulated use of these devices, including questionable data reliability, potential misinterpretation of information, unintended psychological impacts, and an influx of clinically nonactionable data that may overburden the health care system. Further, these technologies could exacerbate, rather than mitigate, health disparities. Experience with wearables in atrial fibrillation underscores these challenges. The prevalent use of D2C wearables necessitates a collaborative approach among stakeholders to ensure effective integration into cardiovascular care. Wearables are heralding innovative disease screening, diagnosis, and management paradigms, expanding therapeutic avenues, and anchoring personalized medicine.

Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure.

Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify echocardiographically determined diastolic dysfunction and increased filling pressure. We trained, validated, and tested an AI-enabled ECG in 98,736, 21,963, and 98,763 patients, respectively, who had an ECG and echocardiographic diastolic function assessment within 14 days with no exclusion criteria. It was also tested in 55,248 patients with indeterminate diastolic function by echocardiography. The model was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, and its prognostic performance was compared to echocardiography. The AUC for detecting increased filling pressure was 0.911. The AUCs to identify diastolic dysfunction grades ≥1, ≥2, and 3 were 0.847, 0.911, and 0.943, respectively. During a median follow-up of 5.9 years, 20,223 (20.5%) died. Patients with increased filling pressure predicted by AI-ECG had higher mortality than those with normal filling pressure, after adjusting for age, sex, and comorbidities in the test group (hazard ratio (HR) 1.7, 95% CI 1.645-1.757) similar to echocardiography and in the indeterminate group (HR 1.34, 95% CI 1.298-1.383). An AI-enabled ECG identifies increased filling pressure and diastolic function grades with a good prognostic value similar to echocardiography. AI-ECG is a simple and promising tool to enhance the detection of diseases associated with diastolic dysfunction and increased diastolic filling pressure.

Rationale and design of the NODE-303 study: evaluating the safety of symptom-prompted, self-administered etripamil for paroxysmal supraventricular tachycardia episodes in real-world settings.

BACKGROUND: Paroxysmal supraventricular tachycardia (PSVT) is a common episodic arrhythmia characterized by unpredictable onset and burdensome symptoms including palpitations, dizziness, chest pain, distress, and shortness of breath. Treatment of acute episodes of PSVT in the clinical setting consists of intravenous adenosine, beta-blockers, and calcium channel blockers (CCBs). Etripamil is an intranasally self-administered L-type CCB in development for acute treatment of AV-nodal dependent PSVT in a nonmedical supervised setting. METHODS: This paper summarizes the rationale and study design of NODE-303 that will assess the efficacy and safety of etripamil. In the randomized, double-blinded, placebo-controlled, Phase 3 RAPID trial, etripamil was superior to placebo in the conversion of single PSVT episodes by 30 minutes post initial dose when administered in the nonhealthcare setting; this study required a mandatory and observed test dosing prior to randomization. The primary objective of NODE-303 is to evaluate the safety of symptom-prompted, self-administered etripamil for multiple PSVT episodes in real-world settings, without the need for test dosing prior to first use during PSVT. Secondary endpoints include efficacy and disease burden. Upon perceiving a PSVT episode, the patient applies an electrocardiographic monitor, performs a vagal maneuver, and, if the vagal maneuver is unsuccessful, self-administers etripamil 70 mg, with an optional repeat dose if symptoms do not resolve within 10 minutes after the first dose. A patient may treat up to four PSVT episodes during the study. Adverse events are recorded as treatment-emergent if they occur within 24 hours after the administration of etripamil. RESULTS: Efficacy endpoints include time to conversion to sinus rhythm within 30 and 60 minutes after etripamil administration, and the proportion of patients who convert at 3, 5, 10, 20, 30, and 60 minutes. Patient-reported outcomes are captured by the Brief Illness Perception Questionnaire, the Cardiac Anxiety Questionnaire, the Short Form Health Survey 36, the Treatment Satisfaction Questionnaire for Medication and a PSVT survey. CONCLUSIONS: Overall, these data will support the development of a potentially paradigm-changing long-term management strategy for recurrent PSVT.

Artificial intelligence electrocardiogram as a novel screening tool to detect a newly abnormal left ventricular ejection fraction after anthracycline-based cancer therapy.

AIMS: Cardiotoxicity is a serious side effect of anthracycline treatment, most commonly manifesting as a reduction in left ventricular ejection fraction (EF). Early recognition and treatment have been advocated, but robust, convenient, and cost-effective alternatives to cardiac imaging are missing. Recent developments in artificial intelligence (AI) techniques applied to electrocardiograms (ECGs) may fill this gap, but no study so far has demonstrated its merit for the detection of an abnormal EF after anthracycline therapy. METHODS AND RESULTS: Single centre consecutive cohort study of all breast cancer patients with ECG and transthoracic echocardiography (TTE) evaluation before and after (neo)adjuvant anthracycline chemotherapy. Patients with HER2-directed therapy, metastatic disease, second primary malignancy, or pre-existing cardiovascular disease were excluded from the analyses as were patients with EF decline for reasons other than anthracycline-induced cardiotoxicity. Primary readout was the diagnostic performance of AI-ECG by area under the curve (AUC) for EFs < 50%. Of 989 consecutive female breast cancer patients, 22 developed a decline in EF attributed to anthracycline therapy over a follow-up time of 9.8 ± 4.2 years. After exclusion of patients who did not have ECGs within 90 days of a TTE, 20 cases and 683 controls remained. The AI-ECG model detected an EF < 50% and ≤ 35% after anthracycline therapy with an AUC of 0.93 and 0.94, respectively. CONCLUSION: These data support the use of AI-ECG for cardiotoxicity screening after anthracycline-based chemotherapy. This technology could serve as a gatekeeper to more costly cardiac imaging and could enable patients to monitor themselves over long periods of time.

Artificial intelligence electrocardiogram can be used to screen for an abnormal heart function after anthracycline chemotherapy, opening the door to new ways of cost-effective screening of cancer survivors at risk of cardiotoxicity over long periods of time.

Realtime Diagnosis from Electrocardiogram Artificial Intelligence-Guided Screening for Atrial Fibrillation with Long Follow-Up (REGAL): Rationale and design of a pragmatic, decentralized, randomized controlled trial.

BACKGROUND: Atrial fibrillation (AF) is associated with increased risks of stroke and dementia. Early diagnosis and treatment could reduce the disease burden, but AF is often undiagnosed. An artificial intelligence (AI) algorithm has been shown to identify patients with previously unrecognized AF; however, monitoring these high-risk patients has been challenging. Consumer wearable devices could be an alternative to enable long-term follow-up. OBJECTIVES: To test whether Apple Watch, used as a long-term monitoring device, can enable early diagnosis of AF in patients who were identified as having high risk based on AI-ECG. DESIGN: The Realtime diagnosis from Electrocardiogram (ECG) Artificial Intelligence (AI)-Guided Screening for Atrial Fibrillation (AF) with Long Follow-up (REGAL) study is a pragmatic trial that will accrue up to 2,000 older adults with a high likelihood of unrecognized AF determined by AI-ECG to reach our target of 1,420 completed participants. Participants will be 1:1 randomized to intervention or control and will be followed up for 2 years. Patients in the intervention arm will receive or use their existing Apple Watch and iPhone and record a 30-second ECG using the watch routinely or if an abnormal heart rate notification is prompted. The primary outcome is newly diagnosed AF. Secondary outcomes include changes in cognitive function, stroke, major bleeding, and all-cause mortality. The trial will utilize a pragmatic, digitally-enabled, decentralized design to allow patients to consent and receive follow-up remotely without traveling to the study sites. SUMMARY: The REGAL trial will examine whether a consumer wearable device can serve as a long-term monitoring approach in older adults to detect AF and prevent cognitive function decline. If successful, the approach could have significant implications on how future clinical practice can leverage consumer devices for early diagnosis and disease prevention. CLINICALTRIALS: GOV: : NCT05923359.

Patient-Level Artificial Intelligence-Enhanced Electrocardiography in Hypertrophic Cardiomyopathy: Longitudinal Treatment and Clinical Biomarker Correlations.

BACKGROUND: Artificial intelligence (AI) applied to 12-lead electrocardiographs (ECGs) can detect hypertrophic cardiomyopathy (HCM). OBJECTIVES: The purpose of this study was to determine if AI-enhanced ECG (AI-ECG) can track longitudinal therapeutic response and changes in cardiac structure, function, or hemodynamics in obstructive HCM during mavacamten treatment. METHODS: We applied 2 independently developed AI-ECG algorithms (University of California-San Francisco and Mayo Clinic) to serial ECGs (n = 216) from the phase 2 PIONEER-OLE trial of mavacamten for symptomatic obstructive HCM (n = 13 patients, mean age 57.8 years, 69.2% male). Control ECGs from 2,600 age- and sex-matched individuals without HCM were obtained. AI-ECG output was correlated longitudinally to echocardiographic and laboratory metrics of mavacamten treatment response. RESULTS: In the validation cohorts, both algorithms exhibited similar performance for HCM diagnosis, and exhibited mean HCM score decreases during mavacamten treatment: patient-level score reduction ranged from approximately 0.80 to 0.45 for Mayo and 0.70 to 0.35 for USCF algorithms; 11 of 13 patients demonstrated absolute score reduction from start to end of follow-up for both algorithms. HCM scores were significantly associated with other HCM-relevant parameters, including left ventricular outflow tract gradient at rest, postexercise, and with Valsalva, and NT-proBNP level, independent of age and sex (all P < 0.01). For both algorithms, the strongest longitudinal correlation was between AI-ECG HCM score and left ventricular outflow tract gradient postexercise (slope estimate: University of California-San Francisco 0.70 [95% CI: 0.45-0.96], P < 0.0001; Mayo 0.40 [95% CI: 0.11-0.68], P = 0.007). CONCLUSIONS: AI-ECG analysis longitudinally correlated with changes in echocardiographic and laboratory markers during mavacamten treatment in obstructive HCM. These results provide early evidence for a potential paradigm for monitoring HCM therapeutic response.