Citations

Abstract Aims Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined >8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA2DS2-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2–5 years prior to AF, 23.6% 1–2 years prior to AF, 34.0% 0–3 months prior to AF, 40.9% 0–3 months after AF, 35.2% 1–2 years after AF, and 42.2% 2–5 years after AF (P < 0.001). The rate of increase prior to AF was higher for age >50 years CHA2DS2-VASc score ≥4. Conclusion The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1–2 years following AF and continues to increase thereafter.

Abstract According to the 2020 report of the American Heart Association’s Heart & Stroke Statistics report, nearly 1,000 people are dying daily because of sudden out-of-hospital cardiac arrests and unfortunately, their survival rate is as low as 10%. Hypertrophic Cardiomyopathy (HCM), a relatively rare genetic heart disease is one of these diseases but finding the right patient for the implantation of ICD is still a research question. Implantation of cardioverter-defibrillator (ICD) can save the life of some of these patients. Due to the complexity of the identification of HCM patients, financial burdens, and the clinical risks involved in the ICD implantation procedure, HCM patients will go into a monitoring state before reaching the implantation trigger. Our study cohort shows about 82% of HCM deaths, did not have an ICD, which highlights the need to improve the pre-screening algorithms. In the current paper, we have proposed a new deep learning-based unsupervised clustering technique to facilitate the prioritization of patients to undergo ICD device implantation. This model uses over 900 echocardiographic measurements to find patients who benefit more from the ICD implantation procedure. Our model was trained and tested over 6 years of echo reports collected at Mayo Clinic. This model can be used as a decision support assistant for cardiologists in finding the right HCM patient when decision-making is hard.

Introduction: OAC are recommended for AF patients with CHA 2 DS 2 -VASc ≥2 in men or ≥3 in women (high-risk group), may be considered in patients with CHA 2 DS 2 -VASc 1 in men or 2 in women (medium-risk group), and are reasonable to omit in patients with CHA 2 DS 2 -VASc 0 in men or 1 in women (low-risk group). Furthermore, NOACs are recommended over warfarin in most AF patients. This study aimed to investigate AF stroke prevention treatment patterns in patients managed in routine practice, in particular, the under-use of OAC in the high-risk group and over-use in the low-risk group. Methods: Adult patients with AF were identified using de-identified claims for commercial and Medicare Advantage enrollees in the OptumLabs Data Warehouse. Prescription fills were assessed within 90 days of a patient’s AF encounter between 1/1/2016-12/31/2020. Patients with other indications for OAC, such as VTE, recent joint replacement, prior mechanical or bioprosthetic valve replacement, rheumatic mitral stenosis, and patients who underwent LAAO were excluded. Results: Among the 339,197 AF patients, 4.4%, 8.0%, and 87.6% were in the low-, medium-, and high-risk groups, respectively. The mean age was 51.2 ±10.5 yr, 59.9 ±9.4 yr, and 75.1 ±8.8 yr. In the low-risk group, 29.6% were treated with OAC, whereas in the high-risk group, 40.3% were not treated with any antithrombotic therapy and 7.5% used thienopyridine only. Although 71% of patients on OAC were on a NOAC, the proportion of patients on NOAC decreased from 91% in the low-risk group to 69% in the high-risk group. Conclusions: There are potentially both under-use of guideline-recommended stroke prevention therapies in the high-risk patients and over-use in the low-risk group. Because patients at a high risk of stroke are also at a high risk of major and intracranial bleeding who might benefit from NOACs, the decreased use of NOAC in high-risk patients might highlight the need for future quality improvement efforts.

Introduction: Mobile thrombi are commonly observed on endovascular leads at the time of catheter ablation. Transseptal puncture poses a risk of paradoxical embolism. We sought to examine the risk of stroke or systemic embolism after transseptal ablation in patients with and without cardiac implantable electronic devices (CIEDs). Methods: Using a large, national administrative claims database we identified 31,720 patients who underwent a catheter ablation requiring transseptal puncture between 1/1/2004 and 9/30/2014. We created two propensity-matched cohorts, matching on age, gender, race, household income, region, Charlson index, CHADS-Vasc score, and year and indication for ablation (5,533 patients with CIEDs and 11,300 patients without devices). Incidence rates (events/100 patient-years) and propensity score-matched cox proportional hazards models (with 95% confidence intervals) were used to estimate risk of stroke, transient ischemic attack, or systemic embolism for patients with or without CIEDs. We also examined the impact of presence of a CIED and oral anticoagulation (OAC) use on risk of stroke or systemic embolism within the matched cohort. Average follow up was 2.1 years. Results: The event rate was 1.5 per 100 person years in patients without device, and 1.9 per 100 person years in patients with device. Among patients not on OAC, presence of a cardiac device was associated with an increased risk of stroke or systemic embolism (HR 1.71 [1.24-2.35], P<0.01) whereas there was no association among patients treated with OAC (0.98 [0.75_1.28], p=ns) (Figure). Conclusions: Cardiac devices are associated with an increased risk of stroke or systemic embolism after transseptal ablation, but this risk is attenuated with post ablation OAC use. Anticoagulation should be considered for all patients with CIEDs post transseptal ablation, regardless of the presence of another compelling indication for anticoagulation.

Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman’s life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.

Abstract Background The Early Treatment of Atrial Fibrillation for Stroke Prevention Trial (EAST-AFNET 4) demonstrated clinical benefit of early rhythm-control therapy in patients with recently diagnosed atrial fibrillation (AF) and concomitant cardiovascular conditions (CHA2DS2-VASc-Score ≥2) compared to the current practice of limited rhythm-control therapy to improve AF-related symptoms. Purpose To evaluate the generalizability of the EAST-AFNET 4 trial in routine practice, we assessed the proportion of patients who would have met trial eligibility and examined the association between early rhythm-control and clinical outcomes. Methods Using a large US administrative database, we identified 109,739 patients with newly diagnosed AF from July 28th, 2011 to December 30th, 2016, the enrollment period of the EAST-AFNET 4 trial. Eligibility for trial inclusion was assessed based on inclusion criteria. Eligible patients were classified as either receiving early rhythm-control, i.e AF ablation and/or any antiarrhythmic drug therapy, within the first year after AF diagnosis (N=27,106) or patients not receiving early rhythm-control (N=82,633). The date 12 months after the first AF diagnosis was defined as the index date and the start of the follow up period. Propensity score overlap weighting was used to balance patients on 90 baseline characteristics. Cox proportional hazards regression was used to compare early rhythm-control with no early rhythm-control for the primary outcome of a composite end point of all-cause mortality, stroke, or hospitalization with the diagnoses heart failure or myocardial infarction. Results Eligible for the trial were 72.9% (82,633/109,739) of all patients with newly diagnosed AF. Early rhythm-control therapy was associated with a reduction in the composite end point in the overall cohort of patients (hazard ratio [HR] 0.85; 95% confidence interval [CI] 0.75–0.97; P=0.015) with largely consistent treatment effects between patients eligible or ineligible for the trial. The reduction of stroke risk associated with early rhythm-control therapy was found in the overall cohort (HR 0.66; 95% CI 0.47–0.93; P=0.017) and in the trial-eligible cohort (HR 0.67; 95% CI 0.45–0.98; P=0.041). Conclusion In a large health care data set, the majority of patients with newly diagnosed AF were eligible for the trial. Early rhythm-control therapy was associated with a 15% reduction in the composite end point of all-cause mortality, stroke, or hospitalization with the diagnoses heart failure or myocardial infarction, with the greatest benefit in the reduction of stroke risk. The treatment effect was consistent between patients eligible or ineligible for the trial. Patients in routine practice had higher rates of adverse outcomes than the trial, but the relative risk reduction with early rhythm-control therapy was similar. These data demonstrate the potential of early rhythm-control therapy to reduce outcomes in patients with AF. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): German Heart Foundation (Mit Fördermitteln der Deutschen Herzstiftung e.V.)

Research ObjectiveAn artificial intelligence (AI) algorithm based on ECG was integrated into clinical workflows to identify patients with a high likelihood of low ejection (EF), a condition that is underdiagnosed but treatable. The trial aimed to assess whether the AI‐powered clinical decision support enabled early diagnosis of low EF.Study DesignA pragmatic, cluster randomized controlled trial (RCT) was conducted in 45 primary care practices, including an academic medical center as well as community and rural clinics across Minnesota and Wisconsin. A total of 120 primary care teams, including 358 clinicians, were randomized to either intervention (access to AI results with positive results indicating a high likelihood of low EF; 181 clinicians) or control (usual care; 177 clinicians). The primary endpoint was a new diagnosis of EF≤50% within 90 days. A positive deviance qualitative study and a survey were used to assess implementation strategies.Population StudiedIn total, 22,641 adult patients (N = 11,573 intervention; N = 11,068 control) with ECG performed as part of routine care between 8/5/2019 and 3/31/2020 were included. Patients were excluded if they had known EF≤50% or a history of documented heart failure (HF) identified by clinical notes abstracted by natural language processing.Principal FindingsThe mean age of patients was 60.5 ± 17.6 years; 53.9% were women. Positive AI results were found in 6.0% of patients in both arms. Clinicians in the intervention group obtained more echocardiograms for patients with positive results (38.1% control vs. 49.6% intervention, p < 0.001), but in the overall cohort, the echocardiogram utilization was similar (18.2% vs. 19.2%, p = 0.17). The intervention increased the diagnosis of low EF in the overall cohort (1.6% vs. 2.1%, odds ratio [OR] 1.32 [1.01–1.61], p = 0.007) and among patients with positive results (14.5% vs. 19.5% OR 1.43 [1.08–1.91], p = 0.01). Results using different thresholds for low EF were consistent with the primary result. Among patients with positive results, the intervention increased echocardiography more significantly in women vs. men (p for interaction 0.02), in patients without prior echocardiogram vs. those with prior echocardiogram (p for interaction 0.04), and in patients whose ECGs were ordered in outpatient clinics (p for interaction 0.02).ConclusionsAn AI algorithm run on existing ECGs enabled the early diagnosis of low EF in a large cohort of patients managed in routine primary care practices. Because ECG is a low‐cost test frequently performed for a variety of purposes, the algorithm could improve early diagnosis and treatment of a condition that is often asymptomatic but has effective treatments, and thus, reduce the disease burden in broad populations.Implications for Policy or PracticeThe current study is one of the first RCTs that demonstrated the value of AI in routine practice. Low EF confers an increased risk of HF progression and mortality. Although there are effective therapies, low EF is undiagnosed, leading to missed opportunities for treatment. EF can be readily determined by echocardiography, but the imagining test is too resource‐intensive to screen unselected populations. The AI applied to the existing ECGs could identify high‐risk patients for further assessment.Primary Funding SourceMayo Clinic.