Noseworthy, Peter A.

Abstract Background An artificial intelligence (AI) algorithm detecting age from 12-lead ECG has been suggested to signal “physiological age” of the individual. Importantly, increased physiological age gauged by an increased difference between ECG-age and chronological age has been associated with higher risk of cardiac events in non-transplant population. Purpose We sought to investigate the validity of the AI-derived ECG-age algorithm in patients who underwent heart transplantation and its relationship to major adverse cardiovascular events (MACE). Methods A total of 489 consecutive patients who had undergone heart transplantation in our institution between 1994 and 2018 were studied. AI-ECG age was calculated by a previously-trained artificial intelligence (AI) algorithm using a 12-lead ECG per patient. ECGs used in the training process of the algorithm were excluded. The average of the ECG-ages within one year before and one year after heart transplantation was used to represent pre- and post-transplant ECG-ages. MACE was defined as any incidence of revascularization, re-transplantation, and death. Results Pre-transplant ECG-age (mean 63±10 years) correlated significantly with recipient chronological age (mean 50±13 years, r=0.57, p<0.0001), but this correlation between recipient and ECG-ages was weakened after transplantation (mean post-transplant ECG age of 55±10 years, r=0.34, p<0.0001). Interestingly, post-transplant ECG-age correlated significantly with donor age (mean ECG age of 55±10 years vs. mean donor age of 32±13 years, r=0.42, p<0.0001). During a median (IQR) follow-up of 9 (5, 14) years, 251 patients had MACE. Mean change in ECG age after transplantation compared to before was −8.8±12.7 years. Patients who had an increase in ECG-age after compared to before transplantation showed increased risk of MACE (HR: 1.53 [1.16, 2.01], p=0.002), independent of recipient and donor ages (adjusted HR: 1.68 [1.26, 2.25], p=0.001); whereas there were no significant differences in risk of MACE in patients who were transplanted with an older donor heart (HR: 1.07 [0.77, 1.50], p=0.66). In a Kaplan Meier survival analysis, those with increased ECG-age after transplantation had significantly lower MACE-free survival compared to those with decreased ECG-age. (Log-rank P=0.002; Wilcoxon P=0.001) (Figure) Conclusion Post-transplant ECG-age correlates more faithfully with the donor's than the recipient's chronological age, suggesting that ECG-age more closely reflects cardiac age than the patient age. Furthermore, ECG-age derived cardiac aging after transplantation is associated with higher risk of MACE. Funding Acknowledgement Type of funding sources: None.

Abstract Background Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms. Purpose The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women. Methods We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. Results 1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, <45% and <50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF <45% and 0.87 for LVEF <50%. For LVEF ≤35%, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to white (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 and 0.72 respectively. Conclusions A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Abstract Background Guidelines promote shared decision-making (SDM) for anticoagulation in patients with atrial fibrillation (AF). We recently showed that adding a within-encounter SDM tool to usual care (UC) increases patient involvement in decision making and clinician satisfaction, without affecting encounter length. Purpose We aimed to estimate the extent to which use of an SDM tool affected patient adherence to the decided care plan and clinical safety endpoints. Methods We conducted a multi-center, encounter-level randomized trial assessing the efficacy of UC with vs. without an SDM conversation tool for use during the clinical encounter (Anticoagulation Choice, AC) in patients with non-valvular AF considering starting or reviewing anticoagulation treatment. We conducted a chart and pharmacy review, blinded to randomization status, at 10 months post-enrollment to assess primary adherence (proportion of patients who were prescribed an anticoagulant who filled their first prescription) and secondary adherence (estimated using the proportion of days for which treatment was supplied and filled [PDC] for DOAC, and as time in therapeutic range (TTR) for warfarin). We also followed for any safety outcomes (stroke [stroke or transient ischemic attack], major bleeding, or death). Results We enrolled 922 evaluable patient encounters (AC=463, UC=459), of which 814 (88%) had pharmacy and clinical follow-up. We found no differences between arms in either primary (78% of patients in AC filled their first prescription vs. 81% in UC) or secondary adherence to anticoagulation (see Figure, PDCDOAC was 74.1% in AC vs. 71.6% in UC; TTRwarfarin was 66.6% in AC vs. 64.4% in UC). PDCDOAC was better (65%) in AC than in UC (55%) (OR 1.49, CIs 1.00, 2.22). Safety outcomes, mostly bleeds, occurred in 13% (AC) of and 14% (UC) of participants. Conclusions This is the largest reported randomized trial in AF comparing usual care with and without an SDM tool to promote SDM. Although patients were more actively involved in SDM, we found no significant differences between arms in primary or secondary adherence to anticoagulation or clinical safety outcomes. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): The trial was funded by and conducted independently of the National Heart, Lung, and Blood Institute (NHLBI) of the U.S. National Institutes of Health (RO1 HL131535-01). The funding body had no influence on the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Figure 1

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.)

AbstractBackgroundAutomated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification.MethodsA derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one “all‐inclusive” model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model's performance.ResultsThe VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X‐lead QRS amplitude change, Y‐lead QRS amplitude change, and Z‐lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95).ConclusionCustom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically.

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.

Abstract Aims Cardiovascular disease is a major threat to maternal health, with cardiomyopathy being among the most common acquired cardiovascular diseases during pregnancy and the postpartum period. The aim of our study was to evaluate the effectiveness of an electrocardiogram (ECG)-based deep learning model in identifying cardiomyopathy during pregnancy and the postpartum period. Methods and results We used an ECG-based deep learning model to detect cardiomyopathy in a cohort of women who were pregnant or in the postpartum period seen at Mayo Clinic. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. The study cohort included 1807 women; 7%, 10%, and 13% had left ventricular ejection fraction (LVEF) of 35% or less, &lt;45%, and &lt;50%, respectively. The ECG-based deep learning model identified cardiomyopathy with AUCs of 0.92 (LVEF ≤ 35%), 0.89 (LVEF &lt; 45%), and 0.87 (LVEF &lt; 50%). For LVEF of 35% or less, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to White (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 to 0.86 and 0.72, respectively. Conclusions An ECG-based deep learning model effectively identifies cardiomyopathy during pregnancy and the postpartum period and outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting.