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Abstract Background Atrial functional mitral regurgitation (AFMR) is an increasingly recognized cause of functional MR thought to result from left atrial enlargement and myopathy in the setting of preserved left ventricular geometry and function. AFMR mostly occurs in patients with atrial fibrillation (AF) and/or heart failure with preserved ejection fraction. Purpose We investigated whether the use of an artificial intelligence-based electrocardiogram (AI-ECG) can identify patients with new-onset AF and sinus rhythm (SR) who are at highest risk of developing AFMR. Methods Adults with a new diagnosis of AF between 2010 and 2021 who had a transthoracic echocardiogram (TTE) within 1 month after AF diagnosis were identified. Similarly, adults in SR who never had AF diagnosis and had a TTE during the same period were identified. Exclusion criteria from both groups included >mild MR at baseline, primary mitral valve disease, cardiomyopathy, ≥moderate aortic valve disease, current or previous low ejection fraction, enlarged left ventricle, cardiac devices, previous cardiac surgeries, and the absence of follow-up TTE ≥6 months from baseline. Only patients who had ECG within 10 days of the TTE were included. When multiple ECGs were available, the closest was considered. A previously trained AI-ECG algorithm to detect the probability of AF was utilized. Incident AFMR was defined as >mild functional MR in the absence of >mild left ventricle enlargement or dysfunction. Diastolic dysfunction was defined according to ASE/EACVI guidelines. Results Overall, 1,570 patients with new-onset AF (median age 67 years; 34% females) and 16,802 patients in SR (median age 59 years; 46% females) were included. The median AI ECG probability for AF was 43% (IQR 26%-59%) in the new-onset AF group and 2% (0.5%-7%) in the SR group, Figure 1. Incident AFMR occurred in 152 patients in the AF group over median 3.06 (IQR 1.5-5.2) years of follow-up and in 482 patients in the SR group over median 3.0 (1.5-5.6) years of follow-up. The probability of silent AF by the AI-ECG was associated with incident AFMR in the new-onset AF group [hazard ratio 1.19 (95% CI 1.11-1.28) per 10%, p <.001] and the SR group [hazard ratio 1.19 (95% CI 1.12-1.25) per 10%, p <.001]. The AI ECG-derived AF probability remained as an independent risk factor for AFMR in multivariable analysis in both AF and SR, Table 1. Conclusion The AI ECG for AF might be able to reflect the degree of left atrial myopathy and thus the risk of incident AFMR in patients with AF and SR.Distribution of AI ECG probability of AFMultivariable risk factors for AFMR

Abstract Background Cardiomyopathy is a leading cause of pregnancy-related mortality and represents the number one cause of death in the late postpartum period. Purpose The goal of this study was to prospectively evaluate the effectiveness of an artificial intelligence-based electrocardiogram (AI-ECG) and a point-of-care artificial intelligence-enhanced digital stethoscope for cardiomyopathy screening among pregnant and postpartum women. Methods We conducted a single-arm prospective study of pregnant and postpartum women (up to 12 months following delivery) enrolled at three sites between October 2021 and October 2022. Study participants completed a standard 12-lead ECG, ECG + phonocardiogram recordings with a digital stethoscope, a demographic questionnaire, and a transthoracic echocardiogram (TTE) within a 24-hour period. Diagnostic performance of the AI-ECG and digital stethoscope for cardiomyopathy detection, defined as left ventricular ejection fraction (LVEF) < 45%, was evaluated using the area under the curve (AUC) and standard measures of diagnostic performance. Results We included 100 consecutive pregnant and postpartum women who provided informed consent and had at least one ECG recorded, completed the questionnaire, and had an echocardiogram performed. All participants self-identified as female, and 41% were recruited from a federally qualified health center. The median age of participants was 31 years (Q1: 27, Q3: 34). Thirty-eight percent identified as non-Hispanic White, 32% as non-Hispanic Black, 21% as Hispanic or Latino, 6% as Asian, and 3% as Other (1 Native Hawaiian and 2 multiracial). Five percent (5/100) had LVEF <45%. The 12-lead AI-ECG model demonstrated close to perfect discrimination (accuracy 99%; AUC 1.00, sensitivity 100%) for detection of cardiomyopathy with 1 false positive screen (LVEF 49%), Figure 1A. The digital stethoscope (angled position) had an AUC of 0.99 (95% CI: 0.97, 1.00) for detection of LVEF <45%. Sensitivity, specificity, positive predictive value, and negative predictive value for the digital stethoscope were 60%, 98%, 60% and 98%, respectively for detection of LVEF <45% (Figure 1B & 1C). Conclusions In this small prospective study, we found that 5% of pregnant and postpartum women had cardiomyopathy, a potentially life-threatening condition, and that a rapid, inexpensive, ubiquitous, massively scalable point-of-care test - the ECG - showed promising results, identifying it with an AUC > 0.98 with the application of artificial intelligence.Figure 1

Abstract Background Premature ventricular contractions (PVCs) are the most common arrhythmia affecting the ventricle. Frequent PVCs are associated with the development of PVC-cardiomyopathy (CM). It is known that the PVC burden has a direct correlation to the severity of left ventricular (LV) dysfunction. However, only 5% of patients with frequent PVCs (>10,000 per day) have associated PVC-CM. Thus, selecting patients for therapy options with drugs or catheter ablation is challenging, especially, when patients are asymptomatic with the PVCs. Purpose To train a deep learning model to predict the probability of low LV ejection fraction (LVEF) in patients with PVCs on a 12-lead electrocardiogram (ECG). Methods Using 69,440 paired 12-lead ECGs (containing at least one PVC), and echocardiogram data, including the left ventricular ejection fraction at Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. ECGs were acquired at a sampling rate of 500 Hz using a Marquette ECG machine (GE Healthcare) and stored using the MUSE data management system for later retrieval. Comprehensive 2-D or 3-D and Doppler echocardiography were available for all patients. We trained a convolutional neural network using the Keras framework with TensorFlow (Google). The ECG-echocardiogram pairs were divided into a 60:20:20 training:validation:test split. Results The mean age of the patients used to train the model was 69.9 ± 13.0 years, and 65.2 percent of patients were male. The mean LVEF for this population was 52 ± 14.9 %, and the proportion of patients with low LVEF was 16.8 percent. The model yielded values for area under the curve, sensitivity, specificity, and accuracy of 0.897, 0.830, 0.813, and 0.816, respectively (Figure 1). A representative example of model prediction of normal LVEF and low LVEF is shown in Figure 2. Conclusion Using large-scale data, we have trained a highly accurate convolutional neural network with excellent predictive accuracy for LV systolic dysfunction using the surface ECG. Such a tool would be invaluable for cardiologists when risk-stratifying patients with frequent PVCs, especially those that are asymptomatic. Patients at risk will benefit from early referral to electrophysiologists for drug therapy and catheter ablation to mitigate the risk of PVC-CM.Figure 1Figure 2

Abstract Background Atherosclerotic cardiovascular disease (ASCVD), of which coronary artery disease (CAD) is the primary manifestation, is the leading cause of death worldwide. The AHA/ACC Pooled Cohort Equation (PCE) facilitates risk stratification and initiation of primary prevention of ASCVD, however their performance is suboptimal. Purpose We develop an artificial intelligence-based electrocardiogram (ECG) analysis (AI-ECG) algorithm to detect different stages of CAD and assessed the value of AI-ECG over PCE to predict incident myocardial infarction (MI) and survival over 3-years. Methods Using electronic health record data from approximately 7 million patients seen at over 70 hospitals and clinics across 5 states in the USA, we created independent AI-ECG models to identify elevated coronary artery calcium (CAC) by computer tomography, the presence of obstructive CAD by stress test or angiography, and left ventricular regional akinesis by echocardiogram, presumed to represent prior MI. Deep structured data, AI-enabled natural language processing of unstructured data were used to ensure cohort, and physician review of labeling were used to ensure cohort accuracy. These models were ensembled into a single model that was used to assess the utility of AI-ECG for predicting new occurrence MI in PCE- and age-stratified cohorts of patient with no prior history of ASCVD, over a 3-year timeframe using Cox’s proportional hazards analysis. Results A total of 6,080 patients (15,192 ECGs) were labeled positive for CAC ≥ 300, 12,561 patients (18,915 ECGs) positive for obstructive CAD, and 10,767 patients (23,870 ECGs) positive for regional akinesis. AI-ECG for identifying elevated CACS, obstructive CAD, and regional akinesis achieved AUROCs of 0.86, 0.80, and 0.93, respectively. All AI-ECG models identified increased 3-year risk for MI and death in AI-ECG positive versus AI-ECG negative patients and subgroups with 2 or 3 positive results had stepwise increased risk (Figure A). In a 3-year survival analysis of all-comer patients, the hazard ratio for those that tested positive on 1, 2, or 3 versus 0 positive disease-specific models were 1.61 (1.53-1.69). 2.40 (2.27-2.55), 3.42 (3.21-3.65), respectively. Risk stratification was also observed in cohorts without prior ASCVD stratified by low and high PCE-derived 10-year ASCVD risk (Figure B) and in cohorts stratified by age, including under the age of 40. Conclusions AI-ECG has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimate ASCVD risk, and in whom risk assessment over periods of time shorter than 10 years is desired.

Abstract 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.

Background: The gap between electrocardiogram (ECG) artificial intelligence-derived age (ECG-Age) and chronological age (Age-Gap) predicts cardiovascular (CV) comorbidities and mortality reflecting physiologic age. Adverse childhood experiences (ACEs) are traumatic and harmful events that impact adulthood CV outcomes. We hypothesized that ACEs would be associated with physiologic age as determined by the Age-Gap. Methods: We applied a previously validated ECG-Age algorithm to the ECGs of 1,988 consecutive individuals aged ≥18, that within 2 years completed an ACEs questionnaire as part of psychological evaluations for weight management. The ACE questionnaire assesses 3 psychosocial domains (i) childhood abuse (psychological, physical or sexual), (ii) household dysfunction (substance abuse, mental illness, female figure treated violently, criminal behavior and parental separation/divorce) and (iii) neglect (emotional or physical) before age 18; results ranged 0 to 10 and were categorized as burden (0, 1-3, and ≥4) and presence (abuse, household dysfunction, neglect). Age-Gap was operationalized as (≤mean or > mean) positive values reflect physiological aging. Logistic regression analyses tested the association between ACE and Age-Gap. Results: Mean ± SD chronological age was 49±13 yr, ECG-Age 54±12 yr, Age-Gap 4.26±8.95 yr,75% women, average ACE 2±2, 32% experienced 0, 44% 1-3, and 24% ≥4 ACEs. Age-Gap was 1.4 years higher in those who experienced at least 1 ACE. Compared to those who didn’t experience ACE those who faced 1-3 ACE were more likely to have an Age-Gap >mean (OR 1.28 95%CI 1.57-1.04) independent of sex and the age-adjusted Charlson comorbidity index Fig A. Also, they were more prone to experiencing neglect and household dysfunction Fig B. Conclusions: Age-gap was associated with ACEs burden and domains, underscoring the need to identify ACEs during clinical evaluations to prevent ACEs at the individual, family, and community levels.

Background: An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM AI-score). However, its clinical application may be challenging due to the low disease prevalence and potentially high false-positive rate when screening unenriched cohorts. Aims: To identify clinical characteristics associated with false-positive HCM AI-score results to improve screening tool application. Methods: From a cohort of 20,677 patients with a digital 12-lead ECG in routine clinical practice in 2021, we performed a detailed chart review of the 200 patients with highest HCM AI-score to determine their HCM status and collect clinical data. Stepwise forward logistic regression was used to create a clinical variable-based “candidacy for HCM screening” score (CHS score), differentiating true- and likely true-positive from likely false-positive HCM AI-score results. We then validated the CHS score in 200 patients with the highest HCM AI-scores from an independent cohort of 15,147 patients with an ECG in 2022. Results: Among the 200 patients in 2021 (median age 71 years, 48% female), 96 (48%) were likely false-positive HCM AI-score results, 71 (36%) definite or likely true-positive results, and 33 (16%) indeterminate. The CHS score included a history of surgical septal myectomy, coronary artery bypass graft surgery, cardiac valve replacement, and pacemaker, and had an AUC of 0.88 (95% CI 0.83-0.92) for the differentiation of true- versus false-positive AI results. In the 2022 cohort, the CHS score had an AUC of 0.82 (95% CI 0.76 - 0.88). After limiting the cohort to HCM candidates identified by the CHS score, the false-positive results rate was reduced to 27/114 (24%). Conclusions: By applying the CHS score to patients with positive HCM AI-screening results, the false-positive rate decreased from 49% to 24%. Utilizing a clinical model used in tandem with an AI model improved diagnostic yield.

Introduction: We have previously shown that ECG-derived age (ECG-Age) predicts all-cause and cardiovascular (CV) mortality, reflecting physiologic age. We hypothesized that ECG-Age minus chronological age (Age-Gap) would be associated with coronary artery calcium (CAC) assessed by computer tomography. Methods: We developed a neural network model to predict ECG-Age using 12 lead ECGs from 399,750 training, 99,977 validation and 275,056 testing patients. We then applied the algorithm to 41,202 consecutive patients that from 1997-2020 underwent clinically indicated CAC and ECG within 1 year. Major modifiable CV risk factors were collected as part of preventive cardiology or general medical evaluations. Exclusion criteria were statin therapy, pacemakers, or history of coronary artery disease. Results: We included 41,202 subjects (68.5% males, 83.6% Caucasian), mean chronological age was 55.2±9.1 yrs and the ECG-Age was 56.01±9.3 yrs. The mean Age-Gap was -0.78±7 yrs, R 2 =0.78, p<0.0001. Mean CAC score was 140.2±532.3, and 21,483 (52%) had CAC >0. After adjustment for age and sex, those older by ECG (defined as having a positive Age-Gap) were more likely to have CAC>0 (OR 1.35, 95% CI 1.34-2.37 for those with >1 but <2 SD of a positive Age-Gap; and OR 1.80, 95% CI 1.34-2.37 for those with ≥2 SD of a positive Age-Gap), while those younger by ECG were less likely to have any CAC (OR 0.70, 95% CI 0.57-0.87 for those with >1 but <2 SD of a negative Age-Gap; and OR 0.75, 95% CI 0.67-0.84 for those with ≥2 SD of a negative Age-Gap), respectively. This association remained significant after multivariate adjustment, Fig. A and increased with CAC burden, Fig. B , all P for trend <0.01. Conclusions: The difference between physiologic AI-ECG age and chronologic age, something likely representing rate of biological aging, is associated with presence of CAC, further supporting the hypothesis that AI-ECG age can identify people more likely to have subclinical coronary atherosclerosis.

Evaluating the Electrocardiographic Characteristics of Transgender Patients Undergoing Gender-affirming Hormone Therapy with Artificial Intelligence Introduction: An artificial intelligence (AI) electrocardiogram (ECG) algorithm has been demonstrated to identify a patient’s birth designated sex with 90.4% accuracy with an AUC = 0.97. However, the algorithm has not been evaluated in transgender and gender diverse individuals. We aimed to determine how the algorithm output varies among transgender individuals seen at the Mayo Clinic Transgender and Intersex Specialty Care Clinic (TISCC) who are undergoing gender-affirming hormonal treatment (GAHT). Methods: We applied the algorithm (which assessed the probability of “male” between 0 and 1, with < 0.5 considered as “female” and > 0.5 as “male”) to ECGs obtained from patients in the TISCC before and after the initiation of GAHT. GAHT frequently involves the use of testosterone in transgender men, and estrogen and anti-androgen agents in transgender women. Patient characteristics, including the date of the initiation of GAHT, were collected through chart review. Results: Among transgender women, the AI model probability of “male” decreased from 0.84± 0.25 (12/86 classified as males) to 0.59±0.36 (68/173 classified as male) with GAHT (p < 7.8e-10). Among transgender men, the AI model probability of “male” increased from 0.16 ± 0.28 (7/47 classified as male) to 0.41 ±0.38 (22/53 classified as male) with GAHT (p < 2.4e-4). Conclusion: After GAHT initiation, the Mayo Clinic AI ECG algorithm assigns a higher probability of being male among transgender men and a lower probability of being male among transgender women. These findings warrant further investigation into the electrocardiographic differences across genders and the effects of GAHT on the human heart.