Attia, Zachi I.

Introduction: Left ventricular diastolic function and filling pressure (FP) can be assessed by echocardiography and cardiac catheterization. Recently, a novel artificial intelligence (AI)-enabled ECG has proven to be effective in identifying increased FP determined by echocardiographic assessment of diastolic function. Aims: We aimed to compare a prognostic value of AI-ECG determination of FP compared to echocardiography and catheterization. Methods: This study included 4,213 patients who had a transthoracic echocardiogram and ECG within 30 days before hemodynamic cardiac catheterization. Increased FP was defined as pulmonary artery wedge pressure >15 mmHg by catheterization and grade 2 or 3 diastolic dysfunction by echocardiography based on diastolic function guidelines. Increased FP by ECG was identified using previously developed AI-ECG. AI-ECG performance was evaluated using the area under the curve (AUC) of the receiver operating curve. We assessed the risk of all-cause mortality using Kaplan-Meier estimate and multivariable Cox proportional-hazards models. Results: AI-ECG had AUCs of 0.8 and 0.7 for predicting increased FP determined by echocardiography and catheterization, respectively. Death was observed in 2,776 (34%) patients over a median follow-up of 7.7 years (IQR 7.1, 8.3). Survival of patients with normal and increased FP by AI-ECG, Echocardiography, and catheterization are shown in Figure 1a. Concordant diagnoses of normal and increased FP by AI-ECG and catheterization or echocardiography had the highest and lowest survival rates, respectively. When FP by AI-ECG was discordant, survival was better predicted by AI-ECG compared to echocardiography and catheterization (Figure 1b). Conclusion: AI-ECG for FP has a potential to discriminate prognosis further compared to FP determined by echocardiography or catheterization.

Background: Early diagnosis of heart failure with reduced ejection fraction (HFrEF) can lead to reduced healthcare costs and improved outcomes. In this study, we evaluated the economic effectiveness of an AI algorithm for detecting low ejection fraction using 12-lead ECG data. Aims: The primary aim was to estimate the health economic impact of the AI-ECG algorithm intervention to detect low ejection fraction. Methods: The study population consisted of 113,719 individuals without a prior history of HFrEF who visited a multistate health system between 2010-2018 with 12-lead ECG data and an echocardiogram within 1 year. The AI-ECG algorithm was run retrospectively, and projected costs were estimated assuming each patient incurred the average cost based on their age group and AI-detected heart failure stage. Hospitalization costs were estimated using Medicare reimbursement rates, and economic benefits from increased survival were estimated using a willingness to pay of $50,000 per quality-adjusted life year (QALY) gained. Results: At the optimal model threshold, the total cost savings per patient (including benefits from QALYs gained) for the AI-ECG algorithm vs. standard-of-care on the overall study population were projected to be $79.11 (95% CI: [$53.77, $104.44]) and $124.93 (95% CI: [$83.99, $165.88]) for the 2 and 4 year periods, respectively. For patients over 50 years old, the projected total cost savings were $85.56 (95% CI: [$56.35, $114.78]) and $135.28 (95% CI: [$88.18, $182.38]) per patient for the 2 and 4 year periods, respectively. See Figure. Conclusions: The AI-ECG algorithm to detect low ejection fraction is a cost-effective early screening tool for HFrEF.

Background: Depression is prevalent in almost half of patients with cardiovascular disease (CVD) and is associated with CVD and mortality. We used an artificial intelligence algorithm using ECG that predicts physiological age (ECG-Age) that has been linked to increased total and CVD mortality and CVD risk factors. We hypothesized that depressive symptoms would be associated with physiologic aging, as measured by AI-ECG Methods: A previously validated algorithm was applied to determine ECG-Age to 105,732 consecutive ECGs of individuals age ≥18 who sought care at Mayo Clinic between June 2019 and January 2022 and filled the Patient Health Questionnaire-9 (PHQ-9) within a month of ECG. PHQ-9 ascertains severity and frequency of depressive symptoms with values from 0 - 27 and were categorized as (None≤4, Mild 5-9, Moderate 10-14, Moderately severe 15-19 and Severe ≥20). Delta-Age was defined as ECG-Age minus chronological age and accelerated aging was defined as a Delta-Age >1SD above the mean. Logistic regression analyses associated PHQ-9 scores with positive Delta-Age while adjusting for CVD risk factors Results: Mean chronological age was 57±17 yr and mean ECG-Age 58±14 yr (R2 0.85, P < 0.0001), Delta-Age 0.71±9.40 yr, 58% women, average PHQ-9 5±6, 61% had none, 20% mild, 10 % moderate, 5%moderately severe and 4% had severe depressive symptoms. Delta-Age was 2.10 years higher in those with depressive symptoms, reflecting accelerated aging. Compared to those with no depressive symptoms, those with severe symptoms were 1.5 times more likely to have Delta-Age >1SD (OR 1.52 CI 95%1.73-1.33), independently of CV risk factors and the Charlson comorbidity index. Figure. There is significant attenuation of the association once accounting for age, sex and CVD risk factors Conclusions: Depressive symptoms are associated with physiologic aging, highlighting the role of depression in physiologic aging and the potential of preventive strategies in patients with depression and CVD

Introduction: The effect of obesity on physiologic aging has not been completely established. We previously developed an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm that predicts age and determines that the difference between AI-ECG age and chronological age (Age-Gap) may reflect accelerated aging, as it is associated with increased total and cardiovascular (CV) mortality. Hypothesis: Obesity is associated with an increased Age-Gap reflecting morbidity and accelerated aging. Methods: We included adults undergoing a wellness evaluation including, body fat percentage (BF%) determination by air displacement plethysmography (Bod-Pod®) and a 12-lead ECG within 2 years. We excluded those with coronary artery disease, congestive heart failure, stroke or being underweight defined as having a BMI<18.5 kg/m2. Logistic regression models tested the association between obesity by BMI (≥30 kg/m2) or BF% quartiles (≥35% for men and ≥25 in women) and being older by AI-ECG. Results: We included 1295 subjects (70% women), mean chronological age 46.7 ± 13.1 years. Mean Age-gap positive difference compared to those with BMI<25 was 1.39, 2.06 and 2.83 for those with BMI 25-29.9, 30-34.9, and >35 respectively (p<0.05). In the BF% group, Age-gap difference compared to those in the 1st quartile was 0.008, 0.37 and 1.52 for those in the 2nd, 3rd, and 4th quartile (p<0.05). Those>3 years older by ECG were more likely to have obesity by BMI and BF%, see figure. Conclusions: Obesity, either by BMI or BF%, is associated with increased physiologic age as measured by AI-ECG, highlighting the importance of obesity as a health threat.

Abstract Aims ECG abnormalities are often the first signs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and we hypothesized that an artificial intelligence (AI)-enhanced ECG could help identify patients with ARVC and serve as a valuable disease-detection tool. Methods and results We created a convolutional neural network to detect ARVC using a 12-lead ECG. All patients with ARVC who met the 2010 task force criteria and had disease-causative genetic variants were included. All case ECGs were randomly assigned in an 8:1:1 ratio into training, validation, and testing groups. The case ECGs were age- and sex-matched with control ECGs at our institution in a 1:100 ratio. Seventy-seven patients (51% male; mean age 47.2 ± 19.9), including 56 patients with PKP2, 7 with DSG2, 6 with DSC2, 6 with DSP, and 2 with JUP were included. The model was trained using 61 case ECGs and 5009 control ECGs; validated with 7 case ECGs and 678 control ECGs and tested in 22 case ECGs and 1256 control ECGs. The sensitivity, specificity, positive and negative predictive values of the model were 77.3, 62.9, 3.32, and 99.4%, respectively. The area under the curve for rhythm ECG and median beat ECG was 0.75 and 0.76, respectively. Conclusion Our study found that the model performed well in excluding ARVC and supports the concept that the AI ECG can serve as a biomarker for ARVC if a larger cohort were available for network training. A multicentre study including patients with ARVC from other centres would be the next step in refining, testing, and validating this algorithm.

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 &gt;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 &gt;mild functional MR in the absence of &gt;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 &lt;.001] and the SR group [hazard ratio 1.19 (95% CI 1.12-1.25) per 10%, p &lt;.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) &lt; 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 &lt;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 &lt;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 &lt;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 &gt; 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 (&gt;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.