Citations

Abstract Background The determination of left ventricular diastolic dysfunction (LVDD) and filling pressure in patients with significant (>moderate) mitral regurgitation (MR) poses a complex challenge. We recently validated an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to detect LVDD and estimate LV filling pressures. Methods This retrospective study sought to examine the risk of all-cause mortality across AI-ECG LVDD grades. Patients from the AI-ECG LVDD study test cohort who underwent transthoracic echocardiography confirming significant MR and an ECG within fourteen days of each other at Mayo Clinic between 09/2001 and 06/2022 were included. LVDD status was determined based on the index ECG, and patients were categorized into groups representing normal diastolic function (NDF) or LVDD grades 1 (G1), 2 (G2), or 3 (G3). Results Of 4,019 patients with significant MR (mean age 69.8, 49.0% women), 1,175 (29.2%), 1,881 (46.8%), and 963 (24.0%) were classified by AI-ECG as NDF/G1-LVDD, G2-LVDD, and G3-LVDD, respectively. The median mitral effective regurgitant orifice area was 26 mm² (interquartile range 20-36 mm²). Over a median follow-up of 3.5 years, 1,636 (40.7%) patients died. In multivariable survival analysis adjusted for multiple risk factors, a worse AI-ECG LVDD grade was independently associated with an increased death risk [G2DD: adjusted hazard ratio (aHR) 1.98 (95% confidence interval (95% CI) 1.63-2.40), G3DD: aHR 2.59 (95% CI 2.09-3.20)]. These findings were consistent when accounting for mitral valve intervention and among patients with >moderate MR or after mitral valve intervention. Conclusion In patients with significant MR, the grading of LVDD by AI-ECG is independently associated with all-cause mortality.

Abstract Aims Gender-affirming hormone therapy (GAHT) is used by some transgender individuals (TG), who comprise 1.4% of US population. However, the effects of GAHT on electrocardiogram (ECG) remain unknown. The objective is to assess the effects of GAHT on ECG changes in TG. Methods and results Twelve-lead ECGs of TG on GAHT at the Mayo Clinic were inspected using a validated artificial intelligence (AI) algorithm. The algorithm assigns a patient’s ECG male pattern probability on a scale of 0 (female) to 1 (male). In the primary analysis, done separately for transgender women (TGW) and transgender men (TGM), 12-lead ECGs were used to estimate the male pattern probability before and after GAHT. In a subanalysis, only patients with both pre- and post-GAHT EGCs were included. Further, the autopopulated PR, QRS, and QTc intervals were compared before and after GAHT. Among TGW (n = 86), the probability (mean ± SD) of an ECG male pattern was 0.84 ± 0.25 in the pre-GAHT group, and it was lowered to 0.59 ± 0.36 in the post-GAHT group (n = 173, P < 7.8 × 10−10). Conversely, among TGM, male pattern probability was 0.16 ± 0.28 (n = 47) in the pre-GAHT group, and it was higher at 0.41 ± 0.38 in the post-GAHT group (n = 53, P < 2.4×10−4). The trend persisted in the subanalysis. Furthermore, both the PR (P = 5.68 × 10−4) and QTc intervals (P = 6.65×10−6) prolonged among TGW. Among TGM, the QTc interval shortened (P = 4.8 × 10−2). Conclusion Among TG, GAHT is associated with ECG changes trending towards gender congruence, as determined by the AI algorithm and ECG intervals. Prospective studies are warranted to understand GAHT effects on cardiac structure and function.

Abstract Aims We aim to determine if our previously validated, diagnostic artificial intelligence (AI) electrocardiogram (ECG) model is prognostic for survival among patients with cardiac amyloidosis (CA). Methods A total of 2533 patients with CA (1834 with light chain amyloidosis (AL), 530 with wild-type transthyretin amyloid protein (ATTRwt) and 169 with hereditary transthyretin amyloid (ATTRv)] were included. An amyloid AI ECG (A2E) score was calculated for each patient reflecting the likelihood of CA. CA stage was calculated using the European modification of the Mayo 2004 criteria for AL and Mayo stage for transthyretin amyloid (ATTR). Risk of death was modelled using Cox proportional hazards, and Kaplan–Meier was used to estimate survival. Results Median age of the cohort was 67 [inter-quartile ratio (IQR) 59, 74], and 71.6% were male. The median overall survival for the cohort was 35.6 months [95% confidence interval (CI) 32.3, 39.5]. For AL, ATTRwt and ATTRv, respectively, median survival was 22.9 (95% CI 19.2, 28.2), 47.2 (95% CI 43.4, 52.3) and 61.4 (95% CI 48.7, 75.9) months. On univariate analysis, an increasing A2E score was associated with more than a two-fold risk of all-cause death. On multivariable analysis, the A2E score retained its importance with a risk ratio of 2.0 (95% CI 1.58, 2.55) in the AL group and 2.7 (95% CI 1.81, 4.24) in the ATTR group. Conclusions Among patients with AL and ATTR amyloidosis, the A2E model helps to stratify risk of CA and adds another dimension of prognostication.

Abstract Artificial intelligence (AI)–enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can identify patterns predictive of future adverse cardiac events. We hypothesized that such an approach would provide prognostic information for the risk of cardiac complications and mortality in patients undergoing hematopoietic cell transplantation (HCT). We retrospectively subjected ECGs obtained before HCT to an externally trained, deep-learning model designed to predict the risk of atrial fibrillation (AF). Included were 1377 patients (849 autologous [auto] HCT and 528 allogeneic [allo] HCT recipients). The median follow-up was 2.9 years. The 3-year cumulative incidence of AF was 9% (95% confidence interval [CI], 7-12) in patients who underwent auto HCT and 13% (10%-16%) in patients who underwent allo HCT. In the entire cohort, pre-HCT AI-ECG estimate of AF risk correlated highly with the development of clinical AF (hazard ratio [HR], 7.37; 95% CI, 3.53-15.4; P < .001), inferior survival (HR, 2.4; 95% CI, 1.3-4.5; P = .004), and greater risk of nonrelapse mortality (NRM; HR, 95% CI, 3.36; 1.39-8.13; P = .007), without increased risk of relapse. Association with mortality was only noted in allo HCT recipients, where the risk of NRM was greater. The use of cyclophosphamide after transplantation resulted in greater 90-day incidence of AF (13% vs 5%; P = .01) compared to calcineurin inhibitor–based graft-versus-host disease prophylaxis, corresponding to temporal changes in AI-ECG AF prediction after HCT. In summary, AI-ECG can inform risk of posttransplant cardiac outcomes and survival in HCT patients and represents a novel strategy for personalized risk assessment.

Abstract Aims To test whether an index based on the combination of demographics and body volumes obtained with a multisensor 3D body volume (3D-BV) scanner and biplane imaging using a mobile application (myBVI®) will reliably predict the severity and presence of metabolic syndrome (MS). Methods and results We enrolled 1280 consecutive subjects who completed study protocol measurements, including 3D-BV and myBVI®. Body volumes and demographics were screened using the least absolute shrinkage and selection operator to select features associated with an MS severity score and prevalence. We randomly selected 80% of the subjects to train the models, and performance was assessed in 20% of the remaining observations and externally validated on 133 volunteers who prospectively underwent myBVI® measurements. The mean ± SD age was 43.7 ± 12.2 years, 63.7% were women, body mass index (BMI) was 28.2 ± 6.2 kg/m2, and 30.2% had MS and an MS severity z-score of −0.2 ± 0.9. Features β coefficients equal to zero were removed from the model, and 14 were included in the final model and used to calculate the body volume index (BVI), demonstrating an area under the receiving operating curve (AUC) of 0.83 in the validation set. The myBVI® cohort had a mean age of 33 ± 10.3 years, 61% of whom were women, 10.5% MS, an average MS severity z-score of −0.8, and an AUC of 0.88. Conclusion The described BVI model was associated with an increased severity and prevalence of MS compared with BMI and waist-to-hip ratio. Validation of the BVI had excellent performance when using myBVI®. This model could serve as a powerful screening tool for identifying MS.

Introduction: Asymptomatic LV systolic dysfunction is often underdiagnosed, leading to missed opportunities for treatment. In a recently completed pragmatic RCT, an AI algorithm performed on the ECGs ordered as part of routine practice increased early diagnosis of low EF. The current study aimed to understand whether the effectiveness of the AI algorithm integrated into routine care differed by the socioeconomic status of the patient. Methods: The main findings of the RCT were previously published. Clinicians in the intervention group had access to AI results (i.e., a positive or negative screening result, with positive results indicating a high likelihood of low EF). When a positive result was found, clinicians in the intervention group received an alert that encouraged them to order TTE if considered clinically appropriate. Clinicians in the control group did not have access or received alerts. Area deprivation index (ADI) was used to measure area-level social determinants of health. This study included 16,992 patients (control 8,225; intervention: 8,767) who had ECG performed for any indications between 8/5/2019 and 3/31/2020, no prior HF, and a valid address to derive ADI. The primary endpoint was a new diagnosis of EF≤50% within 3 months. Results: The mean age was 60.5±17.6 y. AI increased the diagnosis of low EF in the overall cohort (OR 1.42 [1.13, 1.78], p=0.01). The magnitude of benefit was greater in patients with higher socioeconomic status, e.g., living in an area that is less deprived (OR 1.88 [1.16, 3.05]), with higher median income, lower poverty rate, higher education level, better housing conditions, and higher proportion of white-collar workers, although the interaction was not statistically significant (Figure). Conclusions: AI enables early diagnosis of low EF and the magnitude of benefit might be numerically greater in patients with higher socioeconomic status, possibly due to variations in access to or referral for subsequent testing.

Introduction: LMNA encodes the nuclear envelope proteins lamin A/C that are involved in ageing processes. Mutations in LMNA cause a spectrum of phenotypes which include muscular dystrophy, arrhythmogenic dilated cardiomyopathy, peripheral neuropathy, progeroid syndrome and overlap syndrome. Early diagnosis is crucial to prevent sudden cardiac death. We hypothesized that LMNA mutations result in an ECG biological age older than chronological age. Methods: In this study, we used AI-enabled ECG network to investigate standard digital 12-lead ECGs in genetically characterized LMNA patients. Results: We identified 33 LMNA patients with 331, 12-lead ECG available for analysis. Median age at symptoms onset was 23.5 (range: <1 to 61 years;15 females). Mean follow up time was 86 months (range: <1 to 367 months). Symptoms at onset included muscle weakness (n=10) or cardiac symptoms (n=18) and asymptomatic (n=5). We included a total of 1532 age sex matched controls. We found significant differences in positive age-gap (biologically older than chronological age) comparing LMNA patients (median of 17.5) versus controls (4.9 years; p<0.001, Figure 1 ). AI-ECG age with 10 years or higher age-gap was observed in 73% of LMNA patients compared to 27% in the controls (p<0.001). Consecutive serial analysis for all ECG’s in the study versus predicted age showed faster aging when comparing the rates of age-gap overtime in the LMNA group compared to controls (p<0.0001, Figure 2 ). Moreover, the subset analysis of 23 ECG in the 5 asymptomatic patients versus controls also showed significantly higher AI-ECG age-gap (p=0.004) Conclusions: An AI-ECG algorithm to detect biological age effectively demonstrates accelerated aging in patients with LMNA mutations related to physiological aging. Our findings further validate the role of AI-ECG to detect accelerated aging, expanding previous studies demonstrating higher mortality among individuals with in increased positive age-gap.

Background: The Metabolically Obese Normal Weight (MONW) phenotype has been described as a unique subgroup of individuals with metabolic dysregulation similar to obese individuals, but not considered obese per se . In theory, these individuals have an adiposity-independent substrate predisposing them to metabolic dysregulation. We tested whether central obesity explains the metabolic abnormalities in persons with MONW in a prospective population-based cohort in the Czech Republic. Methods: We used data from Kardiovize Brno 2030 study, a prospective community-based cohort in Central Europe. We defined obesity using World Health Organization categories for body mass index (BMI) and central obesity by waist circumference (WC≥ 94 cm in men and ≥ 80 cm in women). Metabolically obese (MO) was defined using AHA criteria by having three or more of the following: Hypertriglyceridemia (≥ 1.7 mmol/l); HDL cholesterol level <1.0 mmol/l in men or <1.3 mmol/l in women; elevated blood pressure (systolic ≥ 130 mmHg and/or diastolic ≥ 85 mmHg) or antihypertensive drug treatment or history of hypertension; elevated fasting plasma glucose ≥ 5.6 mmol/l; or drug treatment and central obesity. Results: Of the 2,056 participants (46% men; mean age= 47.2 years, SD=11.2) included in the current analysis, 975 (47.4%) had normal weight and 696 (33.8%) were overweight. Prevalence of MO in the overall sample was 20.6% (n=424), 3.7% (n=36) among those with normal BMI and 9.9% (n=204) among overweight individuals. From the 36 subjects with MONW, 33 (91.6%) had central obesity by WC, while 201/204 (98.5%) with metabolically obese overweight, had central obesity by WC. Remarkably, in a subgroup of people with no metabolic obesity factors nobody had central obesity, see Figure . Conclusions: Metabolic dysregulation in people labeled as MONW can be explained by excessive visceral adiposity and not by an inherent predisposition, independent of adiposity.These findings underscore the importance of identifying central obesity in clinical practice even if BMI is below the obese criteria.

Objectives: To assess compliance with chronic coronary artery disease (CAD) secondary prevention guidelines in the USA, to determine time trends, and to identify potential sociodemographic predictors of low compliance. Design, Setting and Participants: We analyzed the National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2010, representative of non-institutionalized civilian population in the USA. We included people with self-reported previous diagnosis of CAD, angina or myocardial infarction (MI). Primary Outcome: Adherence to secondary prevention guidelines including blood pressure control, lipid control, not smoking, normal body mass index, physical activity at goal, and meeting all 5 outcomes (“all-or-none”). Use of antiplatelet therapy was reported and analyzed from 1999-2004. Secondary Outcome: Use of beta-blocker therapy in people with history of MI; in diabetes (DM), use of ACE-inhibitors or angiotensin receptor blocker, and HgbA1C <7%. Age, gender, race, income, education and insurance status were evaluated as covariates. Analyses were weighed according to Center for Disease Control guidelines. Results: There were 62,160 individuals in the cycle data of which 2,615 comprised the study group, representative of 73,574,548 individuals in the USA. Mean age was 65 yr, 58% men, 79% white. Only 2.8% of people with CAD met all 5 main outcomes. Only blood pressure and LDL control showed significant improvement over time, see Figure. Antiplatelet therapy was reported in 58.2%. Of those with history of MI, only 50.5% were on a beta-blocker and of those with history of DM, 59.4% were using either an ACE-I or an ARB; and 54% had a HgbA1C less than 7%. When adjusting for demographics, no significant differences were found regarding compliance with all 5 main outcomes. Conclusions: Adherence to guidelines for secondary prevention of CAD is suboptimal. There is an immense opportunity for improvement in secondary prevention for CAD in the USA.

Purpose: Subjects with normal weight obesity (NWO), defined as normal BMI but high body fat (BF)%, are at increased cardiovascular risk. Factors contributing to NWO are not well studied. We hypothesized that NWO is associated with reduced physical activity and unhealthy eating patterns. Methods: We analyzed 5942 people ≥18 years with normal BMI (18.5-24.9 kg/m 2 ) from the NHANES III survey (1988-1994), a representative sample of the US population. Subjects were classified as NWO based on BF% (>35% in women and >25% in men). Leisure-time physical activity was measured with a standard survey. Subjects were stratified as INACTIVE (no physical activity reported), ACTIVE (3-6 METs for ≥ 5 times/week or >6 METs, ≥3 times/week), or insufficiently active (those who were not INACTIVE but did not meet criteria for ACTIVE). Total activity was expressed in METs/hr/month, assuming an exercise duration of 30 minutes for each day reported. A Healthy Eating Index was based on 24-hour dietary recall data. Analyses were weighted for the complex study design. Results: Mean age was 43 years, 54.4% were men; 1170 people (19.7%) with normal BMI had NWO. Table shows demographics, physical activity and nutritional habits by group. After controlling for age, gender, poverty levels, smoking status and Healthy Eating Index, METs/hr/month was an independent predictor of NWO (OR per 100 MET/hours/month is 0.72(95%CI 0.60;0.88). People who were inactive and insufficiently active had higher odds of NWO as compared with active people (OR-1.47; 95%CI 1.10;1.96 and OR-1.60, 95% CI 1.25; 2.05, respectively). Also, strength training was independently associated with a decreased odds of NWO (0.63; 95%CI 0.44;0.89). Dietary pattern was not predictive of NWO. Conclusions: Physical activity, particularly resistance exercise, but not diet, was associated with NWO independently of potential confounders. Promoting physical activity may be an important interventional strategy in these patients.