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.

Introduction: Limited validated rapid dietary screening tools are available for patient use for actionable goal setting to improve health and reduce cardiovascular risk. The Healthy Index (HEI) Score measures compliance with the 2015-2020 Dietary Guidelines for Americans but requires completion of an extensive food frequency questionnaire (FFQ) to compute, which is time consuming and impractical. Hypothesis: A 19-item dietary survey that assesses consumption of common food groups known to affect health is correlated to the gold standard FFQ and HEI score and can be further reduced without affecting validity. Methods: An evidence-based 19-item Eating Assessment Tool (EAT™) of common food groups was created through literature review and expert consensus. We then conducted a cross-sectional survey of adult patients from a preventive cardiology clinic or cardiac rehabilitation and of healthy volunteers (n=628, mean age= 37 y, 76% women). Participants completed an online 156-item FFQ which was used to calculate the HEI score using standard methods. The association between each EAT™ question and HEI score category was analyzed by Kruskal-Walis test. Linear regression models were used to identify univariable and multivariable predictors for HEI score. The final model of Mini-EAT™ was validated by 5-fold cross validation. Results: The 19-item EAT™ had good correlation with the HEI (r=0.77) and was subsequently reduced to the 8 items independently predictive of the HEI score: fruits, vegetables, whole grains, refined grains, legumes/nuts/seeds, low-fat dairy, high-fat dairy, and sweets consumption, without affecting the predictive ability of the tool (r=0.76). Conclusions: Mini-EAT™ is an 8-item validated brief dietary screener that correlates well with a comprehensive FFQ. Future studies to test the Mini-EAT™’s validity in diverse populations and for development of clinical decision support systems to capture changes over time are needed.

Introduction: Transthyretin cardiac amyloidosis (TTR-CA) can cause heart failure with preserved EF (HFpEF). We previously reported a clinical/echo-based score (TCAS) and defined a cut-off that predicted risk of TTR-CA in HFpEF. Herein, we assess the comparative performance of an artificial-intelligence ECG-based screening tool (AI-ECG). Hypothesis: AI-ECG successfully identifies TTR-CA in HFpEF cohorts across a range of TTR-CA prevalence Methods: Referral HFpEF cohort was constructed from a referral population of HFpEF patients (EF ≥ 40%) undergoing 99m Tc-pyrophosphate scintigraphy (PYP) at Mayo Clinic (2013-2020, n=484, 38% with TTR-CA). Community HFpEF cohort was prospectively screened with PYP (n=275; age ≥ 60, LV wall thickness (WT) ≥ 12 mm, 4% with TTR-CA). TCAS was built using a multivariable logistic regression model and converted to a simple point score comprised of age (60-69, 70-79, ≥ 80: +2, +3, +4 points respectively), male sex (+2), relative WT > 0.57 (+2), posterior WT ≥ 12 mm (+1), EF < 60% (+1), and hypertension diagnosis (-1). TCAS was validated in the community cohort. A deep neural network trained to predict cardiac amyloidosis from a single 12-lead ECG, was applied to both referral and community cohorts. Patients were excluded if ECG was not available ≤ 12 months before PYP or if used in development of the AI-ECG. Standard ROC methods were applied. Youden’s index was used to define optimal cut-off. Results: Predictive characteristics of AI-ECG were strong and compared favorably with TCAS across a range of true TTR-CA prevalence (Table). Conclusions: Using a single ECG, AI-ECG effectively predicts TTR-CA with an excellent negative predictive value to rule out TTR-CA and a positive predictive value sufficient to warrant specific TTR-CA testing. This was particularly evident in the community. AI-ECG holds promise for simple, cost-effective global screening of at-risk populations, especially those with limited access to advanced cardiac imaging.

Objective We studied the effect of a lifestyle therapy program using the resources already available in a cardiac rehabilitation setting for patients at risk for metabolic syndrome (MetS). Methods We created a six-week cardiometabolic program (CMP) using facilities and resources of a cardiac rehabilitation setting. The CMP included a minimum of 6 once-a-week 60 min sessions consisting of exercise training and nutrition instruction, in addition to 6 once-a-week 60 min group behavioral modification classes following the LEARN program. We also assessed the outcomes in 239 patients (pts) who had at either obesity or hyperglycemia: 58 pts had enrolled and completed the CMP, 58 pts had signed up for the CMP but either never attended or dropped out early (Control 1) and had follow-up data, and 123 pts had not signed up for the CMP but had been seen in the outpatient preventive cardiology practice at the Mayo Clinic (Control 2). We compared changes in components of the MetS from baseline to six weeks (for the CMP group) and at 6 months for all pts. Results Of 187 people who signed up for the CMP, only 62 completed the program and from those, 58 gave authorization to use their clinical data in research. Of the 125 that never attended or dropped out early, 58 had follow-up data. Clinical characteristics of the CMP, Control 1, and Control 2 groups and improvement in each of the outcome measures are shown in the Table. In the CMP group, there was a significant improvement at 6 weeks in waist circumference, weight, diastolic blood pressure and total cholesterol. At 6 months the fasting glucose had also significantly improved. In contrast, improvements in Control 1 and Control 2 were modest at best. See Table. When comparing the 6 month changes in the CMP and Control 1, those in the CMP had more pronounced weight loss (-4.5±5 kg vs -0.14±6 kg, p<0.001); decrease in systolic blood pressure (-1.1±17 mmHg vs +9.6±20 mmHg, p=0.004); and decrease in diastolic blood pressure (-4.6±11 mmHg vs +3.4±15 mmHg, p=0.0017). Similarly, comparing CMP vs Control 2, a significantly greater drop in body weight (-4.5±5 kg vs -0.9±3 kg, p<0.001) and in diastolic blood pressure (-4.6±11 mmHg vs -0.7±9 mmHg, p=0.0202) was noted in the CMP. The change in the number of MetS components per patient dropped significantly, favoring the CMP group compared with the control groups. Conclusion A lifestyle therapy program for patients at risk for MetS can be implemented using existing resources of a cardiac rehabilitation program, although enrollment and completion rates are low. Such a program, however, is effective at improving cardiometabolic outcomes in individuals at risk for MetS.

Background: Physical activity (PA) prevents cardiovascular (CV) disease and maintains health. It is unclear which factors predict the adoption or maintenance of PA at a worksite wellness center. Methods: To assess the predictors of increase in PA we conducted a retrospective study on 7237 adults attending a worksite wellness center between September 2008 and October 2011. Upon joining the wellness center, participants completed an electronic or paper survey that assessed for the presence of CV risk factors, height, weight, self-perceived health behaviors and psychosocial factors using eleven validated 0-10 point scale. We included 1927 participants who completed the standard survey at the time of enrollment and at 12 months (+/- 90 days). For logistic regression analysis PA increase at 12 months was defined as an increase of ≥2 points of self-reported PA, on a 0 to 10 point scale, or maintenance of a high level of PA at follow up (FU). Results: Mean (SD) age was 38 (11) yr., BMI was 26.93 (6.06) kg/m 2 and 32% were men. From the study sample, 717 (37%) participants increased their PA level or maintained a high level of PA at FU. When comparing people who increased or maintained a high level of PA with the group without improvement or a decline in PA, there were differences in age and BMI: age [39 (11) vs. 38 (11) yr.; p=0.04] and BMI [27.37 (5.92) vs. 26.68 (5.92) kg/m 2 ; p=0.024]. The Table reports the results of the multivariate logistic regression model. While CV risk factors (tobacco use, hyperglycemia/diabetes mellitus, and high blood cholesterol), perceived stress level and self-perception of being overweight were not significant predictors of increase PA, low muscular strength and flexibility and interactions with close friends were significantly associated with PA. Conclusion: A significant proportion of people, over one-third, enrolled in a wellness center increase their PA level or maintain high levels of PA at 12 months after enrollment. Increasing age and BMI, and a measure of social support; interaction with close friends, and perceived muscular strength and flexibility, predict increase in PA. Presence of CV risk factors do not predict increase in PA. Tailoring programs to address these domains may help younger, less fit, and those with less social support improve their PA level.

Introduction: Estimating body fat content has shown to better predict adiposity-related cardiovascular risk than the commonly used body mass index (BMI). The white-light 3D Body Volume Index Scanner (BVI) is a non-invasive device normally used in the clothing industry to asses body shapes and sizes. We assessed the hypothesis that volume obtained by BVI is comparable to the volume obtained by air displacement plethysmography (BodPod) (COSMED Concord, CA, USA) and thus capable of assessing body fat mass using the bicompartmental principles of body composition. Methods: We compared BVI to BodPod, a validated bicompartmental method to assess body fat percent that uses pressure/volume relationships in isothermal conditions to estimate body volume. Volume is then used to calculate body density (BD) applying the formula density= Body mass/Volume. Body fat mass percentage is then calculated using the Siri formula (4.95/BD – 4.50) х 100. Subjects were members of the Mayo Clinic Dan Abraham Healthy Living Center undergoing a wellness evaluation. Measurements from both devices were obtained the same day. A prediction model for total BodPod volume was developed using linear regression based on 80% of the observations (N=971), as follows: Predicted BodPod Volume (L) = 9.498 + 0.805*(BVI volume, L) - 0.0411*(Age, years) - 3.295*(Male=0, Female=1) + 0.0554*(BVI volume, L)*(Male=0, Female=1) + 0.0282*(Age, years)*(Male=0, Female=1). Predictions for BodPod volume based on the estimated model were then calculated for the remaining 20% (N=243) and compared to the volume measured by the BodPod. Results: Mean (SEM) age was 41.5(0.41) years, 39.4% were men, weight 81.6(0.67) kg, BMI was 27.8 (0.20) kg/m2. Average difference between volume measured by BodPod- predicted volume by BVI was 0.0 L, median: -0.4 L, IQR: -1.8 L to 1.5 L. Average difference between Body fat % measured - predicted was -1%, median: -2.7%, IQR: -13.2 to 9.9, R2 = 0.9845 (Figure 1-A, B). Conclusion: Body fat mass can be estimated using volume measurements obtained by a white- light 3D body scanner (Figure 1-A).

Background: Artificial intelligence (AI)-based analysis of the electrocardiogram (ECG) can estimate the probability of having atrial fibrillation (AF) while in sinus rhythm. We evaluated the association between the probability of AF using AI-ECG and incident ischemic stroke (I-Stroke) and cardiovascular (CV) mortality. Methods: We evaluated a community-based cohort of consecutive patients that sought primary care in Olmsted County, MN, from the years 1998 to 2000 followed-up through March 2016. Inclusion criteria were ages 40-79, an electrocardiogram within 2 years and complete CHA2DS2-VASc data. We excluded those with known atherosclerotic CV disease or AF. The AI network was developed in 180,000 patients (649,931 ECG). The primary endpoint was incident I-Stroke; secondary endpoint was I-Stroke or CV mortality; validated in duplicate. Multivariate cox models adjusted for age, sex, history of hypertension, diabetes, heart failure, and in CHA2DS2-VASc Score subgroups (categorized as low=0, moderate=1, and high risk ≥2). Results: We included 14,601 patients, mean age 53.2±13.08 (51% women, 95% whites), CHA2DS2-VASc score 1.3±1.2, median follow-up of 16.3 years, IQR 14.5-17.5, 727 (4.9%) had an I-Stroke and 1309 (8.9%) had I-Stroke or died from CV causes. When comparing the 5 th vs the 1 st quintile, those with high AI-ECG AF probability had increased risk for I-Stroke (HR 3.25, 95%CI 2.55-4.14, P-for trend <.0001 Figure-A ) and ischemic stroke or CV mortality (HR 5.73, 95%CI 4.69-6.99, P-for trend <.0001 Figure-B ). This remained significant after adjustments. Furthermore, AI-ECG AF probability was associated with I-Stroke across CHA2DS2-VASc risk levels (HR 2.43 for high, 2.33 for moderate, 1.98 for low CHA2DS2-VASc risk, all P<0.01). Conclusion: The AI-ECG AF algorithm predicts long-term I-Stroke and CV mortality and may refine CHA2DS2-VASc. These results highlight the utility of the AI enabled ECG when estimating I-Stroke or CV mortality risk.

Introduction: Plasma ceramides (CER) are sphingolipids associated with adverse events and mortality in patients with established coronary artery disease (CAD) and/or risk factors for CAD. The ability of these same specific ratios of CER to predict major adverse cardiovascular events (MACE) in subjects without CAD in the community is less certain. We tested the hypothesis that CER are associated with MACE in a community-based cohort free of CAD at enrollment. Methods: In a prospective community-based cohort we performed passive follow-up using a record linkage system to ascertain the composite outcome of MACE, defined as acute myocardial infarction, coronary revascularization (coronary artery bypass grafting or percutaneous coronary intervention), stroke or death. CER were analyzed as continuous variables, ratios and quintiles adjusting for potential confounders. Results: We included 1221 subjects, 49% males, mean age ± (SD) 61.1±9.6 years. After a median follow-up of 16 years (IQR 1.7), 297 patients had a MACE: Myocardial infarction (105), coronary artery bypass surgery (53), percutaneous coronary intervention (92), stroke (137) and all-cause death (120). Some had more than one event. Ratios of CER were significantly associated with MACE in unadjusted and adjusted models, see Figure 1-A. This association was independent of LDL-C, total cholesterol, Apo-B and traditional CAD risk factors. Those in the highest quintile of (24:1/24:0) CER ratio had more than two-fold risk of MACE, HR 2.32 (95% CI, 1.59-3.46). There was a dose-response association across quintiles of CER ratios and MACE, Figure 1-B.: Conclusions: Elevated CER plasma ratios are associated with MACE in patients with no CAD in the community. The risk conferred by plasma CER is independent of traditional risk factors, including lipids, and has a dose-response.

Introduction: Cardiac rehabilitation (CR) is recommended for secondary prevention following a coronary artery disease (CAD) event. The association between CR attendance and long-term major cardiovascular events (MACE)in the community has not been previously reported . Hypothesis: We tested the hypothesis that higher CR participation would be related to a lower risk of MACE,in residents of Olmsted county, Minnesota. Methods: We performed a community-based retrospective longitudinal study on patients referred to CR for CAD events or procedures between the years 2002 and 2012. Follow-up was performed using a record linkage system from the Rochester Epidemiology Project. CR participation was assessed as the number of CR sessions attended, and was analyzed as a continuous variable, as quintiles and also as a binomial variable using 12 sessions as the cutoff. The composite outcome MACE was defined as having an acute coronary syndrome (myocardial infarction (MI) or unstable angina), revascularization (CABG or PCI), ventricular arrhythmias requiring hospitalization, stroke or death from any cause. Multivariate models were adjusted for age and gender and also for factors associated with MACE in the univariate analysis. Results: Our cohort included 2273 patients, 69% males, mean age (SEM) 64 (0.26) years. After a mean follow-up of 6 (0.07) years, 827 patients had an event: MI (73), unstable angina (113), CABG(53), PCI (260), ventricular arrhythmia (13), stroke (72) and death (243). Participation in 12 or more sessions (vs <12 sessions) had a lower rate of MACE (HR 0.81, 95% CI 0.70-0.93, p=0.003, see Figure1 . After adjusting for smoking, hypertension, diabetes and history of MI the association remained significant HR 0.85, 95% CI 0.74-0.98, p=0.03. Conclusions: A higher participation in CR was associated with a lower risk of MACE. These results provide additional evidence of important CR health advantages and expand on previous evidence of a dose-response benefit of CR.

Background: Cardiac rehabilitation (CR) improves functional capacity after heart valve surgery (HVS), but no survival benefit has yet been described. We hypothesized that attendance at CR after HVS would be associated with improved long-term survival. Methods: In 2013, we surveyed all patients (or a close family member if the patient was deceased) who had isolated HVS between 2006 through 2010 at the Mayo Clinic to assess if they attended CR after their HVS. We assessed survey reliability among local patients by comparing self-reported vs. medical-record verified CR attendance with the kappa statistic. We then performed a propensity-adjusted landmark mortality analysis through October 2014 for the association between CR attendance and long-term all-cause mortality conditional upon surviving the first year after HVS. Results: Survey response rate was 40% (573/1420), with responders more likely to be older, have longer hospitalizations, more aortic valve disease, but have better survival than non-responders. Among local patients, there was “substantial” agreement between reported attendance and medical-record verified CR attendance (agreement at 84%, kappa = 0.62.) A total of 547 patients (age 70 yrs., 62% male, 59% aortic valve surgery) with valid survey responses were included in the propensity analysis, of whom 296 (54.1%) attended CR. There were 100 deaths during a median follow-up of 5.4 years. For all patients, the propensity-adjusted model suggested no impact of CR on mortality (HR 1.03, 95% CI, 0.66 to 1.62). When stratified by procedure type, results suggested a potentially favorable, but non-significant, effect among patients with mitral valve surgery (HR 0.49, 95% CI 0.15 to 1.56), but not among patients with aortic valve surgery (HR 1.00, 95% CI 0.61 to 1.64.) Conclusions: Important improvements in functional capacity from CR have been previously reported after HVS, but, contrary to our hypothesis, we found that CR does not provide long-term survival benefits after HVS. These findings need additional confirmation to better clarify any mortality impact that may exist for CR following HVS, particularly among patients undergoing mitral valve surgery.