Citations

Introduction: Calcium scoring computed tomography (CAC) is a widely available and inexpensive modality of screening for atherosclerosis. This study aims to improve the predictive value of CAC by incorporating pericardial fat characteristics in a CatBoost model. Methods: A sample of 7,000 non-contrast clinically indicated CAC scans were randomly selected from a cohort of consecutive Olmsted County MN residents. Known history of atherosclerotic cardiovascular disease or impaired images were excluded. A pericardial fat segmentation model, trained on 191 images of the same dataset with a dice similarity score of 94%, was applied to 6,194 CT scans; Volume and mean Hounsfield unit (mHU) of the output masks were added to age at imaging, radiologist-measured calcium score, male gender, diabetes, and smoking (Figure 1). A 70,20 and 10% split was used to produce the training, validation and test set, respectively. Features were inputted to CatBoost classifier, as the best model tested, to predict four outcomes: acute coronary syndrome (ACS), heart failure (HF), ischemic stroke, and all-cause mortality. Model was evaluated by area under the ROC curve (AUC) on the unseen test set. Results: Mean age was 55.2 (8.8), 55.2 (8.8) and 54.8 (8.5) years in the training, validation and test sets, respectively, and 65%, 67% and 66% of the populations were male. The mean AUC, obtained through bootstrapping with 100 iterations, was 0.81 [0.77-0.85], 0.86 [0.60-0.92], 0.80 [0.73-0.87] and 0.87 [0.81-0.91] for HF, ACS, stroke and death, respectively; The same analysis excluding pericardial fat measurements showed a 3% reduction in the AUC for all of the investigated outcomes (Figure 2). Conclusion: Our study presents a robust risk prediction model for ACS, HF, stroke, and mortality based on CAC scans. The improved performance of the model by incorporating pericardial fat characteristics, highlights the potentials of deep learning in timely inexpensive risk prediction in large populations.

Introduction: Sleep duration is now recognized as an important determinant of cardiovascular (CV) health. Recently, the CV implications of day-to-day variability in sleep duration have become apparent, with irregular sleep duration associated with increased risk of CV disease, including hypertension. However, the relationship between sleep duration variability and ambulatory blood pressure (BP) patterns in the general population or in patients with CV disease is unknown. Research Questions: We sought to investigate whether irregular sleep duration is associated with aberrant ambulatory BP or heart rate (HR) profiles in patients undergoing cardiac rehabilitation (CR). Methods: We recruited a sample of 42 patients with coronary artery disease enrolled in CR at Mayo Clinic Rochester. Participants underwent 24-hour ambulatory BP monitoring and sleep assessment including one week of wrist actigraphy and sleep diary prior to entering CR. Twenty-four hour, awake, and asleep averages were calculated for systolic BP, diastolic BP, and HR. Nocturnal dipping and morning surge were also computed for BP and HR. Sleep regularity was quantified by SD of actigraphy-derived sleep duration and irregular sleep duration was defined as SD >1 hour. Chi-square tests and Mann-Whitney U tests were used to compare groups. Results: Thirty-six participants (22% females, median age [25 th ,75 th IQR] 66.5 [61,75.8] years, body mass index 31.7 [26.9,35.1] kg/m 2 ) had valid actigraphy and ambulatory BP recordings. We found sleep duration SD >1 hour in 36% of them. Demographics and average sleep duration were similar between those with irregular and regular sleep duration (all Ps>0.11). Twenty-four-hour, awake and asleep HR values were comparable between groups (all Ps>0.26). Participants with irregular sleep duration exhibited lower nocturnal HR dipping (9.2 [3.1,11.6] % vs 12.9 [8.9,18.2] %, P=0.019) than their counterparts with regular sleep. Morning HR surge was also attenuated in patients with irregular sleep duration (3 [-1,9] bpm vs 10 [7,14] bpm, P=0.006). Ambulatory SBP and DBP measures did not differ significantly between groups (all Ps>0.17). Conclusions: High day-to-day variability in sleep duration is linked to blunted nocturnal dipping and morning surge in HR in patients with coronary artery disease enrolled in CR. Because altered ambulatory HR profile predicts unfavorable CV outcomes, evaluation of sleep patterns in the CR setting may yield important prognostic information.

Background: AI-ECG is a cost-effective tool for left ventricular dysfunction (LVD) screening. However, its cost-effectiveness for other forms of structural heart disease (SHD) is unknown. While AI-ECG is inexpensive, a drawback is low positive predictive value (PPV), which leads to high costs from unnecessary follow-up tests. Therefore, strategies to improve the yield of AI-ECG-based screening are needed. Aim: To evaluate the cost savings of a stepwise approach to SHD screening with AI-ECG followed by POCUS compared to AI-ECG alone. Methods: 286 adult outpatients undergoing AI-ECG were selected at random. Participants received same-day POCUS and had a recent TTE (our gold standard for SHD). We evaluated four SHDs: aortic stenosis (AS), cardiac amyloidosis (CA), HCM, and LVD. The costs of AI-ECG ($75) and TTE ($1,305) were obtained from Healthcare Bluebook. The cost of POCUS ($100) was estimated independently. Cost savings were analyzed for simultaneous screening for all forms of SHD and screening for individual SHDs. Results: AI-ECG identified potential SHD in 125 patients, but only 39 were true positives by TTE (31% PPV). In AI-ECG positive patients, POCUS demonstrated findings of SHD in 52/125. Compared to TTE, this stepwise approach yielded 32 true positives and 20 false positives (62% PPV). The cost per patient diagnosed with SHD was $4,733 with AI-ECG alone but decreased to $3,182 with stepwise screening (33% cost savings). Screening for individual SHDs resulted in cost reduction from $18,724 to $6,315 (66% savings) for AS, $21,023 to $12,230 (42% savings) for CA, $9,883 to $6,175 (38% savings) for HCM, and $4,019 to $3,582 (11% savings) for LVD. Conclusions: Stepwise screening for SHD with AI-ECG followed by POCUS significantly reduces costs compared to AI-ECG alone. We also suggest a model for parallel screening for multiple SHDs, which is likely more cost-effective than screening for individual SHDs.

Background: Left ventricular diastolic dysfunction (LVDD) predicts mortality in cardiac intensive care unit (CICU) patients. A novel artificial intelligence enhanced electrocardiogram (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in the cardiac intensive care unit (CICU). We aim to assess if LVDD by AI-ECG is associated with in-hospital and one-year mortality in CICU patients. Methods: In this retrospective cohort study, we included consecutive unique adults admitted to the Mayo Clinic CICU from 2007 to 2018 with an admission AIECG, which AI assigned LVDD grade (0 to 3). Medial mitral E/e’ ratio >15 on transthoracic echocardiogram (TTE) defined elevated filling pressures. AIECG and TTE assessment of LVDD were used to assign patients to four groups based on TTE as gold standard: true and false negative, and true and false positive. In-hospital mortality was evaluated using multivariable logistic regression. One-year survival was evaluated using Kaplan-Meier curves and multivariable Cox proportional-hazards. Results: We included 11,868 patients (median age 69.5 years, 37.7% females); 48% had heart failure and 44% had acute coronary syndromes. AIECG LVDD grades were: grade 0 (normal), 33%; grade 1, 7%; grade 2, 39%; grade 3, 21%. In-hospital (adjusted OR) and one-year (adjusted HR) mortality increased in each higher AIECG LVDD grade (Figure A/B), before and after adjustment including TTE measurements. Patients with grade 2-3 LVDD by AIECG and medial mitral E/e’ ratio >15 by TTE (true positive) had the highest in-hospital (adjusted OR 2.5 [1.7-3.9]) and one-year (adjusted HR 1.9 [1.5-2.5]) mortality (Figure C/D), and mortality was elevated similarly in patients with either grade 2-3 LVDD by AIECG (false positive) or medial mitral E/e’ ratio >15 by TTE (false negative). Conclusions: The AIECG LVDD grade was strongly associated with in-hospital and one-year mortality in CICU patients, even after adjusting for clinical variables and TTE measurements. Patients with concordant AIECG and TTE for elevated filling pressures (true positive) were at highest risk.

Background A subset of individuals with major depressive disorder (MDD) have a high burden of cardiovascular risk factors and cerebral small‐vessel disease, implicating vascular disease in the development of depression. Cross‐sectional studies demonstrate a link between endothelial dysfunction and MDD, but the prospective association between peripheral endothelial dysfunction (PED) and an incident diagnosis of MDD is unknown. Methods and Results Patients undergoing a baseline assessment of cardiovascular risk were evaluated for PED using reactive hyperemia–peripheral arterial tonometry (≤1.8 consistent with PED). Patient medical records were reviewed to identify those who underwent a formal clinical evaluation of MDD after the index PED evaluation. The frequency of PED was compared in those with and without MDD. Logistic regression analyses were performed to assess the association between baseline PED and incident MDD. Between January 2006 and December 2020, 1614 patients underwent testing for PED. Four hundred eighty‐four (30.1%) patients underwent a formal evaluation for MDD after (0–15 years) the index procedure (mean±SD age, 52.8±13.8 years; 65.2% women). Of these, 157 (32.4%) had PED and 108 (31.0%) were diagnosed with MDD. Individuals with MDD had a higher frequency of PED (40.2% versus 30.2%; P =0.034) compared with those without MDD. In multivariable analyses, PED was significantly associated with MDD (odds ratio, 2.3 [95% CI, 1.4–3.8]; P <0.001). Conclusions PED is significantly associated with incident MDD. Thus, PED may be a useful marker to identify individuals at increased risk of depression who may benefit from more frequent and earlier management strategies.

Background Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in adults using a 12‐lead ECG; however, its efficacy in children is uncertain. We aimed to develop novel artificial intelligence–enabled ECG algorithms for LVSD and RVSD detection in pediatric patients. Methods and Results We identified 10 142 unique pediatric patients (age≤18) with a 10‐second, 12‐lead surface ECG within 14 days of a transthoracic echocardiogram, performed between 2002 and 2022. LVSD was defined quantitatively by left ventricular ejection fraction (LVEF). RVSD was defined semiquantitatively. Novel pediatric models for LVEF ≤35% and LVEF <50% achieved excellent test areas under the curve of 0.93 (95% CI, 0.89–0.98) and 0.88 (95% CI, 0.83–0.94) respectively. The model to detect LVEF <50% had a sensitivity of 0.85, specificity of 0.80, positive predictive value of 0.095, and negative predictive value of 0.995. In comparison, the previously validated adult data‐derived model for LVEF <35% achieved an area under the curve of 0.87 (95% CI, 0.84–0.90) for LVEF ≤35% in children. A novel pediatric model for any RVSD detection reached a test area under the curve of 0.90 (0.87–0.94). Conclusions An artificial intelligence–enabled ECG demonstrates accurate detection of both LVSD and RVSD in pediatric patients. While adult‐trained models offer good performance, improvements are seen when training pediatric‐specific models.

Abstract Background Heart failure (HF) and atrial fibrillation (AF) are intertwined, sharing common risk factors and influencing each other in a reciprocal manner. While left ventricular diastolic dysfunction (LVDD) stands as a hallmark of HF's hemodynamic profile, its direct correlation with heightened AF risk remains uncertain. Our recent validation of an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm for LVDD detection has opened new avenues. Yet, whether AI-ECG-detected LVDD correlates with long-term AF risk remains unexplored. Methods We performed a retrospective study among all patients with a comprehensive LVDD assessment from the test population of the AI-ECG LVDD study between September 2001 and June 2022. Patients with prior documented AF according to health records or previous ECG were excluded. Patients were classified by AI-ECG as normal diastolic function (DF), grade 1 (G1), grade 2 (G2), or grade 3 (G3) LVDD. We assessed the risk of AF across AI-ECG LVDD grade, with normal DF as reference, with competing risk models. Results Of 97,023 patients free of AF at baseline, 63,533 (65.4%), 10,776 (11.1%), 18,940 (19.5%), and 3,774 (3.9%) were classified as normal DF, G1-LVDD, G2-LVDD, and G3-LVDD, respectively. Worse AI-ECG LVDD was associated with older age, lower left ventricular ejection fraction (LVEF), and higher rates of HF, diabetes, hypertension, chronic pulmonary disease, renal failure, and cardiovascular disease (p-of-trend&lt;0.001 for all). Over a median follow-up of 4.8 years (interquartile range 1.8-9.2 years), new-onset AF occurred in 6,795 (7.0%) patients. In multivariable survival analysis, adjusted to multiple risk factors including LVEF and clinical HF, G2 and G3 LVDD, representing patients with high filling pressures, were independently associated with a higher AF-risk [G2-LVDD: adjusted hazard ratio (aHR) 1.64 (95% confidence interval (95%CI) 1.52-1.73), G3-LVDD: aHR 2.24 (95%CI 2.02-2.48)]. Conclusion The AI-ECG LVDD algorithm independently predicts long-term AF risk, regardless of LVEF and clinical HF status, highlighting its potential for identifying high-risk populations and guiding proactive monitoring and management.

Abstract Background Cardiovascular disease remains the leading cause of death and burden globally, warranting the need to explore novel predictors of major adverse cardiovascular events (MACE) using new technologies. Artificial Intelligence (AI)-enhanced ECG (AI-ECG) algorithms can provide information on cardiovascular health of individuals, independent of conventional risk factors. Purpose The current study aimed to understand whether a previously described AI-ECG sex estimation algorithm could be applied for risk stratification of major adverse cardiovascular events (MACE), with a focus on a population without previous history of MACE. Methods Patients having 12-lead ECGs between 2018-2019 were included and followed up until August 2023. Exclusion criterion was a prior history of MACE. AI-ECG sex estimation was determined using a previously described algorithm predicting sex based on 12-lead ECGs. The algorithm generates outputs ranging from 0 to 1, representing the estimated probability of being male. Outputs above 0.5 were considered as AI-ECG-estimated male, and below as AI-ECG-estimated female. This was compared with biological sex to determine whether the two matched. Inconsistency between AI-ECG estimated sex and biological sex was defined as discordance. MACE was defined as myocardial infarction, coronary revascularization, non-fatal stroke, and all-cause death. Results Among 80,578 subjects (mean age 59.1 ± 16.1 years, 52.4% female), 9.7% exhibited sex discordance in AI-ECG estimation. Discordance was associated with a 56% higher risk of MACE (HR, 1.56, 95% CI 1.46-1.65, p &lt;0.001), and remained significant in multivariable analysis with traditional risk factors (HR 1.35, 95% CI 1.27-1.43, p &lt;0.001) (Figure 1). Sex-specific analysis showed increased MACE risk in both discordant males and females (Figure 2). Conclusion In patients with no history of MACE, discordance in AI-ECG sex estimation is associated with increased risk of MACE. These findings suggest the potential of AI-ECG sex estimation algorithm for risk assessment, especially in patients with no history of MACE.Figure 1.Cox proportional hazard modelFigure 2.Kaplan-Meier of MACE by sex

Abstract Background The concept of ideal cardiovascular health (CVH) was developed by the American Heart Association (AHA) as a composite of seven health factors and behaviors labeled as the Life’s Simple 7 (LS7). Ideal CVH has been associated with complex measures of cardiovascular aging. We hypothesized that physiologic aging, as determined by a simple, routinely used tool such as the electrocardiogram (ECG) enabled by an artificial intelligence algorithm to predict an individual’s physiologic age (ECG age), will be associated with poor CVH as defined by an adapted version of the AHA LS7. Methods We conducted an observational retrospective study of consecutive patients aged ≥18 years who sought care between June 2019 and March 2022, self-reported behavioral history through a validated intake questionnaire in clinical outpatient visits, had a clinically indicated ECG within one year, and provided research authorization. We applied a previously developed AI-ECG convolutional neural network to derive the ECG-Age. We excluded patients with missing data, those with body mass index (BMI) &lt;18.5 kg/m2, previous history of left-bundle branch block, atherosclerotic cardiovascular disease events, atrial fibrillation, heart failure, and non-skin cancer. An adapted version of the LS7 was used to define CVH, which includes anthropometrics (e.g., BMI), behavioral history (e.g., smoking, physical activity, and diet), vital signs (e.g., blood pressure), and laboratory values (e.g., total cholesterol, and fasting glucose). CVH was stratified as ideal, intermediate, and poor if the LS7 score had values of 9-14, 7-8, and 0-6, respectively, constructed by adding 2,1 or 0 points to each metric that met each category criteria, respectively. Accelerated aging was defined as having a Delta-Age ≥1SD above the mean, whereas Delta-age was defined as ECG minus chronological age. Logistic regression models tested the association between CVH categories and positive Delta-Age while adjusting for chronological age and sex. Results We included 114,214 individuals (53.3% females, 86.9% Caucasian). Mean chronological age was 57.7±14 years and mean AI-ECG- Age was 58 ± 12.44 years (R2 0.80, p-value &lt;0.001). Mean Delta-Age was 0.28 ±8.8 years. LS7 average score was 8.3 ± 2.23; 46.8% had ideal, 31.5% intermediate, and 21.7% had poor CVH (Figure A). Compared to those with ideal CVH, those with poor CVH were more than 50% more likely to have Delta-Age ≥1SD (OR 1.61, CI 95% 1.54-1.69, p-value: &lt;0.001), independent of chronological age and sex. (Figure B). Conclusion Poor CVH, defined by the AHA LS7, is linked with biological aging as determined by AI-enabled ECG, highlighting the importance of promoting CVH in adults as a strategy to decelerate biological aging and reduce its associated mortality.