López-Jiménez, Francisco

Objective: Cardiovascular disease remains a leading cause of mortality worldwide. Ceramide scores have been associated with adverse outcomes in patients with established coronary artery disease. The prognostic value of ceramide score has not been assessed in the general population. We tested the hypothesis that ceramide scores are associated with major adverse cardiac events (MACE) in a community-based cohort with average coronary artery disease burden at enrollment. Approach and results: 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 (bypass grafting or percutaneous intervention), stroke, or death. Ceramides were analyzed as log-transformed continuous variables, ratios or scores, and quartiles with adjustment for confounders. We analyzed 1131 subjects, 52% females, mean age±(SD) 64±9 years. After a median follow-up of 13.3 years (Q1, 12.7; Q3, 14.4), 486 patients experienced a MACE: myocardial infarction (80), coronary artery bypass surgery (34), percutaneous coronary intervention (62), stroke (94), and all-cause death (362). Ceramide ratios were significantly associated with MACE independently of LDL-c (low-density lipoprotein cholesterol) and conventional coronary artery disease risk factors. Those in the highest quartile of ceramide score had nearly 1.5-fold risk of MACE, hazard ratio, 1.47 (95% CI, 1.12–1.92). There was a dose-response association across quartiles of ceramide ratios and MACE. Conclusions: Elevated ceramide score is a robust predictor of cardiovascular disease and MACE in the community. The risk conferred by the ceramide score has a dose-response behavior and is independent of conventional risk factors.

Background An artificial intelligence algorithm that detects age using the 12‐lead ECG has been suggested to signal “physiologic age.” This study aimed to investigate the association of peripheral microvascular endothelial function (PMEF) as an index of vascular aging, with accelerated physiologic aging gauged by ECG‐derived artificial intelligence–estimated age. Methods and Results This study included 531 patients who underwent ECG and a noninvasive PMEF assessment using reactive hyperemia peripheral arterial tonometry. Abnormal PMEF was defined as reactive hyperemia peripheral arterial tonometry index ≤2.0. Accelerated or delayed physiologic aging was calculated by the Δ age (ECG‐derived artificial intelligence–estimated age minus chronological age), and the association between Δ age and PMEF as well as its impact on composite major adverse cardiovascular events were investigated. Δ age was higher in patients with abnormal PMEF than in patients with normal PMEF (2.3±7.8 versus 0.5±7.7 years; P =0.01). Reactive hyperemia peripheral arterial tonometry index was negatively associated with Δ age after adjustment for cardiovascular risk factors (standardized β coefficient, –0.08; P =0.048). The highest quartile of Δ age was associated with an increased risk of major adverse cardiovascular events compared with the first quartile of Δ age in patients with abnormal PMEF, even after adjustment for cardiovascular risk factors (hazard ratio, 4.72; 95% CI, 1.24–17.91; P =0.02). Conclusions Vascular aging detected by endothelial function is associated with accelerated physiologic aging, as assessed by the artificial intelligence–ECG Δ age. Patients with endothelial dysfunction and the highest quartile of accelerated physiologic aging have a marked increase in risk for cardiovascular events.

Abstract Aims An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care unit (CICU) patients, independent of the presence of LVSD by transthoracic echocardiography (TTE). Methods and results We included 11 266 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG after CICU admission. Left ventricular ejection fraction (LVEF) data were extracted for patients with a TTE during hospitalization. Hospital mortality was analysed using multivariable logistic regression. Mean age was 68 ± 15 years, including 37% females. Higher AI-ECG probability of LVSD remained associated with higher hospital mortality [adjusted odds ratio (OR) 1.05 per 0.1 higher, 95% confidence interval (CI) 1.02–1.08, P = 0.003] after adjustment for LVEF, which itself was inversely related with the risk of hospital mortality (adjusted OR 0.96 per 5% higher, 95% CI 0.93–0.99, P = 0.02). Patients with available LVEF data (n = 8242) were divided based on the presence of predicted (by AI-ECG) vs. observed (by TTE) LVSD (defined as LVEF ≤ 35%), using TTE as the gold standard. A stepwise increase in hospital mortality was observed for patients with a true negative, false positive, false negative, and true positive AI-ECG. Conclusion The AI-ECG prediction of LVSD is associated with hospital mortality in CICU patients, affording risk stratification in addition to that provided by echocardiographic LVEF. Our results emphasize the prognostic value of electrocardiographic patterns reflecting underlying myocardial disease that are recognized by the AI-ECG.

Abstract Background The ACC/AHA Pooled Cohort Equation (PCE) for atherosclerotic cardiovascular disease (ASCVD) has been recommended as the initial step in cardiovascular risk assessment. The sensitivity of this tool to detect those who will develop ASCVD within 10-years, while considering age and sex groups, has not been extensively studied. Methods Using the Rochester Epidemiology Project (REP) we evaluated a community-based cohort of consecutive patients that sought primary care in Olmsted County, MN, between the years 1998–2000 and were followed up through March 1st 2016. Inclusion criteria were ages 40–79 and complete data to calculate the PCE. We excluded those with known ASCVD, atrial fibrillation or heart failure. Criteria were similar to those used to derive the PCE. Events were validated in duplicate and included fatal and non-fatal myocardial infarction and ischemic stroke. Patient information was ascertained using the record linkage system of the REP. Follow-up was truncated at 10 years. We assessed the ASCVD predicted risk (categorized as low <5%, intermediate 5–9.9%, high 10–19.9%, and very high ≥20% risk) at baseline, in subjects having an ASCVD event within 10-years in the community across age (<65 years) and sex categories. We also categorized ideal cardiovascular health as ≥4 metrics [non-smoker, body mass index <25 kg/m2, and not having of elevated blood pressure (≥130/80 mmHg), LDL cholesterol (>100 mg/dL), or fasting blood glucose (>100 mg/dL), in the absence of a medical diagnosis or treatment]. Results We included 30,042 adults, mean ± SD age 48.5±12.2 years, 54% women, with a median follow-up of 16.5±5.3 years. There were 1,555 ASCVD events (5.2%) at 10 years of follow-up. The performance of the PCE was similar to what was described in the original report (0.78 vs 0.79). Overall, among those who suffered an ASCVD, 54% of women and 41% of men were not high risk as predicted by PCE (Figure 1A). Most women (73%) <65 years of age would had been considered low risk within 10-years before the event, and only 10% would have been considered to be high risks (Figure 1B). Nonetheless, women <65 years who had an ASCVD event and low 10-year predicted ASCVD risk by PCE were less likely to have ideal cardiovascular health [55 (0.40%) vs 3884 (28.39%), p-value<0.0001], when compared to women in the low risk category without an event. Conclusion The PCE fails to identify most women who will develop an ASCVD event, particularly women <65 years of age. These results underscore the importance of using additional information when estimating ASCVD risk among women and the need for better cardiovascular risk prediction tools. Figure 1 Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Mayo Clinic

Abstract Background Left ventricular systolic dysfunction (LVSD) is associated with increased morbidity and mortality. Although there are effective treatments for patients with LVSD to prevent mortality, heart failure and to improve symptoms, many patients remain undetected and untreated. We have recently derived a deep learning algorithm to detect LVSD using the electrocardiogram (ECG) which could have an important screening role, particularly in limited resources settings. We evaluated the accuracy of this algorithm for the first time in Africa in a sample of subjects attending a cardiology clinic. Methods We conducted a retrospective study in a general cardiac clinic in Uganda. Consecutive patients ≥18 years who had a digital ECG and echocardiogram done within two days of each other were included. We excluded patients with pacemakers or missing information regarding left ventricular ejection fraction (LVEF). Routine 10-second, twelve-lead surface rest ECG were performed using an Edan PC ECG Model SE-1515, Hamburg, Germany. The probability of LVSD was estimated with the Mayo Clinic artificial intelligence (AI) ECG algorithm. LVEF was calculated by the MMode (Teichholz method) using a Philips Ultrasound system, HD7XE, Bothel, Washington, USA. LVSD was defined as a LVEF≤35%. We assessed the overall diagnostic performance of the algorithm to identify LVSD in this population with the area under the receiver operating curve (AUC), and estimated sensitivity, specificity and accuracy using a pre-specified cut-off based on the probability for LVSD generated by the algorithm. We conducted secondary analyses using different LVEF cutoff values. Results We included 634 subjects, 32% (200) of whom had hypertension and 12% (77) clinical heart failure. Mean age was 57±18.8 years, 58% were women and the overall prevalence of LVSD was 4%. The AI-ECG had an AUC of 0.866 (see figure below), sensitivity 73.08%, specificity 91.10%, negative predictive value 98.75%, positive predictive value 26.03% and an accuracy of 90.96% using the original threshold. Using the optimal cutoff based on the AUCs, the sensitivity was 80.77% and specificity was 81.05% with a negative predictive value of 98.99%. The ROC for the detection of LVEF of 40% or below was 0.821. Conclusion The Mayo AI-ECG algorithm demonstrated good accuracy, sensitivity and specificity to detect LVSD in patients seen in a clinical setting in Uganda. This tool may facilitate the identification of people at a high risk for LVSD in settings with low resources. ROC Funding Acknowledgement Type of funding source: None

Abstract Background Cardiac rehabilitation (CR) is an established model of cardiovascular (CV) prevention that has proven benefits. Availability, characteristics and need of CR programs in Latin-American and Caribbean (LAC) countries remains poorly characterized. This study aims to establish the availability, capacity, density and aspects of CR delivery in LAC. Methods A cross-sectional survey was administered to CR programs in 24 LAC. Local CV organizations and societies identified CR programs. Characteristics of individual CR program were reviewed including: funding sources, core components, healthcare providers, and dose (number of sessions per weeks X total number of weeks) of CR. National CR capacity (median number of patients a program could serve per year X number of programs per country), density (Ischemic Heart Disease [IHD] incidence per year/ national capacity), need (IHD incidence per year- national capacity) and occupancy (median number patients program served per year/national capacity) were computed based on survey responses. Results At least one CR program was identified per LAC country (total 255 programs across 24 countries). Data was collected in 20 of the 24 countries. Responses were received from 139/255 programs (median program response rate=55%; Table 1). Over 50% (n=73) of programs were funded by multiple sources (government, hospital/clinic, private health insurance); Self-payment was reported by 63% programs, in which 24 (33.8%) patients paid over 50% of the cost. Guideline-indicated conditions were accepted in 77% or more programs. Physiotherapists (n=106, 76.3%), cardiologists (n=105, 75.5%) and dietitians (n=79, 56.8%) were the most common healthcare providers on CR teams. Regionally, programs offered 9 (IQR = 8–10) core components (patient education, exercise prescription and initial assessment delivered by nearly all programs). Median CR was 36 (IQR = 24–56) sessions/patient. Twenty-seven (20.9%) programs offered alternative CR models (e.g., home or community-based and hybrid models). Median national capacity was 500 CR spots/country (IQR= 200–2300). Regional density was 1 CR spot per 24 incident IHD patients per year. Greatest need in absolute terms for CR was observed in Brazil, Dominican Republic and Mexico (all with >150,000 spots needed per year to manage incident IHD patients; Table 1). Occupancy ranged from over 100% in Colombia to 15% in Chile (median=60%, IQR = 32%–81%), Table 1. Conclusion In LAC countries, there is very limited capacity to meet the need for CR. Nature of CR services varied regionally. Funding Acknowledgement Type of funding source: None

Abstract Background We have demonstrated that artificial intelligence interpretation of ECGs (AI-ECG) can estimate an individual's physiologic age and that the gap between AI-ECG and chronologic age (Age-Gap) is associated with increased mortality. We hypothesized that Age-Gap would predict long-term atherosclerotic cardiovascular disease (ASCVD) and that Age-Gap would refine the ACC/AHA Pooled Cohort Equations' (PCE) predictive abilities. Methods Using the Rochester Epidemiology Project (REP) we evaluated a community-based cohort of consecutive patients seeking primary care between 1998–2000 and followed through March 2016. Inclusion criteria were age 40–79 and complete data to calculate PCE. We excluded those with known ASCVD, AF, HF or an event within 30 days of baseline.A neural network, trained, validated, and tested in an independent cohort of ∼ 500,000 independent patients, using 10-second digital samples of raw, 12 lead ECGs. PCE was categorized as low<5%, intermediate 5–9.9%, high 10–19.9%, and very high≥20%. The primary endpoint was ASCVD and included fatal and non-fatal myocardial infarction and ischemic stroke; the secondary endpoint also included coronary revascularization [Percutaneous Coronary Intervention (PCI) or Coronary Artery Bypass Graft (CABG)], TIA and Cardiovascular mortality. Events were validated in duplicate. Follow-up was truncated at 10 years for PCE analysis. The association between Age-Gap with ASCVD and expanded ASCVD was assessed with cox proportional hazard models that adjusted for chronological age, sex and risk factors. Models were stratified by PCE risk categories to evaluate the effect of PCE predicted risk. Results We included 24,793 patients (54% women, 95% Caucasian) with mean follow up of 12.6±5.1 years. 2,366 (9.5%) developed ASCVD events and 3,401 (13.7%) the expanded ASCVD. Mean chronologic age was 53.6±11.6 years and the AI-ECG age was 54.5±10.9 years, R2=0.7865, p<0.0001. The mean Age-Gap was 0.87±7.38 years. After adjusting for age and sex, those considered older by ECG, compared to their chronologic age had a higher risk for ASCVD when compared to those with <−2 SD age gap (considered younger by ECG). (Figure 1A), with similar results when using the expanded definition of ASCVD (data not shown). Furthermore, Age-Gap enhanced predicted capabilities of the PCE among those with low 10-year predicted risk (<5%): Age and sex adjusted HR 4.73, 95% CI 1.42–15.74, p-value=0.01 and among those with high predicted risk (>20%) age and sex adjusted HR 6.90, 95% CI 1.98–24.08, p-value=0.0006, when comparing those older to younger by ECG respectively (Figure 1B). Conclusion The difference between physiologic AI-ECG age and chronologic age is associated with long-term ASCVD, and enhances current risk calculators (PCE) ability to identify high and low risk individuals. This may help identify individuals who should or should not be treated with newer, expensive risk-reducing therapies. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Mayo Clinic

Abstract Background An Artificial intelligence (AI)-enabled algorithm can provide an age estimate from a single standard resting ECG. The gap between AI-estimated age (ECG age) and chronological age (Age-Gap) has been associated with total and cardiovascular mortality. We hypothesized that coronary endothelial dysfunction (CED), an early feature of coronary atherosclerosis, is associated with physiological aging, as measured by ECG age. Purpose The aim of this study is to investigate the association of CED, a potential index of cardiac aging, and AI-estimated physiological aging. Methods 1902 patients with signs and/or symptoms of ischemia and with non-obstructive coronary artery disease (<40% angiographic stenosis) who underwent functional coronary angiography with intracoronary (IC) acetylcholine (Ach) infusions and a resting ECG within one year were included in this analysis. CED was defined as coronary artery constriction [%ΔCoronary artery diameter (CAD) <0] in response to IC Ach. Patients were categorized as CED+ or CED−. The ECG age were calculated by a previously-trained AI algorithm that was built using the Keras framework with Tensorflow backend and Python. The association between ECG-age and Age-Gap, with CED was investigated. Results Average chronological age was 50.9±12.5, ECG age was 54.1±11.6, 1261 (66%) were females. Compared to CED−, the CED+ group had more males and hyperlipidaemia (p<0.05 for both). Age-gap was significantly higher in patients with CED+ (Fig. A). Multiple regression analysis showed that CED+ was associated with Age-gap (standardized β [sβ] coefficient = 0.08, P=0.001) even after adjustment for chronological age (sβ coefficient = 0.07, P=0.001), and even after adjusting for other cardiovascular risk factors like gender and obesity (sβ coefficient = 0.07, P<0.0001). Furthermore, there is a significant gradual increase in vasoconstriction, in response to IC Ach, with increasing Age-gap (Fig. B). Conclusion Abnormal coronary endothelial function is associated with an increased AI-estimated age which could indicate a higher physiological age. Funding Acknowledgement Type of funding source: None

Abstract Background Artificial Intelligence (AI) algorithms enabled the detection of patients with paroxysmal atrial fibrillation (PAF) from a single normal sinus rhythm (NSR) ECG. Coronary microvascular dysfunction (CMD) is a precursor for coronary artery disease, which is a known risk factor for AF. Purpose The aim of this study is to examine the probability of PAF, according to AI-enabled algorithm estimation, in patients with CMD. Methods 1858 patients without persistent atrial fibrillation with signs and/or symptoms of ischemia and with non-obstructive CAD (<40% stenosis) who underwent invasive coronary microvascular functional assessment and the ECG closest to the functional assessment were included in this analysis. Patients with coronary flow velocity reserve (CFR) <2 in response to adenosine were labelled as endothelial-independent CMD; % increase in coronary blood flow (%ΔCBF) <50% in response to acetylcholine were labelled as endothelial-dependent CMD. Patients were categorized into 4 groups. G1: Normal (NL) CFR/NL %ΔCBF; G2: Abnormal (ABN) %ΔCBF only; G3: ABN CFR only; G4: ABL CFR & %ΔCBF. The probability of having PAF (%probAF) was calculated by a previously-trained and validated AI algorithm. AF Flag = %probAF >9%; which is a pre-set cut-off found to have the highest accuracy of identifying patients with PAF (Area Under the Curve = 0.87). Results Mean age for patients was 51.2±12.4 and 66.3% were females. 835 (45%) were in G1, 39 (2%) in G2, 911 (49%) in G3, and 73 (4%) in G4. Compared to G1 and G2, G3 and G4 were older, had more diabetes and higher smoking rates (p<0.05 for all). Furthermore, G4 had a significantly higher %probAF compared to other groups (Fig. 1). G4 were also more likely to be flagged by the algorithm as having PAF, even after adjusting for cardiovascular risk factors, with an odds ratio of 1.9 [CI 95% 1.1–3.3; p=0.03]) (Fig. 2). Conclusion Patients with combined CMD have a significantly higher probability of having PAF based on an AI-enabled algorithm. Further research is warranted to know if patients with CMD would benefit from formal AF screening at the time of diagnosis. Funding Acknowledgement Type of funding source: None