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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

Background: Myocardial injury results in release of cardiac troponin (cTn) into the bloodstream, readily detected by high-sensitivity cTn (hs-cTn) assays. A noninvasive, rapid, home-based test to exclude acute myocardial injury at the time of the test and for the next 7 hours would help manage patients with suspected myocardial infarction. Since myocyte injury is associated with ECG changes, we hypothesized an artificial intelligence ECG (AI-ECG) could identify absence of injury. Objective: To train and test an AI-ECG convolutional neural network (CNN) to identify patients using a single ECG who are suspected of myocardial infarction who are low risk, with hs-cTn levels below the 99 th percentile at test time and for the subsequent 7 hours. Methods: A CNN tuned to identify the absence of a hs-cTnT (5 th Gen cTnT Roche Diagnostics) >15ng/L for men and >10ng/L for women was developed. All ECGs were recorded within one hour of the hs-cTnT assay. We used 73,012 ECGs and hs-cTnT pairs from 47,542 unique patients to train the network, 9031 ECGs from 5,811 patients for internal validation to optimize hyperparameters, and 11,904 ECGs with 21,191 hs-cTnT measurements up to 7 hours after the ECG, from 11,904 different patients as a holdout test set. Results: The mean age was 63.9±17.5 years, and 30,348 of the 59,446 patients (51%) were male. 5,852 patients (49.1%) had no elevation of hs-cTnT and 6,052 (50.9%) had an hs-cTnT above the 99 th percentile at baseline or within 7 hours of the test. Of the 11,904 patients in the test set, using a sensitive threshold, the 12 lead AI ECG identified 1037 patients (8.7%) likely to have a low risk for subsequent hs-cTnT increases > 99 th % (AUC 0.86), and the single lead ECG identified 685 patients. Of the 1037 low risk pts, 59 had an hs-cTnT > 99 th percentile within 7 hours. The mean maximum hs-cTnT among the 59 low-risk patients was 53 ng/L ± 92 vs 184 ng/L ± 1474 in the others. None of these low risk patients died within 14 days of the test. Conclusion: Use of the AI ECG to identify patients without cardiac injury at the time of the test and for 7 subsequent hours is feasible. This may permit rapid, point of care, home-based, bloodless, painless testing to exclude cardiac injury, facilitating care and management of patients with suspected ischemia.

Background: High-sensitivity cardiac troponin (hs-cTn) assays quantify cTn in patients at very low concentrations. Myocyte injury due to ischemia or other pathologies cause blood levels to increase, which is prognostic. A noninvasive, rapid, broadly available, home-based test to detect hs-cTn increases would facilitate risk-stratification. Since myocyte injury is associated with ECG changes, we hypothesized an artificial intelligence ECG (AI-ECG) could non-invasively predict current or impending hs-cTnT elevations. Objective: To develop an AI-ECG convolutional neural network (CNN) to detect an abnormal hs-cTnT (5 th Gen cTnT, Roche Diagnostics) concentration using a 12-lead ECG, and a single lead ECG (lead I), which would enable smartphone, home-based detection. Methods: We developed single lead and 12-lead ECG CNNs to detect a) hs-cTnT concentrations that were at or above the 6ng/L limit that can be reported b) above the 99 th percentile upper limits of >15ng/L for men and >10ng/L for women. All ECGs were recorded within one hour of the hs-cTnT measurements. We used 73,012 ECG and hs-cTnT pairs from 47,542 unique patients to train the network, 9031 ECGs from 5,811 patients for internal validation to optimize hyperparameters, and 18,276 ECG and hs-cTnT pairs from 11,904 different patients as a holdout test set to determine the area under the receiver-operator curve (AUC). Results: The mean age was 63.9±17.5 years, and 30,348 of the 59,446 patients (51%) were male. Of the 91,288 hs-cTnT pairs 73,271 (80.2%) were above 6ng/L and 50,799 (55.6%) are above the 99 th percentile. In the test set, the AUC for the detection of a hs-cTnT level higher than 6ng/L was 0.88 using the 12 lead ECG and 0.834 with the single lead. For the detection of hs-cTnT level above of 99 th percentile, the 12 lead ECG AUC was 0.853 and the single lead was 0.806. Conclusion: The AI-ECG permits detection of levels of hs-cTnT consistent with myocardial injury. This may allow a home-based, non-invasive test that would be massively scalable and could further enhance rapid-risk stratification and patient triage with potentially significant cost reductions and enable novel triage strategies at sites without hs-cTn assays.

Introduction: It has been estimated that up to a billion people worldwide could be exposed to Zika re-emergence including North America. Zika was considered the worst outbreak of the century before COVID-19. There are only few reports of cardiac complications and the long-term risk of heart failure (HF) is unknown. This is the objective of this study. Methods: We included 19 patients who developed acute myocarditis within one week of symptomatic Zika, in a prospective observational multi-center study. Patients underwent clinical, laboratory evaluation including Zika confirmation, ECG, echocardiogram, Holter, cardiac MRI, coronary arteriography (2). Patient follow-up examinations were performed at 2, 6,12, 24, 36 and 48 months. Results: Of the 19 patients, 12 (63%) were female with a mean age of 49 ± 17 years with a median follow-up of 3 years. Nine patients (47%) developed acute HF; 8 (42%) with reduced ejection fraction (HFrEF) and one with preserved EF (HFrEF) and moderate to severe pericardial effusion. Two cases died suddenly in the first week of Zika onset, both with HFrEF. Treatment included sacubitril /valsartan, or ace-inhibitors, SGLT2 inhibitors, beta-blockers, aldosterone-blockers, cardiovascular rehabilitation, and in one case cardiac resynchronization therapy plus implanted defibrillator. There was no new HF onset after the acute phase of the disease. Myocarditis resolved in 10 (56%) patients. Persistent atrial and ventricular arrhythmia with normal EF observed in 5 (26%) of cases. Altogether, characteristic features of dilated cardiomyopathy that developed in 7 patients (36, 8%); 2 (11%) of them died. Conclusion: Almost half of the patients with Zika myocarditis developed acute HF during the first week of disease onset, none of the patients developed HF after the acute phase. A 3-year mortality rate of 10,5 % was observed.

Existing data have described benefits and drawbacks of alcohol consumption on cardiovascular diseases (CVD), but no research has evaluated its association with the cardiovascular health (CVH) score proposed by the American Heart Association. Here, we conducted a cross-sectional analysis on the Kardiovize cohort (Brno, Czech Republic), to investigate the relationship between alcohol consumption and CVH. We included 1773 subjects (aged 25–64 years; 44.2% men) with no history of CVD. We compared CVD risk factors, CVH metrics (i.e., BMI, healthy diet, physical activity level, smoking status, blood pressure, fasting glucose, and total cholesterol) and CVH score between and within several drinking categories. We found that the relationship between drinking habits and CVH was related to the amount of alcohol consumed, drinking patterns, and beverage choices. Heavy drinkers were more likely to smoke tobacco, and to report diastolic blood pressure, fasting glucose, triglycerides, and low-density lipoprotein (LDL)-cholesterol at higher level than non-drinkers. Among drinkers, however, people who exclusively drank wine exhibited better CVH than those who exclusively drank beer. Although our findings supported the hypothesis that drinking alcohol was related to the CVH in general, further prospective research is needed to understand whether the assessment of CVH should incorporate information on alcohol consumption.

Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83–0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76–0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.

Abstract Background : The term arterial stiffness (ArSt) describes structural changes in arterial wall related to the loss of elasticity and is known as an independent predictor of cardiovascular diseases (CVD). The evidence relating to ArSt and triglycerides (TG) shows contradictory results. This paper means to survey the association between high TG and ArSt, utilizing the cardio-ankle vascular index (CAVI). Methods : Subjects aged between 25 and 64 years from a random population-based sample were evaluated between 2013-2016. Data from questionnaires, blood pressure, anthropometric measures, and blood samples were collected and analyzed. CAVI was measured using VaSera VS-1500N devise. Subjects with a history of CVD or chronic renal disease were excluded. Results : 1934 participants, 44.7 % of males, were included. The median age was 48 (Interquartile Range [IQR] 19) years, TG levels were 1.05 (0.793) mmol/L, and CAVI 7.24 (1.43) points. Prevalence of high CAVI was 10.0 % (14.5 % in males and 6.4 % in females; P < 0.001) and prevalence of hypertriglyceridemia was 20.2 % (29.2 % in males and 13 % in females; P <0.001). Correlation between TG and CAVI was 0.136 ( P <0.001). High CAVI values were more prevalent among participants with metabolic syndrome (MetS), high blood pressure, dysglycemia, abdominal obesity, high LDL-cholesterol, and high total cholesterol. Using binary regression analysis, high TG were associated with high CAVI, even after adjustment for other MetS components, age, gender, smoking status, LDL-c, and statin treatment (β=0.474, OR= 1.607, 95 % CI = 1.063-2.429, P =0.024). Conclusion : TG levels were correlated with ArSt, measured as CAVI. High TG was associated with high CAVI independent of multiple cardiometabolic risk factors. Awareness of the risks and targeted treatment of hypertriglyceridemia could further benefit in reducing the prevalence of CVD and events.

Abstract Introduction Sleep disordered breathing (SDB) is associated with adverse cardiovascular outcomes and decreased cardiorespiratory fitness (CRF). The risk of long-term major adverse cardiovascular events (MACE) when SDB and decreased CRF co-occur has not been determined. Methods We included consecutive patients that underwent a symptom-limited cardiopulmonary exercise test followed by first-time diagnostic polysomnography within 6 months. Patients were stratified based on the presence of moderate-severe SDB (apnea/hypopnea index ≥15/hour) and decreased CRF defined as &lt;70% predicted peak oxygen consumption (VO2). MACE was a composite outcome of myocardial infarction (MI), coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI), stroke/transient ischemic attack (TIA) and death. Cox-proportional hazard models adjusting for factors known to influence CRF and MACE were constructed. Results Of 498 included patients (60±13 years, 28.1% female), 175 (35%) had MACE (MI=17, PCI=14, CABG=13, stroke=20, TIA=12, deaths=99) at a median follow-up of 8.7 years (interquartile range=6.5-10.3 years). After adjusting for age, sex, beta-blockers, systemic hypertension, diabetes mellitus, coronary artery disease, cardiac arrhythmia, chronic obstructive pulmonary disease, smoking and positive airway pressure (PAP) usage, decreased CRF alone (HR=1.91, 95%CI=1.15-3.18, p=0.012), but not SDB alone (HR=1.26, 95%CI=0.75-2.13, p=0.389) was associated with increased risk of MACE. Those with SDB and decreased CRF had increased risk of MACE compared to patients with decreased CRF alone (HR=1.85, 95%CI=1.21-2.84, p&lt;0.005) after accounting for these confounders; the risk was attenuated after additionally adjusting for adequate adherence to PAP (HR=1.85, 95%CI=0.99-3.05, p=0.05). Conclusion The incidence of MACE, including mortality, was high in this sample. Moderate-severe SDB with concurrent decreased CRF was associated with higher risk of MACE than decreased CRF alone. These results highlight the importance of including CRF in the risk assessment of patients with SDB, and conversely, that of screening for SDB in patients with low peak VO2. Support None.

e17535 Background: Androgen Deprivation Therapy (ADT) is commonly used to treat prostate cancer (PC), but has been associated with cardiac morbidity and mortality. The exact mechanisms of this association are unclear. We sought to use an artificial intelligence (AI) enabled algorithm to identify ECG changes in PC patients who received ADT compared to PC patients who did not receive ADT. Methods: From 1,000,000 ECGs performed on 210,414 patients between 1993 and 2017 at our institution, a convolutional neural network was developed to detect predictive signatures for cardiac pathologies. During this process, the ability to predict “estimated sex” of the patient was developed, with output values ranging from 0 (female) to 1 (male). We applied this algorithm to 8,619 ECGs performed on 1,057 men age 75 or younger treated with radiation for high-risk or recurrent PC at our institution, and compared estimated sex after receiving ADT (n = 1,065) to ADT-naive ECGs (n = 7,554). We correlated ECG-identified estimated sex with serum testosterone levels using Spearman rank correlation. Results: Patients who had received ADT had a mean (SD) estimated sex value of 0.81 (0.26) compared to 0.92 (0.17) for those who did not (p < 0.001). Difference between estimated sex in post-ADT ECGs and ADT-naive ECGs remained significant across age groups (Table). Decreased serum total testosterone correlated with decreased estimated sex values in men receiving ADT (R = .57, p < 0.001). Conclusions: ADT for prostate cancer is associated with changes in AI-identified ECG parameters, including lower estimated male sex after receiving ADT. Lower ECG male sex estimate was associated with decreased serum testosterone. In this study, we provide preliminary proof of concept for a potential non-invasive means of monitoring treatment effect and physiologic change using ECGs. [Table: see text]