Gersh, Bernard J.

A 34-year-old man was referred for progressive angina and exertional dyspnoea refractory to medical therapy, with a presumptive diagnosis of hypertrophic cardiomyopathy (HCM). Transthoracic echocardiography (TTE) revealed asymmetric septal hypertrophy without systolic anterior motion of the mitral valve leaflet and with no dynamic left ventricular outflow tract (LVOT) obstruction. However, the LVOT velocity was elevated at rest as well as with provocation, without the characteristic late peaking obstruction seen in HCM. Focused TTE to evaluate for suspected fixed obstruction demonstrated a subaortic membrane 2.2 cm below the aortic valve. Coronary CT angiography confirmed the presence of the subaortic membrane and was negative for concomitant coronary artery disease. Surgical resection of the subaortic membrane and septal myectomy resulted in significant symptomatic relief and lower LVOT velocities on postoperative TTE. This case reminds the clinician to carefully evaluate for alternative causes of LVOT obstruction, especially subaortic membrane, as a cause of symptoms mimicking HCM.

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: Atrial fibrillation (AF) is a serious complication, which occurs in 30% to 40% of patients after surgical aortic valve replacement (SAVR) and contributes to adverse outcomes. Advanced technology has enabled transcatheter aortic valve replacement (TAVR) in patients with High-risk Severe Aortic Stenosis (HRSAS). In these patients, the compliance of the systemic vasculature is an independent predictor of diastolic dysfunction, a known predictor of postoperative AF (POAF). We hypothesize that patients with HRSAS undergoing TAVR represent a population at increased risk for POAF. METHODS: The medical records of 195 TAVR patients at the Mayo Clinic from November 2008 to January 2013 were analyzed. All patient demographic, clinical, pre- and post-procedural outcomes were collected from the patients’ medical records, and reviewed to ascertain all cases of POAF (AF ≤30 days of surgery). Patients with a prior history of AF (n= 75 (38.5%)) were excluded. Logistic regression analysis was used to identify clinical predictors of POAF after TAVR. Results: The mean age of the study population was 80.8±7.8 years and 60.8% (n=73) were men. The mean STS risk score was 8.9±5.1. Of the 120 patients without prior AF, 20.8% (n=25) developed new-onset POAF after TAVR. Patients who developed POAF were more likely to have undergone a transapical or transaortic TAVR approach (OR 3.20; 95% CI (1.3-8.0), had a higher incidence of renal complications (52.0% vs 21.5%; p= 0.003), greater hospital length of stay (7.4±2.58 vs 5.0±2.6 days; p= 0.0003, and a trend toward higher 30-day mortality (28.0% vs 13.7%; p= 0.09) and pulmonary complications (16.0% vs 5.4%; p= 0.08) compared with those who did not develop POAF, respectively. Neurologic complications were not different between the 2 group (4.0% vs 3.2%; p= 0.85). Conclusions: Patients with High-risk Severe Aortic Stenosis undergoing TAVR have increased risk of POAF, primarily associated with a transapical or aortic surgical approach. Patients who develop POAF had higher incidence of post-procedural complications and longer hospitalizations. Therefore, perioperative strategies aimed at prevention of POAF are as important in TAVR patients as in those undergoing SAVR.

Tomas Konecny, 2012 Finalist and Presenting Author

Introduction Current evidence suggests that abnormal ventriculo-vascular coupling may contribute to the development of atrial fibrillation (AF) by increasing ventricular filling pressures and consequently, left atrial pressure. Recent investigations have also suggested that inflammation might have a role in the pathophysiology of AF. We hypothesize that autoimmune vasculitis plays a significant role in the pathogenetic mechanism of AF. Methods In this longitudinal cohort study, residents of Olmsted County, MN with first- time diagnosed AF during 1980-2000 and a reference age- and sex-matched control group from the same underlying population and time period were identified and followed until death, or 2000. The relationship between autoimmune vasculitis and first documented AF was analyzed. Results Of a total of 8766 patients (mean age 61.1+14.9 years; 56% women), 691 (7.9%) were diagnosed with autoimmune vasculitis during the 20 year period. The prevalence of autoimmune vasculitis in the AF group was 9.9% and 5.9% in the control group; p <0.001, respectively. After adjusting for CAD and LV dysfunction, the odds of autoimmune vasculitis in AF was 1.7 times higher than in controls (OR 1.69, 95%; CI 1.42 - 2.01, p < 0.0001). AF patients with autoimmune vasculitis had a worse survival compared to controls; p=.0114 (log-rank test). Conclusion Autoimmune vasculitis strongly increases the risk of atrial fibrillation and portends shorter survival in the AF population. These observations may represent one mechanism linking increased arterial stiffness and inflammation in the pathogenesis and prognosis of AF as an age-related disorder.

Abstract Aims Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P &lt; 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.

Introduction: Hospitalization for COVID-19 is often associated with myocardial injury (elevated cardiac troponin [cTn]). In the initial pandemic waves, patients with myocardial injury had increased likelihood of cardiovascular complications and inpatient mortality; however, the differences in impact of distinct subtypes of myocardial injury on mortality is unclear. In addition, little is known about how these outcomes compare with the subsequent, milder, Omicron wave. Methods: Utilizing Mayo Clinic enterprise patients we developed algorithms based on diagnostic/procedural codes, cTn levels, and temporal relationships to positive COVID-19 PCR tests, to determine 5 common subtypes of myocardial injury: stress cardiomyopathy, myocarditis, pericarditis, pulmonary embolism, and myocardial infarction. We examined hospitalized adults with COVID-19; those with elevated cTn designated as cases and others as controls. We fitted Cox models using the following covariates: spline transformed age, sex, race, ethnicity, index date and clinical comorbidities. Patients were analyzed based on admission dates coinciding with either the Alpha-Delta or Omicron waves. Results: Across both wave cohorts, hospitalized patients with acute COVID-19 complicated by myocardial injury had an increased risk of mortality compared to those without ( Figure ). Patients with one of the 5 subtypes examined tended to have higher risk than those in the “other myocardial injury” category. Admission during the Omicron wave, compared to the Alpha-Delta waves, tended to have higher mortality risk in all subtypes except myocarditis. Conclusions: Our algorithms successfully identified subgroups of myocardial injury in patients hospitalized with acute COVID-19 leading to stratification of mortality risk. Surprisingly, among hospitalized patients during the Omicron wave, myocardial injury of most subtypes was tended to have increased risk of mortality compared to the Alpha-Delta waves.

Introduction: Recently, an artificial intelligence (AI) algorithm was validated for identifying ECG signatures of atrial fibrillation (AF) risk during normal sinus rhythm. Traditionally, socioeconomically disadvantaged populations are less likely to receive novel or guideline-recommended treatments, and have high chronic disease burden, including cardiovascular diseases. There have also been concerns about new technologies creating a digital divide, worsening existing health disparities. Hypothesis: An AI algorithm will detect AF risk unequally across patients with different levels of social determinants of health (SDOH). Methods: The initial prospective, non-randomized, interventional study leveraged EHR and digital technologies to prospectively enroll 1004 patients with a broad range of AI-ECG risks of AF, who received continuous cardiac rhythm monitoring for up to 30 days. The Area Deprivation Index (ADI) was used as a composite measure of SDOH which is comprised of indicators spanning four domains: income, education, employment, and house quality. 904 intervention patients linked to the ADI data were matched to 904 real-world control patients derived from eligible but unenrolled patients. Those with ADI under 33% were defined as having better SODH; and patients with ADI above 33% were defined as having worse SODH. Results: Median age was 74 years and 38% were women. After matching, the intervention and control arms were balanced in all baseline characteristics, including HF, HTN, diabetes, stroke, CAD and ADI. A hazard ratio of 3.11 (P<0.001) was computed when comparing intervention to control for primary AF diagnosis outcome. In patients with worse SDOH, the intervention increased AF detection from 2.6% to 8.8% (HR 3.63 [2.09, 6.32]); in the better SDOH, the intervention increased AF detection from 3.9% to 9.3% (2.41 [1.23, 4.74]). Conclusions: An AI-guided AF screening program increased the detection of AF in both patients with better or worse SDOH. The current study validates the use of AI in historically disadvantaged populations displaying the possibility for broad implementation with decreased possibility of perpetuating disparities.

Introduction: Previous studies have shown inconsistent changes in the rates of adverse cardiovascular events after daylight savings time (DST) transitions. In this large-scale, U.S.-based nationwide study, we sought to investigate the association of DST transitions with the rates of adverse cardiovascular events. Methods: The study cohort included 36,116,951 unique individuals from de-identified administrative claims data of the OptumLabs Data Warehouse®. There were 74,722 total adverse cardiovascular events during the week of DST transition and the control weeks (two weeks prior and after) in spring and autumn of 2015-2019. We used Bayesian hierarchical Poisson regression models to estimate the event rate ratios (ERR) representing the ratio of composite adverse cardiovascular event rates (acute myocardial infarction, stroke, cardiogenic shock, cardiac arrest, and sudden death) between DST transition weeks and control weeks. Results: Our results showed an average increase of 3% [95% uncertainty interval; -3% to 10%] and 4% [-2% to 12%] in the rate of adverse cardiovascular events during Monday and Friday of the spring DST transition week, respectively. The probability of spring DST transition being associated with a moderate-to-large increase in the event rates (ERR>1.10) was estimated to be <6% for Monday and Friday, and <1% for the remaining days. During autumn DST transition, the probability of any decrease in adverse cardiovascular event rates was estimated to be <46% and a moderate-to-large decrease in the event rates to be <4% across all days of the week. Conclusions: Spring DST transition week had a suggestive association with a minor increase in adverse cardiovascular event rates on Monday and Friday. The probability of this association to be of clinically meaningful magnitude was estimated to be very low. We did not observe any evidence suggestive of a change in adverse cardiovascular event rates immediately following autumn DST transition. Taken together, our findings, based on a large, diverse cohort from across the United States, suggest that DST transitions are unlikely to significantly impact the rate of cardiovascular events.

Background Guidelines for patients with atrial fibrillation (AF) at high thromboembolic risk recommend oral anticoagulants (OACs) for preventing stroke and systemic embolism (SE). The reasons for guideline non-adherence are still unclear. Aim The aim is to identify clinical, demographic and non-patient characteristics associated with withholding OAC in patients with AF at high stroke risk. Methods Patients in the Global Anticoagulant Registry in the FIELD-AF, newly diagnosed with AF between March 2010 and August 2016, and with CHA2DS2-VASc Score≥2 (excluding sex), were grouped by OAC treatment at enrolment. Factors associated with OAC non-use were analysed by multivariable logistic regression. Results Of 40 416 eligible patients, 12 126 (30.0%) did not receive OACs at baseline. Globally, OAC prescription increased over time, from 60.4% in 2010–2011 to 74.7% in 2015–2016. Country of enrolment was the major predictor for OAC withholding (χ2−df=2576). Clinical predictors of OAC non-use included type of AF (χ2−df=404), history of bleeding (χ2−df=263) and vascular disease (χ2−df=99). OACs were used most frequently around the age of 75 years and decreasingly with younger as well as older age beyond 75 years (χ2−df=148). Non-cardiologists (χ2−df=201) and emergency room physicians (χ2−df=14) were less likely to prescribe OACs. OAC prescription correlated positively with country health expenditure. Conclusions Approximately one out of three AF patients did not receive OAC, while eligible according to the guidelines. Country of enrolment was the major determinant of anticoagulation strategy, while higher country health expenditure was associated with lower likelihood of withholding anticoagulation.