Citations

Background: Atrial fibrillation (AF) is a low-flow state and may underestimate aortic stenosis (AS) severity. Single-high Doppler signals (HS) consistent with severe AS (peak velocity ≥4 m/s or mean gradient ≥40 mm Hg) are averaged down in current practice. The objective for the study was to determine the significance of HS in AF low-gradient AS (LGAS). Methods: One thousand five hundred forty-one patients with aortic valve area ≤1 cm 2 and left ventricular ejection fraction ≥50% were identified and classified as high-gradient AS (HGAS) (≥40 mm Hg) and LGAS (<40 mm Hg), and AF versus sinus rhythm (SR). Available computed tomography aortic valve calcium scores (AVCS) were retrieved from the medical record. Outcomes were assessed. Results: Mean age was 76±11 years, female 47%. Mean gradient was 51±12 in SR-HGAS, 48±10 in AF-HGAS, 31±5 in SR-LGAS, and 29±7 mm Hg in AF-LGAS, all P ≤0.001 versus SR-HGAS; HS were present in 33% of AF-LGAS. AVCS were available in 34%. Compared with SR-HGAS (2409 arbitrary units; interquartile range, 1581–3462) AVCS were higher in AF-HGAS (2991 arbitrary units; IQR1978–4229, P =0.001), not different in AF-LGAS (2399 arbitrary units; IQR1817–2810, P =0.47), and lower in SR-LGAS (1593 arbitrary units; IQR945–1832, P <0.001); AVCS in AF-LGAS were higher when HS were present ( P =0.048). Compared with SR-HGAS, the age-, sex-, comorbidity index-, and time-dependent aortic valve replacement-adjusted mortality risk was higher in AF-HGAS (hazard ratio=1.82 [1.40–2.36], P <0.001) and AF-LGAS with HS (hazard ratio=1.54 [1.04–2.26], P =0.03) but not different in AF-LGAS without HS or SR-LGAS (both P =not significant). Conclusions: Severe AS was common in AF-LGAS. AVCS in AF-LGAS were not different from SR-HGAS. AVCS were higher and mortality worse in AF-LGAS when HS were present.

Background We aimed to provide personalized risk estimates for cardiac events (CEs) and life‐threatening events in women with either type 1 or type 2 long QT. Methods and Results The prognostic model was derived from the Rochester Long QT Syndrome Registry, comprising 767 women with type 1 long QT (n=404) and type 2 long QT (n=363) from age 15 through 60 years. The risk prediction model included the following variables: genotype/mutation location, QTc‐specific thresholds, history of syncope, and β‐blocker therapy. A model was developed with the end point of CEs (syncope, aborted cardiac arrest, or long QT syndrome–related sudden cardiac death), and was applied with the end point of life‐threatening events (aborted cardiac arrest, sudden cardiac death, or appropriate defibrillator shocks). External validation was performed with data from the Mayo Clinic Genetic Heart Rhythm Clinic (N=467; type 1 long QT [n=286] and type 2 long QT [n=181]). The cumulative follow‐up duration among the 767 enrolled women was 22 243 patient‐years, during which 323 patients (42%) experienced ≥1 CE. Based on genotype‐phenotype data, we identified 3 risk groups with 10‐year projected rates of CEs ranging from 15%, 29%, to 51%. The corresponding 10‐year projected rates of life‐threatening events were 2%, 5%, and 14%. C statistics for the prediction model for the 2 respective end points were 0.68 (95% CI 0.65–0.71) and 0.71 (95% CI 0.66–0.76). Corresponding C statistics for the model in the external validation Mayo Clinic cohort were 0.65 (95% CI 0.60–0.70) and 0.77 (95% CI 0.70–0.84). Conclusions This is the first risk prediction model that provides absolute risk estimates for CEs and life‐threatening events in women with type 1 or type 2 long QT based on personalized genotype‐phenotype data. The projected risk estimates can be used to guide female‐specific management in long QT syndrome.

Introduction: Clinical predictive value of transient prolonged ventricular conduction and repolarization during right ventricular (RV) pacing have not been studied. Hypothesis: Ventricular conduction and repolarization prolongation during RV pacing predict risk of arrhythmic and mortality events. Methods: Electrophysiology studies in 501 patients with cardiomyopathy (CM) were reviewed. Duration changes of QRS and QT interval during pacing from the RV apex were measured. Subjects were followed up for a mean of 3.2 years for arrhythmic events and total mortality. Results: Participants had a mean age 65 yrs, 80% male, 74% funcional class II or III, and a mean EF of 33.1%. A paced QRS duration > 190 ms was associated with more severe CM and in multi-variable analysis it was associated with a 3.5 times higher risk of ventricular arrhythmias or appropriate defibrillator therapy (HR 3.5, 95% CI 2.3-5.4, p<0.01) and 2.6 times higher risk of total mortality or arrhythmic events (HR 2.6, 95% CI 2.3-3.4, p<0.01) compared to a paced QRS duration <190 ms, independent of baseline EF or baseline QRS duration, and after adjustment for electrophysiology study results, baseline functional class, type of cardiomyopathy and other relevant clinical factors. Similarly a QTc interval during RV pacing > 620 ms was associated with a 2.7 times higher risk of arrhythmic events compared to a QTc interval during RV pacing < 620 ms. Conclusions: Prolongation of ventricular conduction (QRS > 190 ms) and repolarization (QTc > 620 ms) observed during RV pacing in subjects with CM are associated with higher risk of arrhythmic events, defibrillator therapies and all-cause mortality.

Background: Contemporary breast cancer radiotherapy (RT) results in variable cardiac radiation exposure. While cardiomyocytes are radio-resistant, radiation induces coronary microvascular endothelial damage and inflammation which may ultimately lead to myocardial inflammation, ischemia and fibrosis and put patients at risk for heart failure (HF) and particularly, HF with preserved ejection fraction (HFpEF). Methods: Community based (Olmsted County, MN), HF case-control study of contemporary (1999-2014) breast cancer RT patients with CT-based RT planning for precise mean cardiac radiation dose (MCRD) calculation. HF cases (n=66) and controls (n=129) were matched (1:2) for age at RT, HF risk factors, tumor side and chemotherapy use. Matched controls and cases had identical follow-up (index interval). Proportion of HF and clinical characteristics in categories of MCRD were reported (table) and conditional logistic regression was used to estimate the HF odds ratio (OR) associated with MCRD as a continuous variable. Results: Of the 66 HF cases, 46 (70%) had HFpEF and 20 (30%) had HF with reduced ejection fraction (HFrEF). The proportion of patients with HF (any) or HFpEF increased and proportion with HFrEF tended to increase with increasing MCRD. The prevalence of HF risk factors (hypertension, diabetes, coronary disease) at time of RT did not vary by MCRD while left sided tumor, cancer stage and adriamycin use increased with increasing MCRD. The OR per 1 unit increase in log MCRD was 3.71 (1.98, 6.94) for total HF (p<0.001), 4.91 (1.93, 9.09) for HFpEF (p<0.001) and 2.78 (0.91, 8.47) for HFrEF (p=0.07). The mean index interval was 5.0±3.2 years. Conclusion: Cardiac radiation incident to contemporary breast RT increases the risk of HF, and particularly HFpEF over a short post-RT interval. These data may guide use of proton beam therapy in breast cancer. These data also provide support for the role of coronary microvascular compromise in the pathophysiology of HFpEF.

Introduction: Stress echocardiography (SE) has an established role in the evaluation of symptomatic patients, but its utility in asymptomatic patients is unclear. Current guidelines discourage the use of SE in most asymptomatic patients, citing lack of data to support its use. We therefore sought to describe the yield of SE in a group of asymptomatic individuals according to risk profile. Hypothesis: We hypothesized that SE would rarely be abnormal in asymptomatic individuals. Methods: We conducted a retrospective cohort study of all asymptomatic individuals who underwent exercise or pharmacologic SE between 2003 and 2013. Reasons for SE included screening for CAD, preoperative evaluation, abnormal ECG, coronary calcification, and assessment of established CAD (defined as prior myocardial infarction or coronary revascularization). We grouped subjects using both the 10-year Framingham risk equation and ATP3-suggested categories of low-risk (<10%), intermediate-risk (10-20%), and high-risk (>20%) for non-diabetic subjects without known CAD and by ACC/AHA Pooled Cohort risk estimates for subjects with and without diabetes. Subjects with established CAD were considered separately. Abnormal echocardiograms were defined as having ischemic, fixed, or mixed defects. Results: The study population comprised 19,170 subjects; 63% men, mean±SD age was 64±13 years, and 52% underwent pharmacologic SE. The likelihood of abnormal SE results was proportional to risk, with the highest yield in those with established CAD (59%). Those with diabetes had a yield similar to those with a 10 year risk of 10-20% by both ATP3/FRS and ACC/AHA. Conclusions: SE abnormalities are commonly seen in asymptomatic individuals; the likelihood of abnormal SE results was proportional to patients’ estimated risk. The frequency of SE abnormalities in diabetic patients is similar to that of intermediate-risk, but not high-risk patients.

Introduction: In asymptomatic patients with severe aortic stenosis (SAS), left ventricular (LV) ejection fraction (EF) <50% is generally considered to be the threshold for referral for aortic valve replacement (AVR). Hypothesis: We investigated the importance of LVEF on long-term outcome after AVR in symptomatic and asymptomatic SAS patients and studied whether LVEF < 50% is the optimal threshold for referral for AVR. Methods and Results: We retrospectively identified 2017 patients with SAS (aortic valve area (AVA)<1cm2, mean aortic valve gradient ≥40 mm Hg, or indexed AVA <0.6 cm2/m2) who underwent surgical AVR from January 1995 to June 2009 at our institution. Patients were divided into 4 groups depending on preoperative LVEF (<50% in 300 (15%) patients, 50-59% in 331 (17%), 60-69% in 908 (45%), and ≥70% in 478 (24%)). The primary end-point was all-cause mortality. During follow-up of 5.3±4.4 years, 1056 (52%) died. Five-year mortality rate increased with decreasing LVEF (41% (n=106), LVEF<50%); 35% (n=98), LVEF 50-59%; 26% (n=192), LVEF 60-69%; 22% (n=90), LVEF≥70%, p<0.0001). Compared to patients with LVEF≥60%, patients with LVEF 50-59% had increased mortality (HR 1.58, p<0.001), with a similar risk increase in both symptomatic (HR=1.56, p<0.001) and asymptomatic patients (HR 1.58, p=0.006, Figure). In a Cox regression analysis corrected for standard risk factors, LV mass index, AVA, and stroke volume index, LVEF was predictive of all-cause mortality (HR=0.89 per 10%, p<0.001). When this multivariable analysis was repeated in the subset of 1333 patients with no history of coronary artery disease, LVEF was still associated with all-cause mortality (HR=0.90 per 10%, p=0.009). Conclusion: In patients with SAS undergoing AVR, patients with LVEF 50-59% have also increased mortality compared to patients with LVEF>60%, suggesting that a different LVEF threshold should be used when referring for AVR.

Introduction: Plasma aldosterone (Aldo) increases Na and water reabsorption and mediates inflammation. Our recent findings in a general population cohort showed Aldo strongly associated with hypertension (HTN), obesity, chronic kidney disease (CKD) and lower natriuretic peptide levels (NPs) during a first visit (V1), even when Aldo was within the normal range. It is undefined whether Aldo itself (V1) and/or its increase after 4-year (second visit, V2) can predict new onset cardiovascular and metabolic diseases. Hypotheses: We hypothesized that: 1) Aldo-V1 predicts new onset disease at V2; 2) The increase of Aldo between V1 and V2 predicts new onset disease-V2; and 3) Aldo-V2 remains associated with cardiovascular and metabolic disease-V2. Methods: We defined the correlations between Aldo-V1 and Aldo-V2 with cardiometabolic diseases and medications at V2, in a general community cohort (N=1368) from Olmsted County, MN. Associations were adjusted for age, sex and BMI; Aldo was analyzed as continuous variable and according to tertiles. Results: After 4 years, Aldo-V1 continuous variable predicted new onset of central obesity (p=0.0113, OR=1.36, CI=1.07-1.73), HTN (p=0.0012, OR=1.36, CI=1.13-1.63) and use of antilipemic drugs (p=0.0119, OR=1.25, CI=1.05-1.48); while Aldo-V1 in the 3 rd tertile predicted new onset of type 2 diabetes (T2DM) (p=0.0392, OR=1.96, CI=1.03-3.70), use of antilipemic therapy (p=0.0162, OR=1.59, CI=1.09-2.31) and HTN (p=0.049, OR1.44, CI=1.00-2.08) at V2. Secondly, we observed that the increase in Aldo between V1 and V2 predicted new HTN-V2. Finally, we found higher Aldo-V2, but still within the normal range and Aldo-V2 remained associated with HTN, obesity and CKD, when analyzed as continuous variable and when in the 3 rd tertile. Conclusions: In the general community, Aldo-V1 predicts new onset HTN, central obesity, T2DM and use of antilipemic therapy. Importantly, the increase of aldosterone between V1 and V2 also predicts new onset HTN. Finally, Aldo-V2 correlates with HTN, obesity and CKD as we found at V1. Further studies are warranted to evaluate aldosterone as therapeutic target to delay disease progression, and to support its role as prognostic biomarker to identify high-risk subjects.

Introduction: Idiopathic Dilated (DCM) and hypertrophic cardiomyopathies (HCM) are leading causes of sudden cardiac death. The incidence and prevalence of both DCM and HCM were originally estimated in Olmsted County, MN over 1975-1984. Incidence was 6.0 and prevalence was 36.5 per 100,000 person-years, for DCM; with incidence and prevalence rates of 2.5 and 19.7 per 100,000 person-years for HCM. This study aims to determine the incidence and mortality rates of individuals diagnosed with HCM and DCM in Olmsted County, MN from 1984 to 2016. Methods: The Rochester Epidemiology Project is a validated long term medical record linkage system that collects and links medical information for the approximately 145000 residents of Olmsted County, Minnesota. Using this database and the Electronic Health Record systems at Mayo Clinic and Olmsted Medical Center in Rochester, MN, we manually reviewed and validated the medical records of 4440 patients. This cohort includes all patients with any mention of a cardiomyopathy based on ICD and HICDA codes during the period 1984 to 2016. Data were summarized with mean and standard deviation, frequency and percentage and compared between groups using two sample t-test for continuous data and chi-square for proportions. Survival was computed using Kaplan-Meier methods and compared to expected survival based on age and gender. Incidence was computed using Olmsted County population numbers during the time period and adjusted to the US 1980 white population to allow for comparison with the previous study. Results: The calculated crude incidence rate for DCM and HCM are 10.1 and 7.8 per 100,000 respectively. Incidence rates adjusted for comparison to US 1980 white population (age and sex adjusted) are 10.4 (95% CI: 9.3-11.4) and 8.0 (95% CI: 7.1-8.8) per 100,000 person-years, respectively. The observed number of deaths in this cohort (HCM and DCM) was 420 vs 194 expected. Conclusions: Calculated incidence, both crude and adjusted rates are higher than previously estimated rates. The crude incidence rate for HCM is almost 4-fold higher than that for DCM is 2.4 times higher than the original estimate by Codd et al. Individuals with cardiomyopathies were found to have a higher overall mortality rate than the general population with an observed to expected hazard ratio of 1.73 (95% CI 1.49-2.01, p<0.001) for DCM and 1.44 (95% CI 1.21-1.71, p<0.001) for HCM. Our findings suggest that previous studies may have underestimated the true burden of HCM and DCM in the population which has important implications for healthcare provision and cost.

Abstract Background: Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. Methods: We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values<5x10-8 as genome-wide significant, and p-values<1x10-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants.Results: None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values<1x105. The strongest evidence was found for rs4674019 (p-value=2.27x10-7), which showed genome-wide significant interaction (p-value=3.8x10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. Conclusions: In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT–breast cancer risk association.

Abstract Accurate estimation of mammographic breast density could aid in augmenting breast cancer risk assessment for women undergoing breast screening with full-field digital mammography (FFDM). Breast density can be estimated from FFDM and is most commonly assessed in the clinic by visual grading into one of the four categories defined by the American College of Radiology BI-RADS. However, BI-RADS density assessment is highly subjective and does not provide a quantitative, continuous measure of percent density (PD), which would allow for more refined risk stratification and assessment of density changes. Here, we introduce Deep-LIBRA, an artificial intelligence (AI) tool for fully-automated assessment of breast PD from FFDM images. Two key modules form the core of Deep-LIBRA: 1) an implementation of a modified U-Net architecture for breast segmentation and 2) a radiomic machine learning module that performs PD estimation within the segmented breast region. To develop and validate Deep-LIBRA, raw (i.e., "For Processing") FFDM images (Selenia Dimensions, Hologic Inc.) acquired at two breast cancer screening practices were retrospectively analyzed. For the breast segmentation module, we used a total of 12,100 FFDM studies from 2,200 individual women and a 90%-10% split-sample training-validation approach, using the Dice coefficient to evaluate the accuracy of Deep-LIBRA versus ground-truth manual breast segmentation. For the PD estimation module we used a total of 3,304 FFDM images from 1,652 individual women; manual PD scores obtained with the widely used Cumulus software were used as the "gold standard" in a three-fold cross-validation setting to assess the accuracy of Deep-LIBRA in PD estimation. PD estimates from Deep-LIBRA were also compared with breast density estimates from the commercially available Volpara software. Breast segmentation had a Dice coefficient of 95.31% when compared to ground-truth manual breast segmentation in the validation set. Deep-LIBRA average differences from ground-truth PD scores in the three cross-validation folds were 4.91%, 4.65%, and 4.22%, while Volpara had corresponding average differences of 6.20%, 6.01%, and 5.94%. Deep-LIBRA PD scores were also significantly different from Volpara PD (t-test p-value &lt; 0.001 in all three folds). Preliminary evaluation results show that Deep-LIBRA is a promising AI approach for accurately assessing PD from FFDM images. Citation Format: Omid Haji Maghsoudi, Scott Christopher, Aimilia Gastounioti, Lauren Pantalone, Fang-Fang Wu, Eric A. Cohen, Winham Stacey, Emily F. Conant, Celine Vachon, Despina Kontos. Deep-LIBRA: An artificial intelligence approach for fully-automated assessment of breast density in digital mammography [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2600.