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Background A high prevalence of preclinical heart failure (HF) (Stages A and B) has previously been shown. The aim of this study was to explore factors associated with the incidence of preclinical HF in a community population. Methods and Results Retrospective review of 393 healthy community individuals aged ≥45 years from the Olmsted County Heart Function Study that returned for 2 visits, 4 years apart. At visit 2, individuals that remained normal were compared with those that developed preclinical HF. By the second visit, 191 (49%) developed preclinical HF (12.1 cases per 100 person‐years of follow‐up); 65 (34%) Stage A and 126 (66%) Stage B. Those that developed preclinical HF (n=191) were older ( P =0.004), had a higher body mass index ( P <0.001), and increased left ventricular mass index ( P =0.006). When evaluated separately, increased body mass index was seen with development of Stage A ( P <0.001) or Stage B ( P =0.009). Echocardiographic markers of diastolic function were statistically different in those that developed Stage A [higher E/e’ ( P <0.001), lower e’ ( P <0.001)] and Stage B [higher left atrial volume index ( P <0.001), higher E/e’ ( P <0.001), lower e’ ( P <0.001)]. NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) was higher at visit 2 in those that developed Stage A or B ( P <0.001 for both). Hypertension (57%), obesity (34%), and hyperlipidemia (25%) were common in the development of Stage A. Of patients who developed Stage B, 71% (n=84) had moderate or severe diastolic dysfunction. Conclusions There is a high incidence of preclinical HF in a community population. Development of Stage A was driven by hypertension and obesity, while preclinical diastolic dysfunction was seen commonly in those that developed Stage B.

Background Many patients with symptomatic severe aortic stenosis do not undergo aortic valve replacement (AVR) despite clinical guidelines. This study analyzed the association of managing provider type with cardiac specialist follow‐up, AVR, and mortality for patients with newly diagnosed severe aortic stenosis (sAS). Methods and Results We identified adults with newly diagnosed sAS per echocardiography performed between January 2017 and March 2019 using Optum electronic health record data. We then selected from those meeting all eligibility criteria patients managed by a primary care provider (n=1707 [25%]) or cardiac specialist (n=5039 [75%]). We evaluated the association of managing provider type with cardiac specialist follow‐up, AVR, and mortality, as well as the independent association of cardiac specialist follow‐up and AVR with mortality, within 1 year of echocardiography detecting sAS. A subgroup analysis was limited to patients with symptomatic sAS. Patient characteristics and comorbidities at baseline were used for covariate‐adjusted cause‐specific and multivariable Cox proportional hazard models assessing group differences in outcomes by managing provider type. An adjusted Cox proportional hazard model with additional time‐dependent covariates for follow‐up and AVR was used to assess these practices' association with mortality. Within 1 year of echocardiography detecting sAS, data revealed that primary care provider management was associated with lower rates of cardiac specialist follow‐up (hazard ratio [HR], 0.47 [95% CI, 0.43–0.50], P <0.0001) and AVR (HR, 0.58 [95% CI, 0.53–0.64], P <0.0001) and with higher 1‐year mortality (HR, 1.45 [95% CI, 1.26–1.66], P <0.0001). Cardiac specialist follow‐up and AVR were independently associated with lower mortality (follow‐up: HR, 0.55 [95% CI, 0.48–0.63], P <0.0001; AVR: HR, 0.70 [95% CI, 0.60–0.83], P <0.0001). Results were similar for patients with symptomatic sAS. All analyses were adjusted for baseline patient characteristics and comorbidities. Conclusions For patients newly diagnosed with sAS, we observed differences in rates of cardiac specialist follow‐up and AVR and risk of mortality between primary care provider‐ versus cardiologist‐managed patients with sAS. In addition, a lower likelihood of receiving follow‐up and AVR was independently associated with higher mortality.

Abstract Introduction: Incorporation of mammographic density to breast cancer risk models could improve risk stratification to tailor screening and prevention strategies according to risk. However, robust validation of such models in prospective cohort studies is needed to determine their accuracy in identifying women at different risk levels. Methods: We incorporated Breast Imaging and Reporting Data System (BI-RADS) breast density to a literature-based model with questionnaire-based risk factors and a 313-variant polygenic risk score (PRS). The Individualized Coherent Absolute Risk Estimator (iCARE) tool was used to build and validate a 5-year absolute risk model for breast cancer. The model was evaluated for calibration and discrimination in three prospective cohorts of women of European ancestry (1,468 cases, 19,104 controls): US-based Nurses’ Health Study (NHS I and II) and Mayo Mammography Health Study (MMHS); and Sweden-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Analyses were done separately for women younger (NHS II, KARMA) and older than 50 years (NHS I, MMHS, KARMA). Improvements in risk stratification were assessed among US non-Hispanic White women aged 50-70 years. Results: For women younger than 50 years, the model with questionnaire-based risk factors, PRS and BI-RADS was well calibrated across risk deciles in NHS II, but overestimated risk at the highest risk decile in KARMA. For women 50 years or older, the model showed good calibration in all studies, with evidence of slight overestimation at the highest risk decile and underestimation at the lowest risk decile. The model with PRS and BI-RADS was well calibrated for women at high-risk in both age groups. Incorporation of BI-RADS to questionnaire-based risk factors and PRS improved risk discrimination: area under the curves (AUC) 67.0% (95% CI: 63.5-70.6%) vs. 65.6% (95% CI: 61.9-69.3%) for models with and without BI-RADS for younger women and 66.1% (95% CI 64.4-67.8%) vs. 65.5% (95% CI: 63.8-67.2%) for older women. The model with BI-RADS identified 18.4% population of non-Hispanic US women 50-70 years old above 3% 5-year risk (used for recommending risk-reducing medication in the US), with 42.4% of future cases expected to occur in this group. Addition of BI-RADS led to the reclassification of ~8.0% of US non-Hispanic White women aged 50-70 years at the high-risk threshold (3% 5-year risk), resulting in identification of 4.2% of additional future cases. Conclusion: Integrating BI-RADS with questionnaire-based risk factors and PRS resulted in improved risk stratification among women of European ancestry, with evidence of slight overestimation of risk for women at elevated risk. Additional validation of the integrated model in diverse populations is needed prior to considering clinical applications. Citation Format: Charlotta V. Mulder, Yon Ho Jee, Xin Yang, Christopher G. Scott, Chi Gao, Amber N. Hurson, Mikael Eriksson, Per Hall, Peter Kraft, Celine M. Vachon, Antonis C. Antoniou, Gretchen Gierach, Montserrat Garcia-Closas, Parichoy Pal Choudhury. Validation of an integrated breast cancer risk model with mammographic density in three prospective cohort studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2256.

Background: Patients with peripheral artery disease (PAD) are at increased risk for major adverse limb and cardiac events including mortality. Developing screening tools capable of accurate PAD identification is a necessary first step for strategies of adverse outcome prevention. This study aimed to determine whether machine analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with PAD. Methods: Consecutive patients (4/8/2015 – 12/31/2020) undergoing rest and postexercise ankle–brachial index (ABI) testing were included. Patients were randomly allocated to training, validation, and testing subsets (70%/15%/15%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict normal (> 0.9) or PAD (⩽ 0.9) using rest and postexercise ABI. A separate dataset of 151 patients who underwent testing during a period after the model had been created and validated (1/1/2021 – 3/31/2021) was used for secondary validation. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate test performance. Results: Among 11,748 total patients, 3432 patients met study criteria: 1941 with PAD (mean age 69 ± 12 years) and 1491 without PAD (64 ± 14 years). The predictive model with highest performance identified PAD with an AUC 0.94 (CI = 0.92–0.96), sensitivity 0.83, specificity 0.88, accuracy 0.85, and positive predictive value (PPV) 0.90. Results were similar for the validation dataset: AUC 0.94 (CI = 0.91–0.98), sensitivity 0.91, specificity 0.85, accuracy 0.89, and PPV 0.89 (postexercise ABI comparison). Conclusion: An artificial intelligence-enabled analysis of a resting Doppler arterial waveform permits identification of PAD at a clinically relevant performance level.

Abstract According to the 2020 report of the American Heart Association’s Heart & Stroke Statistics report, nearly 1,000 people are dying daily because of sudden out-of-hospital cardiac arrests and unfortunately, their survival rate is as low as 10%. Hypertrophic Cardiomyopathy (HCM), a relatively rare genetic heart disease is one of these diseases but finding the right patient for the implantation of ICD is still a research question. Implantation of cardioverter-defibrillator (ICD) can save the life of some of these patients. Due to the complexity of the identification of HCM patients, financial burdens, and the clinical risks involved in the ICD implantation procedure, HCM patients will go into a monitoring state before reaching the implantation trigger. Our study cohort shows about 82% of HCM deaths, did not have an ICD, which highlights the need to improve the pre-screening algorithms. In the current paper, we have proposed a new deep learning-based unsupervised clustering technique to facilitate the prioritization of patients to undergo ICD device implantation. This model uses over 900 echocardiographic measurements to find patients who benefit more from the ICD implantation procedure. Our model was trained and tested over 6 years of echo reports collected at Mayo Clinic. This model can be used as a decision support assistant for cardiologists in finding the right HCM patient when decision-making is hard.

Introduction: Transthyretin cardiac amyloidosis (TTR-CA) can cause heart failure with preserved EF (HFpEF). We previously reported a clinical/echo-based score (TCAS) and defined a cut-off that predicted risk of TTR-CA in HFpEF. Herein, we assess the comparative performance of an artificial-intelligence ECG-based screening tool (AI-ECG). Hypothesis: AI-ECG successfully identifies TTR-CA in HFpEF cohorts across a range of TTR-CA prevalence Methods: Referral HFpEF cohort was constructed from a referral population of HFpEF patients (EF ≥ 40%) undergoing 99m Tc-pyrophosphate scintigraphy (PYP) at Mayo Clinic (2013-2020, n=484, 38% with TTR-CA). Community HFpEF cohort was prospectively screened with PYP (n=275; age ≥ 60, LV wall thickness (WT) ≥ 12 mm, 4% with TTR-CA). TCAS was built using a multivariable logistic regression model and converted to a simple point score comprised of age (60-69, 70-79, ≥ 80: +2, +3, +4 points respectively), male sex (+2), relative WT > 0.57 (+2), posterior WT ≥ 12 mm (+1), EF < 60% (+1), and hypertension diagnosis (-1). TCAS was validated in the community cohort. A deep neural network trained to predict cardiac amyloidosis from a single 12-lead ECG, was applied to both referral and community cohorts. Patients were excluded if ECG was not available ≤ 12 months before PYP or if used in development of the AI-ECG. Standard ROC methods were applied. Youden’s index was used to define optimal cut-off. Results: Predictive characteristics of AI-ECG were strong and compared favorably with TCAS across a range of true TTR-CA prevalence (Table). Conclusions: Using a single ECG, AI-ECG effectively predicts TTR-CA with an excellent negative predictive value to rule out TTR-CA and a positive predictive value sufficient to warrant specific TTR-CA testing. This was particularly evident in the community. AI-ECG holds promise for simple, cost-effective global screening of at-risk populations, especially those with limited access to advanced cardiac imaging.

Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene–environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this. We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P &lt; 0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test. After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE = 4.44 × 10−6). In this transcriptome-informed genome-wide gene–environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk. Our study suggests a limited role of gene–environment interactions in breast cancer risk.

A significant proportion of patients diagnosed with symptomatic severe aortic stenosis (ssAS) do not receive treatment with aortic valve replacement (AVR) despite current ACC/AHA guidelines. Delayed care leads to increased hospitalizations, worsening heart failure and death. Timely referral to a cardiac specialist is a critical step in the treatment pathway. We sought to identify the impact of managing provider type on follow-up care for newly diagnosed patients with severe aortic stenosis (sAS). We analyzed Optum® electronic medical records to identify adults with a new outpatient sAS echo diagnosis in the pre-COVID era (1/2017 - 3/2019). We identified integrated delivery network patients alive 1d post echo excluding those with missing data or AVR within 3d of echo, then selected those managed by a primary care provider (PCP) or a cardiologist (n=5,683). Baseline characteristics were used for covariate-adjusted Cumulative Incidence Function and Fine-Gray subdistribution hazard models. We estimated the impact of managing provider type on the specialist follow-up visit rate (cardiologist, interventional cardiologist and/or cardiothoracic surgeon) for all sAS patients and of surgical or transcatheter AVR for ssAS patients within 1 year of diagnosis. Within 90d of sAS diagnosis, 47% of PCP- vs. 73% of cardiologist-managed patients had a follow-up visit (1-year rates: PCP, 69% vs. cardiologist, 90%; Hazard ratio [HR]=0.54, 95% confidence interval [CI]: 0.49-0.59, p<0.0001). Within 1 year of diagnosis, 36% of PCP- vs. 51% of cardiologist-managed ssAS patients underwent AVR (HR=0.72, CI: 0.63-0.82, p<0.0001) (Figure). Analysis suggests lower referral rates for PCP- than cardiologist-managed patients. In ssAS, 1-year AVR rates were low, particularly for PCPs. These data underscore the importance of timely follow up of all severe AS patients for symptom assessment and appropriate treatment, presenting an opportunity for initiatives to drive behavior change.

Introduction: The cardiac hormone atrial natriuretic peptide (ANP) plays a key role in blood pressure (BP) and metabolic homeostasis through guanylyl cyclase receptor-A. While the role of ANP in cardiovascular disease (CVD) has been extensively investigated, its role in metabolic regulation has only recently emerged. A genetic variant of the ANP gene (rs5068), which increases production of ANP and NT-proANP, is associated with lower blood pressure (BP), reduced risk for hypertension and a decreased prevalence of obesity and metabolic syndrome. Hypothesis: We hypothesized that sex differences in ANP may have an important role in cardiometabolic disease together with cardiovascular (CV) morbidity and mortality. Methods: We studied 1176 subjects (age > 45 years) in the general population from Olmsted County MN. Plasma ANP molecular forms were quantified. Metabolic and cardiorenal parameters, and detailed medical records were reviewed. The cardiometabolic protective action of the ANP gene variant rs5068 was also assessed. Subjects were followed for all-cause mortality and morbidity for 12 years. Results: Plasma ANP molecular forms were significantly lower in males compared to females. Males had higher BMI and greater incidence of myocardial infarction (MI), coronary artery disease and heart failure (HF). Males also had lower EF and HDL cholesterol and higher LA volume and LV mass, serum glucose, insulin and diastolic pressure. The presence of the rs5068 minor allele improved the cardiometabolic phenotype including BP and widespread metabolic parameters in males but not females. After adjustment by age, males had increased risk of all-cause mortality, HF, MI, and stroke. Conclusions: Males are characterized by a relative ANP deficiency compared to females and widespread prevalence of cardiometabolic disease and increased CV morbidity and mortality in the general population. We demonstrate that the cardiometabolic protective action of the ANP gene variant rs5068 minor allele is sex dependent and protective in males. These findings have diagnostic and therapeutic implications, particularly in males.

Background: Transcatheter aortic valve replacement (TAVR) is often complicated by AV block requiring pacing, but whether patients recover AV conduction and whether the need for pacing affects outcomes is unknown. Methods: We retrospectively studied 534 patients who underwent TAVR at Mayo Clinic in Rochester, Minnesota between November 2008 and January 2015. Of these, 115 patients had implantable cardiac devices. Data was collected from subsequent device follow-ups to determine pacemaker dependency and percentage pacing. Results: 44 patients (8.2%) had devices implanted in the immediate post procedural period. The most common indications for implantation were high grade AV block (18 patients) and complete heart block (14 patients). 17/44 (38.6%) patients were pacemaker-dependent at implant and only 4 of these patients were no longer pacemaker-dependent during subsequent followup. The indication for pacing among these four patients was high grade atrio-ventricular block (2 patients), asystole (1 patient) and complete heart block (1). Among non-pacemaker-dependent patients, no significant change in the ventricular pacing percentages was noted over time. Survival was similar between patients who required device implantation post TAVR and those who did not after adjustment for age and gender (HR 0.73 [0.39-1.36], p=ns; figure) Conclusion: Patients requiring pacing after TAVR are often pacemaker-dependent at the time of implant. It is uncommon for patients who are dependent at the time of implant to regain AV conduction, but the need for cardiac pacing is not associated with increased mortality.