Scott, Christopher

Abstract Background and Aims Incidence and types of secondary tricuspid regurgitation (TR) are not well defined in atrial fibrillation (AFib) and sinus rhythm (SR). Atrial secondary TR (A-STR) is associated with pre-existing AFib; however, close to 50% of patients with A-STR do not have AFib. The aim of this study was to assess incidence, types, and outcomes of ≥ moderate TR in AFib vs. SR. Methods Adults with and without new-onset AFib without structural heart disease or ≥ moderate TR at baseline were followed for the development of ≥ moderate TR. Tricuspid regurgitation types were pacemaker, left-sided valve disease, left ventricular (LV) dysfunction, pulmonary hypertension (PH), isolated ventricular, and A-STR. Results Among 1359 patients with AFib and 20 438 in SR, 109 and 378 patients developed ≥ moderate TR, respectively. The individual types of TR occurred more frequently in AFib related to the higher pacemaker implantation rates (1.12 vs. 0.19 per 100 person-years, P < .001), larger right atrial size (median 78 vs. 53 mL, P < .001), and higher pulmonary pressures (median 30 vs. 28 mmHg, P < .001). The most common TR types irrespective of rhythm were LV dysfunction-TR and A-STR. Among patients in SR, those with A-STR were older, predominantly women with more diastolic abnormalities and higher pulmonary pressures. All types of secondary TR were associated with all-cause mortality, highest in PH-TR and LV dysfunction-TR. Conclusions New-onset AFib vs. SR conferred a higher risk of the individual TR types related to sequelae of AFib and higher pacemaker implantation rates, although the distribution of TR types was similar. Secondary TR was universally associated with increased mortality.

Background Symptomatic limitations in apical hypertrophic cardiomyopathy may occur because of diastolic dysfunction with resultant elevated left ventricular filling pressures, cardiac output limitation to exercise, pulmonary hypertension (PH), valvular abnormalities, and/or arrhythmias. In this study, the authors aimed to describe invasive cardiac hemodynamics in a cohort of patients with apical hypertrophic cardiomyopathy. Methods and Results Patients presenting to a comprehensive hypertrophic cardiomyopathy center with apical hypertrophic cardiomyopathy were identified (n=542) and those who underwent invasive hemodynamic catheterization (n=47) were included in the study. Of these, 10 were excluded due to postmyectomy status or incomplete hemodynamic data. The mean age was 56±18 years, 16 (43%) were women, and ejection fraction was preserved (≥50%) in 32 (91%) patients. The most common indication for catheterization was dyspnea (48%) followed by suspected PH (13%), and preheart transplant evaluation (10%). Elevated left ventricular filling pressures at rest or exercise were present in 32 (86%) patients. PH was present in 30 (81%) patients, with 6 (20%) also having right‐sided heart failure. Cardiac index was available in 25 (86%) patients with elevated resting filling pressures. Of these, 19 (76%) had reduced cardiac index and all 6 with right‐sided heart failure had reduced cardiac index. Resting hemodynamics were normal in 8 of 37 (22%) patients, with 5 during exercise; 3 of 5 (60%) patients had exercise‐induced elevation in left ventricular filling pressures. Conclusions In patients with apical hypertrophic cardiomyopathy undergoing invasive hemodynamic cardiac catheterization, 86% had elevated left ventricular filling pressures at rest or with exercise, 81% had PH, and 20% of those with PH had concomitant right‐sided heart failure.

Background Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all‐cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer‐assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events. Methods and Results Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle–brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all‐cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow‐up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78–3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49–2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43–22.39]) at 5 years. Conclusions An artificial intelligence–enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.

Objective Tricuspid regurgitation (TR) is a prevalent valve disease associated with significant morbidity and mortality. We aimed to apply machine learning (ML) to assess risk stratification in patients with ≥moderate TR. Methods Patients with ≥moderate TR on echocardiogram between January 2005 and December 2016 were retrospectively included. We used 70% of data to train ML-based survival models including 27 clinical and echocardiographic features to predict mortality over a 3-year period on an independent test set (30%). To account for differences in baseline comorbidities, prediction was performed in groups stratified by increasing Charlson Comorbidity Index (CCI). Permutation feature importance was calculated using the best-performing model separately in these groups. Results Of 13 312 patients, mean age 72 ± 13 years and 7406 (55%) women, 7409 (56%) had moderate, 2646 (20%) had moderate–severe and 3257 (24%) had severe TR. The overall performance for 1-year mortality by 3 ML models was good, c-statistic 0.74–0.75. Interestingly, performance varied between CCI groups, (c-statistic = 0.774 in lowest CCI group and 0.661 in highest CCI group). The performance decreased over 3-year follow-up (average c-index 0.78). Furthermore, the top 10 features contributing to these predictions varied slightly with the CCI group, the top features included heart rate, right ventricular systolic pressure, blood pressure, diuretic use and age. Conclusions Machine learning of common clinical and echocardiographic features can evaluate mortality risk in patients with TR. Further refinement of models and validation in prospective studies are needed before incorporation into the clinical practice.

Abstract Background and Aims Atrial fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF) are intimately associated disorders; HFpEF may be overlooked in AF when symptoms are simply attributed to dysrhythmia, and incident AF may identify patients at risk for developing diastolic dysfunction (DD). This study aimed to investigate the prevalence and incidence of DD in patients with new-onset AF compared with sinus rhythm (SR). Methods Adults with new-onset AF (n = 1747) or SR (n = 29 623) and no structural heart disease were identified. Propensity score matching was performed (1:3 ratio) between AF and SR based on age, sex, body mass index, and comorbidities. Severe DD (SDD) was defined by ≥3/four abnormal parameters (medial e’, medial E/e’, tricuspid regurgitation velocity, and left atrial volume index) and ≥moderate DD (>MDD) by ≥2/4. Annualized changes in DD indices were determined. Results New-onset AF was independently associated with SDD (8% vs. 3%) and ≥MDD (25% vs. 16%); 62% of patients with AF had high-risk H2FPEF scores, and 5% had clinically recognized HFpEF. Over a median follow-up of 3.2 (interquartile range 1.6–5.8) years, DD progressed two–four-fold more rapidly in those with new-onset AF (P < .001 for all). The risk for incident DD was increased in new-onset AF [hazard ratio (95% confidence interval) 2.69 (2.19–3.32) for SDD and 1.73 (1.49–2.02) for ≥MDD]. Conclusions Patients with new-onset AF display high-risk features for HFpEF at diagnosis, emphasizing the importance of evaluating for HFpEF among symptomatic patients with AF. Patients with new-onset AF have accelerated progression in DD over time, which may identify patients with preclinical HFpEF, where preventive therapies may be tested.

Abstract Aims Atrial functional mitral regurgitation (AFMR) has been associated with atrial fibrillation (AF) and heart failure with preserved ejection fraction. However, data on incident AFMR are scarce. We aimed to study the incidence, risk factors, and clinical significance of AFMR in AF or sinus rhythm (SR). Methods and results Adults with new diagnosis of AF and adults in SR were identified. Patients with >mild MR at baseline, primary mitral disease, cardiomyopathy, left-sided valve disease, previous cardiac surgery, or with no follow-up echocardiogram were excluded. Diastolic dysfunction (DD) was indicated by ≥2/4 abnormal diastolic function parameters [mitral medial e′, mitral medial E/e′, tricuspid regurgitation velocity, left atrial volume index (LAVI)]. Overall, 1747 patients with AF and 29 623 in SR were included. Incidence rate of >mild AFMR was 2.6 per 100 person-year in new-onset AF and 0.7 per 100 person-year in SR, P < 0.001. AF remained associated with AFMR in a propensity score-matched analysis based on age, sex, and comorbidities between AF and SR [hazard ratio: 3.80 (95% confidence interval 3.04–4.76)]. Independent risk factors associated with incident AFMR were age ≥65 years, female sex, LAVI, and DD in both AF and SR, in addition to rate (vs. rhythm) control in AF. Incident AFMR was independently associated with all-cause death in both groups (both P < 0.001). Conclusions AF conferred a three-fold increase in the risk of incident AFMR. DD, older age, left atrial size, and female sex were independent risk factors in both SR and AF, while rhythm control was protective. AFMR was universally associated with worse mortality.

Abstract Background and Aims Bicuspid aortic valve (BAV) is the most common congenital heart anomaly. Lifetime morbidity and whether long-term survival varies according to BAV patient-sub-groups are unknown. This study aimed to assess lifetime morbidity and long-term survival in BAV patients in the community. Methods The authors retrospectively identified all Olmsted County (Minnesota) residents with an echocardiographic diagnosis of BAV from 1 January 1980 to 31 December 2009, including patients with typical valvulo-aortopathy (BAV without accelerated valvulo-aortopathy or associated disorders), and those with complex valvulo-aortopathy (BAV with accelerated valvulo-aortopathy or associated disorders). Results 652 consecutive diagnosed BAV patients [median (IQR) age 37 (22–53) years; 525 (81%) adult and 127 (19%) paediatric] were followed for a median (IQR) of 19.1 (12.9–25.8) years. The total cumulative lifetime morbidity burden (from birth to age 90) was 86% (95% CI 82.5–89.7); cumulative lifetime progression to ≥ moderate aortic stenosis or regurgitation, aortic valve surgery, aortic aneurysm ≥45 mm or z-score ≥3, aorta surgery, infective endocarditis and aortic dissection was 80.3%, 68.5%, 75.4%, 27%, 6% and 1.6%, respectively. Survival of patients with typical valvulo-aortopathy [562 (86%), age 40 (28–55) years, 86% adults] was similar to age-sex-matched Minnesota population (P = .12). Conversely, survival of patients with complex valvulo-aortopathy [90 (14%), age 14 (3–26) years, 57% paediatric] was lower than expected, with a relative excess mortality risk of 2.25 (95% CI 1.21–4.19) (P = .01). Conclusion The BAV condition exhibits a high lifetime morbidity burden where valvulo-aortopathy is close to unavoidable by age 90. The lifetime incidence of infective endocarditis is higher than that of aortic dissection. The most common BAV clinical presentation is the typical valvulo-aortopathy with preserved expected long-term survival, while the complex valvulo-aortopathy presentation incurs higher mortality.

Males are characterized by lower ANP and BNP with greater prevalence of cardiometabolic disease. The ANP genetic variant rs5068 was associated with less metabolic dysfunction in males, whereas no metabolic profile was related to the BNP genetic variant rs198389 in the general population. ANP may play a more biological role in metabolic homeostasis compared with BNP in the general population with greater physiological metabolic actions in males compared with females.

PURPOSE Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown. METHODS We identified 2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance. RESULTS On mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures ( P LRT values < .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, Δ AUC 0.065, P = .01) but did not reach statistical significance for interval cancer. CONCLUSION AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.