Pislaru, Sorin V.

Background: Diagnosis of heart failure with preserved ejection fraction (HFpEF) is challenging due to non-diagnostic imaging outputs and discordant clinical features. Using only a single videoclip from a standard transthoracic echocardiogram (TTE), we developed an artificial intelligence (AI) model to differentiate patients with HFpEF from those without HFpEF. In a separate test population, we assessed the model’s ability to predict cardiac mortality. Methods: A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber videoclips to classify patients with HFpEF (cases) versus without HFpEF (controls). Overall and cardiac mortality were obtained from the National Death Index. Results: The model was independently tested in 646 cases and 638 controls from 8 centers in 4 states. Sensitivity (87.8%; 84.5-90.9%) and specificity (81.9%; 78.2-85.6%) were high and sustained in various subgroups, including increased left atrial volume index, average E/e’ ≥15, and pulmonary artery systolic pressure >35 mm Hg. During follow-up (median [IQR] 3.4 [1.7-6.5] years), 540 patients died. There were 135 cardiac deaths attributable to heart failure (47%), coronary artery disease (41%), valve disease (4%), arrhythmia (4%), and others (5%). Compared to a negative output, all-cause mortality was higher in patients with a positive (HR 2.9 [2.4-3.5]) or uncertain output (HR 1.8 [1.2-2.5]), and cardiac mortality (Figure) was also higher in patients with a positive (HR 7.7 [4.5-13.3]) or uncertain output (HR 3.4 [1.4-8.1]). Cardiac mortality differences according to AI model prediction remained after age adjustment (positive: HR 5.9 [3.3-10.4], p<0.001; uncertain: HR 2.8 [1.2-6.8], p=0.02). Conclusion: A novel AI model to detect HFpEF based on a single routinely acquired TTE videoclip also identifies those at increased risk of cardiac mortality.

Introduction: Rate of aortic stenosis (AS) progression in patients with concurrent atrial fibrillation (AF) has not been previously described. Objectives: To describe changes over time in aortic valve area (AVA) and associated hemodynamics during AS progression in patients in AF versus sinus rhythm (SR). Methods: Patients with AVA ≤ 1 cm 2 and preserved left ventricular ejection fraction ≥50% were identified from the Mayo Clinic Echocardiographic Laboratory Database. Serial echocardiograms prior to the development of AVA ≤ 1 cm 2 (Time 0) were examined. Inverse probability weighting was used for statistical analysis. Results: A total of 166 patients were included (SR=79 and AF=87), mean age 78.1 ± 7.8 years, females 31%. The rates of AVA reduction (-0.14±0.01 vs -0.12±0.01 cm2/year, p=0.31) and increase in trans-aortic peak velocity (+0.23 ± 0.02 vs +0.22 ± 0.02 m/sec, p=0.55) and mean gradient (MG) (3.9 ± 0.3 vs 3.8 ± 0.4 mmHg, p=0.83) did not differ between AF and SR (Figure 1). However, AF was associated with systematically lower forward stroke volume index (SVi), peak velocity and MG for the same AVA compared to SR (p<0.04 for all) throughout AS progression. When AVA reached 1 cm 2 , SVi (38.8±6.6 vs 45.7 ±8.9, p=0.02), peak velocity (3.9 ± 0.6 vs 4.1 ± 0.6 m/sec, p=0.04) and MG (36.6±10.9 vs 41±12.8 mmHg, p=0.03) were lower in AF compared to SR. Conclusions: The rate of AS progression was not different in AF versus SR. However, forward flow was systematically lower in AF resulting in discordant AS during AS progression and when AVA reached 1 cm 2 . Doppler velocity and MG criteria for severe AS appear to be lower in AF versus SR which has implications for timely diagnosis of severe AS and patient management.

Background: Recently, our team developed an end-to-end artificial intelligence (AI) framework that can successfully estimate left ventricular ejection fraction (LVEF) from echocardiogram videos. However, this framework requires specific views (A2C and PLAX), and cannot estimate LVEF for studies from which these views are missing. Goal: To develop a deep learning model to estimate LVEF from any type and number of echocardiogram video(s). Methods: We built a deep learning model for B-mode echocardiogram videos of the left ventricle (A2C, A3C, A4C, or PLAX views), using retrospective transthoracic echocardiography (TTE) data. The model was evaluated with two test datasets: one with random valid (i.e., showing the left ventricle) views, and one with preselected views (both having 433 patients). Later, the model was tested with a prospective patient cohort that had both TTE and point-of-care ultrasound (POCUS) data collected simultaneously (393 patients). Results: On the retrospective TTE test set, we observed excellent performance (five random valid views per study: RMSE of 6.87% and Pearson correlation (PC) of 0.82 on LVEF estimation, and an AUC of 0.96 (95% CI: of 0.94-0.98) on low LVEF classification (<=40%); preselected views: RMSE of 6.75%, PC of 0.83, and an AUC of 0.96 (95% CI: 0.94-0.98)). For both TTE and POCUS data from the prospective cohort, we also observed strong performance (TTE: RMSE of 5.62%, PC of 0.87, and an AUC of 0.97; POCUS: RMSE of 6.12%, PC of 0.84, and an AUC of 0.96). For the prospective test sets as well as for the retrospective TTE test set with preselected views, results reflect the estimated LVEF average of A2C, A3C, A4C, and PLAX videos from each patient. Conclusions: For both TTE and POCUS data, the current model showed strong performance which was comparable to that of our previously established view-specific model. Alongside this comparable performance, the current model surpasses our previous model in terms of flexibility, as it isn't dependent on view classification and allows a broader selection in terms of the number and types of videos per study or patient. This innovative approach could prove particularly beneficial in point-of-care situations in which it may not be feasible to obtain high-quality, view-specific videos.

Introduction: Doppler mean gradient (MG) can underestimate the severity of aortic stenosis (AS) in patients with atrial fibrillation (AF) compared to patients in sinus rhythm (SR). Patients with AF and severe AS may experience delayed diagnosis and more advanced disease at time of referral for transcatheter aortic valve replacement (TAVR). Methods: Patients who underwent TAVR from 2013 to 2017 for native valve severe AS were identified from an institutional database. MG and aortic valve calcium score (AVCS) at time of referral were compared in patients in AF and SR. Retrospective theoretical AVCS in AF patients were derived assuming an average progression of 152 AU/year (published data). Results: There were 820 patients included; mean age was 81.3+/- 8.2 years and 41.6% were female. AF was present in 356 patients at time of referral. Patients in AF were older (82.2 vs. 80.5, p = 0.003), had lower MG compared to SR patients (42.0 vs. 44.9, p = 0.002) and a lower peak velocity (4.1 vs. 4.2, p = 0.010) with a similar indexed aortic valve area (0.4 vs. 0.4, p = 0.17). Stratified by sex, the median calcium score for AF patients was higher (by around 300 AU) among both males (AF 2850.0 vs. SR 2561.0, p = 0.044) and females (AF 1942.0 vs. 1610.5, p = 0.025) ( figure 1 ). A density plot of retrospective theoretical AVCS in AF patients (projected assuming referral to TAVR approximately two years prior) resulted in nearly identical median AVCS between the two cohorts ( figure 2 ). Conclusions: Older age and higher AVCS in patients with AF compared to SR suggests that AS was both underestimated and more advanced at time of referral to TAVR. Patients in AF may have severe AS as early as 2 years prior to TAVR referral.

Background: Clinically significant tricuspid regurgitation (TR) is an important contributor to morbidity and mortality. The Tricuspid Regurgitation Impact on Outcomes (TRIO) score based on 8 parameters (age, gender, chronic heart failure, chronic lung disease, heart rate, creatinine, aspartate transaminase level, and TR severity) is used for risk stratification. The performance of the TRIO score in patients with TR secondary to severe pulmonary hypertension (PH) is unknown. Research Questions: To evaluate whether the TRIO score can be applied in patients with TR and pulmonary hypertension (PH), and to identify other predictors of mortality. Methods: Retrospective study including patients with TR ≥ moderate. Severe PH was defined as right ventricular systolic pressure (RVSP) ≥50 mmHg. The impact of clinical, echocardiographic and laboratory variables on mortality was evaluated with the Cox proportional hazard method. Results: A total of 13,482 patients from the TRIO population were included; severe PH was present in 6,530 (48.4%); mean age 73.4 + 13.3, 55.7% females. TRIO score performance was modest (AUC 0.62) in risk stratification (Figure A). PH patients had significantly higher mortality than those without PH at all levels of TRIO score (Figure B); differences were progressively lower with increasing TRIO scores. At multivariate analysis, only TRIO score [HR = 1.54 (1.35-1.77) per unit change], presence of an elevated NT-proBNP [HR = 1.02 (1.02-1.03) per 1000 pg/ml change], RVSP (HR = 1.08 (1.02-1.16) per 10 mmHg change], presence of RV dysfunction [HR = 1.15, (1.05-1.26)] and pulmonary artery pulsatility index (PAPi) ≤ 4 [HR = 1.34 (1.07-1.67)] were significant predictors of mortality (model AUC 0.65). Conclusions: TRIO score allows risk stratification in PH patients, but performance is modest; incorporating additional factors marginally increased model performance. PH patients have higher mortality at all levels of TRIO score, more so at low scores.

Introduction: Isolated functional tricuspid regurgitation (FTR) in the absence of structural heart disease may develop secondary to elevation in left heart pressures caused by left ventricular diastolic dysfunction (DD), as in heart failure with preserved ejection fraction (HFpEF). FTR may also herald the presence of occult DD or indicate greater risk for new-onset DD. However, prevalence and incidence of diastolic dysfunction (DD) in isolated FTR have not been studied. Aims: Investigate the prevalence and incidence of DD across the different severities of FTR of unclear cause. Methods: Adults with normal EF and without structural heart disease undergoing echocardiography at a tertiary medical center with a repeat study ≥6 months from baseline were identified. Severe DD was defined by ≥3/4 abnormal DD parameters (medial e’, medial E/e’, tricuspid regurgitation velocity, left atrial volume index) and moderate DD by ≥2. Results: Among 30,428 patients (median age 60; 50% women), FTR was absent in 73%, mild in 22%, moderate in 4%, and severe in 0.4%. Severe DD was present in 2%, 5%, 11%, and 13% patients, respectively, (p<.001) and moderate DD in 9%, 19%, 31%, and 48%, respectively (p<.001). The association of DD with increasing FTR was independent of all clinical characteristics. Over median 3.0 (IQR 1.5-5.4) years, incident severe and moderate DD developed more frequently with increasing FTR severity ( Figure) . Findings were confirmed in a propensity-matched subset where patients were matched for age, sex, BMI, atrial fibrillation, and comorbidities and in patients without lung disease or ≥moderate right ventricular enlargement. Conclusions: Patients with FTR of undefined etiology commonly have DD suggesting the presence of underlying left heart disease. Patients without apparent DD or HF at baseline who have FTR remain at significantly increased risk of developing DD over follow-up and may be a target population for therapies to prevent progression to clinical HFpEF.

Introduction: Tricuspid regurgitation is a common echocardiographic finding leading to excess morbidity and mortality in adult population. Hypothesis: Racial and ethnic disparities have been observed in various cardiovascular disorders’ prevalence and natural course, including valvular lesions. However, little is known about the epidemiology and clinical correlations of TR among non-Hispanic Black adults. Methods: To examine risk factors and clinical outcomes in non-Hispanic Black adults with moderate or more severe TR, 685 patients (7%) self-identified as non-Hispanic Black and 9127 (93%) self-identified as non-Hispanic White with a first echocardiographic report of moderate or more severe TR between 2005-2016 were studied. Results: Non-Hispanic Black patients had more severe TR when compared to white patients (25% vs. 20%, p<0.001). After adjusting for age and sex, non-Hispanic Black patients had significantly higher 10-year all-cause mortality (HR=1.34 (1.18, 1.52), p<0.001). Black race remained a risk factor for all-cause mortality even after adjustment for comorbidities like CHF, COPD, DM, CKD, hypertension, and ischemic heart disease (HR=1.23 (1.08, 1.40), p=0.002). Conclusions: Significant racial disparities exist in demographic, clinical and echocardiographic characteristics between non-Hispanic Black and White patients with moderate or more severe TR. Black race is a potential disease modifier leading to excess mortality.

ABSTRACT Aim Left atrial (LA) myopathy is increasingly recognized as an important phenotypic trait in heart failure (HF) with preserved ejection fraction (HFpEF). Right atrial (RA) remodelling and dysfunction also develop in HFpEF, but little data are available regarding the clinical characteristics and pathophysiology among patients with isolated LA, RA, or biatrial myopathy. Methods and results Patients with HFpEF underwent invasive haemodynamic exercise testing, comprehensive imaging including speckle tracking strain echocardiography, and clinical follow-up at Mayo Clinic between 2006 and 2018. LA myopathy was defined as LA volume index &gt;34 ml/m2 and/or LA reservoir strain ≤24% and RA myopathy by RA volume index &gt;39 ml/m2 in men and &gt;33 ml/m2 in women and/or RA reservoir strain ≤19.8%. Of 476 consecutively evaluated patients with HFpEF defined by invasive exercise testing with evaluable atrial structure/function, 125 (26%) had no atrial myopathy, 147 (31%) had isolated LA myopathy, 184 (39%) had biatrial myopathy, and 20 (4%) had isolated RA myopathy. Patients with HFpEF and biatrial myopathy had more atrial fibrillation, poorer left ventricular systolic and diastolic function, more severe pulmonary vascular disease, tricuspid regurgitation, ventricular interdependence and right ventricular dysfunction, and poorer cardiac output reserve with exercise. There were 94 patients with events over a median follow-up of 2.9 (interquartile range 1.4–4.6) years. Individuals with biatrial myopathy had an 84% higher risk of HF hospitalization or death as compared to those with isolated LA myopathy (hazard ratio 1.84; 95% confidence interval 1.16–2.92, p = 0.01). Conclusions Biatrial myopathy identifies patients with more advanced HFpEF characterized by more severe pulmonary vascular disease, right HF, poorer cardiac reserve, and a greater risk for adverse outcomes. Further study is required to define optimal strategies to treat and prevent biatrial myopathy in HFpEF.

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 Atrial functional mitral regurgitation (AFMR) is an increasingly recognized cause of functional MR thought to result from left atrial enlargement and myopathy in the setting of preserved left ventricular geometry and function. AFMR mostly occurs in patients with atrial fibrillation (AF) and/or heart failure with preserved ejection fraction. Purpose We investigated whether the use of an artificial intelligence-based electrocardiogram (AI-ECG) can identify patients with new-onset AF and sinus rhythm (SR) who are at highest risk of developing AFMR. Methods Adults with a new diagnosis of AF between 2010 and 2021 who had a transthoracic echocardiogram (TTE) within 1 month after AF diagnosis were identified. Similarly, adults in SR who never had AF diagnosis and had a TTE during the same period were identified. Exclusion criteria from both groups included &gt;mild MR at baseline, primary mitral valve disease, cardiomyopathy, ≥moderate aortic valve disease, current or previous low ejection fraction, enlarged left ventricle, cardiac devices, previous cardiac surgeries, and the absence of follow-up TTE ≥6 months from baseline. Only patients who had ECG within 10 days of the TTE were included. When multiple ECGs were available, the closest was considered. A previously trained AI-ECG algorithm to detect the probability of AF was utilized. Incident AFMR was defined as &gt;mild functional MR in the absence of &gt;mild left ventricle enlargement or dysfunction. Diastolic dysfunction was defined according to ASE/EACVI guidelines. Results Overall, 1,570 patients with new-onset AF (median age 67 years; 34% females) and 16,802 patients in SR (median age 59 years; 46% females) were included. The median AI ECG probability for AF was 43% (IQR 26%-59%) in the new-onset AF group and 2% (0.5%-7%) in the SR group, Figure 1. Incident AFMR occurred in 152 patients in the AF group over median 3.06 (IQR 1.5-5.2) years of follow-up and in 482 patients in the SR group over median 3.0 (1.5-5.6) years of follow-up. The probability of silent AF by the AI-ECG was associated with incident AFMR in the new-onset AF group [hazard ratio 1.19 (95% CI 1.11-1.28) per 10%, p &lt;.001] and the SR group [hazard ratio 1.19 (95% CI 1.12-1.25) per 10%, p &lt;.001]. The AI ECG-derived AF probability remained as an independent risk factor for AFMR in multivariable analysis in both AF and SR, Table 1. Conclusion The AI ECG for AF might be able to reflect the degree of left atrial myopathy and thus the risk of incident AFMR in patients with AF and SR.Distribution of AI ECG probability of AFMultivariable risk factors for AFMR