Citations

Abstract Aims This study aimed to evaluate the clinical significance of secondary mitral regurgitation (MR) in patients with heart failure with preserved ejection fraction (HFpEF). Methods and results We conducted a prospective study enrolling consecutively evaluated patients with HFpEF undergoing invasive haemodynamic exercise testing with simultaneous echocardiography. Compared to HFpEF without MR (n = 145, 79.7%), those with mild or moderate MR (n = 37, 20.3%) were older, more likely to be women, had more left ventricular (LV) systolic dysfunction, and more likely to have left atrial (LA) myopathy reflected by greater burden of atrial fibrillation, more LA dilatation, and poorer LA function. Pulmonary artery (PA) wedge pressure was higher at rest in HFpEF with MR (17 ± 5 mmHg vs. 20 ± 5 mmHg, p = 0.005), but there was no difference with exercise. At rest, only 2 (1.1%) patients had moderate MR, and none developed severe MR. Pulmonary vascular resistance was higher, and right ventricular (RV)-PA coupling was more impaired in patients with HFpEF and MR at rest and exercise. LV and LA myocardial dysfunction remained more severe in patients with MR during stress compared to those without MR, characterized by greater LA dilatation during all stages of exertion, lower LA emptying fraction and compliance, steeper and rightward-shifted LA pressure–volume relationships, and reduced LV longitudinal contractile function. Conclusions Patients with HFpEF and mild or moderate MR have more severe LV systolic dysfunction, LA myopathy, RV-PA uncoupling, and more severe pulmonary vascular disease. Mitral valve incompetence in this setting is a phenotypic marker of more advanced disease but is not a causal factor in development of HFpEF.

Abstract Introduction Postoperative atrial fibrillation (POAF) after noncardiac surgery constitutes 10-15% of all cases of incident atrial fibrillation (AF) in the community and has been associated with an increased risk of stroke. The risk of subsequent AF among patients with POAF is not well defined. Purpose To evaluate the role of systematic 30-day electrocardiographic (ECG) monitoring in the detection of AF recurrence after POAF. Methods In a prospective interventional study, patients who underwent major noncardiac surgeries and had new AF documented by ECG in the post-operative period between May 2022 and April 2023 were identified. For these patients, a 30-day ambulatory ECG monitor was recommended upon discharge, with final ordering deferred to the primary inpatient teams. Rates of recurrent AF of any duration detected by 30-day monitoring plus routine care versus routine care alone after the surgery were compared. Results Eighty-five patients with new-onset POAF were included (mean age 72 years, male 67%). Of these, 35 (41%) patients underwent up to 30 days [median 23 (IQR 6-27) days] of continuous ECG monitoring after hospital discharge. There were no significant differences in baseline characteristics between patients who did or did not undergo monitoring (Table 1). During median 281 (IQR 197-385) days of follow-up, recurrent AF was documented in 14/35 (40%) patients in the monitoring group as opposed to 7/50 (14%) patients in the usual care group (log-rank p=0.003, Figure 1). The median time from day of surgery until documented AF recurrence was 25 (IQR 6-53) days in the monitoring group and 66 (IQR 25-145) in the usual care group. Overall, 81% of recurrent AF events in both groups were detected in the first 3 months after surgery. The median burden of AF in the cardiac monitoring group was 7% (IQR 1-15), with the longest AF event lasting a median of 6 (IQR 2-18) hours. Anticoagulation for AF was prescribed in 24/64 (37%) patients with POAF without recurrence and in 16/21 (76%) patients with recurrent AF. Conclusion Recurrent AF following POAF is common and detected most frequently in the first 3 months after noncardiac surgery. Systematic ambulatory rhythm monitoring is associated with a higher rate of AF detection following hospital discharge and may identify a patient subgroup who might benefit from anticoagulation therapy.Table 1Figure 1

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.

Introduction: Graves’ disease (GD) is known to cause menstrual changes and has been linked to infertility in women. This can be due to the increased production of androstenedione and testosterone induced by the excess thyroid hormone. However, screening for sex hormone levels in all women diagnosed with GD to identify patients more likely to have infertility is not cost-effective and not clinically indicated. AI-augmented ECG can identify the signature of female with an AUC of 0.97. In this model, AI- ECG gives a probability number between 0-100% of being female, with higher numbers indicating high probability of being female. Hypothesis: We hypothesized that this inexpensive AI ECG tool is reflective of hormonal levels and the resulting changes in menstrual cycle and fertility associated with GD in women. Methods: Patients diagnosed with GD (2009-2019) at our institution were included. Three intervals were identified: 1) Before GD: >2 months before GD diagnosis 2) During GD: within 2 months before GD and before attaining euthyroidism 3) After GD: after attaining euthyroidism. The matched-pairs t-test was used to compare probability of being a female; patients acted as their own controls. A subset analysis was done on females <45 years at GD diagnosis to study the association between female probability during GD and menstrual changes (defined as shorter, lighter, or newly irregular menstrual cycles). Results: 154 females with ECGs both before and during GD were identified. AI ECG female probability decreased with GD diagnosis (85% vs. 77%, P<.001). On the other hand, 137 females with GD had ECGs during and after GD. Female probability increased after attaining euthyroidism (76% vs 83%, P=.005). Moreover, 72 females <45 years were identified; 30 (42%) developed menstrual changes and had significantly lower AI-ECG female probability [77 (58-94)% vs 98 (92-100)%, P<.001] Conclusions: AI-enabled ECG signature of female gender in women was altered by GD and was associated with menstrual changes, likely reflecting changes in sex hormone levels. Future studies to confirm its role in screening for infertility in women with GD are needed.

Introduction: Acceleration time (AT) and Doppler Velocity Index (DVI) are critical parameters in assessment of bioprosthetic aortic valve dysfunction (BAVD). AT>100ms and DVI < 0.25 are highly suggestive of prosthetic obstruction and are incorporated in all triage algorithms. Little is known about their relationship with flow. Hypothesis: AT and DVI may be affected by transprosthetic flow in patients with significant bioprosthetic aortic valve dysfunction (BAVD). Methods: Single center, retrospective analysis of patients with severe BAVD (confirmed at surgical re-replacement, transcatheter valve-in-valve implantation, or successfully treated bioprosthetic valve thrombosis). Standard prosthetic parameters (AT, DVI, effective orifice area (EOA), mean gradient, presence and degree of regurgitation), flow (stroke volume index, Svi; transprosthetic flow rate, FR) were measured on the transthoracic echocardiogram at the time of diagnosis with severe BAVD. Results: A total of 266 patients with severe BAVD underwent treatment between 2008-2020. BAVD mechanism was obstruction (N=192, 72%), regurgitation (n=48, 18%) mixed stenosis and regurgitation (n=15, 6%) and patient-prosthetic mismatch (n=12, 5%). DVI was moderately correlated with FR (r=0.50, p<0.001); AT had a weak but significant inverse correlation with FR (r=0.24, p<0.001). SVi had a weaker influence on DVI (r=0.35, p<0.001) and no correlation with AT. Conclusions: AT and DVI are influenced by transprosthetic flow rate. This flow-dependency should be considered when assessing the mechanism of BAVD.

Abstract Aims Augmenting echocardiography with artificial intelligence would allow for automated assessment of routine parameters and identification of disease patterns not easily recognized otherwise. View classification is an essential first step before deep learning can be applied to the echocardiogram. Methods and results We trained two- and three-dimensional convolutional neural networks (CNNs) using transthoracic echocardiographic (TTE) studies obtained from 909 patients to classify nine view categories (10 269 videos). Transthoracic echocardiographic studies from 229 patients were used in internal validation (2582 videos). Convolutional neural networks were tested on 100 patients with comprehensive TTE studies (where the two examples chosen by CNNs as most likely to represent a view were evaluated) and 408 patients with five view categories obtained via point-of-care ultrasound (POCUS). The overall accuracy of the two-dimensional CNN was 96.8%, and the averaged area under the curve (AUC) was 0.997 on the comprehensive TTE testing set; these numbers were 98.4% and 0.998, respectively, on the POCUS set. For the three-dimensional CNN, the accuracy and AUC were 96.3% and 0.998 for full TTE studies and 95.0% and 0.996 on POCUS videos, respectively. The positive predictive value, which defined correctly identified predicted views, was higher with two-dimensional rather than three-dimensional networks, exceeding 93% in apical, short-axis aortic valve, and parasternal long-axis left ventricle views. Conclusion An automated view classifier utilizing CNNs was able to classify cardiac views obtained using TTE and POCUS with high accuracy. The view classifier will facilitate the application of deep learning to echocardiography.

Abstract Aims We aimed to clarify the extent to which cardiac and peripheral impairments to oxygen delivery and utilization contribute to exercise intolerance and risk for adverse events, and how this relates to diversity and multiplicity in pathophysiologic traits. Methods and results Individuals with heart failure with preserved ejection fraction (HFpEF) and non-cardiac dyspnoea (controls) underwent invasive cardiopulmonary exercise testing and clinical follow-up. Haemodynamics and oxygen transport responses were compared. HFpEF patients were then categorized a priori into previously-proposed, non-exclusive descriptive clinical trait phenogroups, including cardiometabolic, pulmonary vascular disease, left atrial myopathy, and vascular stiffening phenogroups based on clinical and haemodynamic profiles to contrast pathophysiology and clinical risk. Overall, patients with HFpEF (n = 643) had impaired cardiac output reserve with exercise (2.3 vs. 2.8 L/min, p = 0.025) and greater reliance on peripheral oxygen extraction augmentation (4.5 vs. 3.8 ml/dl, p &lt; 0.001) compared to dyspnoeic controls (n = 219). Most (94%) patients with HFpEF met criteria for at least one clinical phenogroup, and 67% fulfilled criteria for multiple overlapping phenogroups. There was greater impairment in peripheral limitations in the cardiometabolic group and greater cardiac output limitations and higher pulmonary vascular resistance during exertion in the other phenogroups. Increasing trait multiplicity within a given patient was associated with worse exercise haemodynamics, poorer exercise capacity, lower cardiac output reserve, and greater risk for heart failure hospitalization or death (hazard ratio 1.74, 95% confidence interval 1.08–2.79 for 0–1 vs. ≥2 phenogroup traits present). Conclusions Though cardiac output response to exercise is limited in patients with HFpEF compared to those with non-cardiac dyspnoea, the relative contributions of cardiac and peripheral limitations vary with differing numbers and types of clinical phenotypic traits present. Patients fulfilling criteria for greater multiplicity and diversity of HFpEF phenogroup traits have poorer exercise capacity, worsening haemodynamic perturbations, and greater risk for adverse outcome.

Background: Our team has previously developed convolutional neural networks (CNNs) to estimate age and sex from a 10-second, 12-lead ECG as indicators of patient wellness. Here, we develop an additional wellness network to estimate body mass index (BMI) from the ECG input signal. Aims: To evaluate the performance of neural networks trained to classify and directly estimate BMI. Methods: We identified all adult patients in the Mayo Clinic system who had a 10-second, 12-lead ECG within 15 days of a BMI measurement. Of the&nbsp;174,863 patients in the cohort, 2,895 were underweight (BMI < 18.5), 42,387 were of normal weight (BMI 18.5-24.9), and 57,939 were overweight only (BMI 25.0-29.9). Among patients categorized as obese, 39,788 had class I obesity (BMI 30.0-34.9), 18,978 had class II obesity (BMI 35.0-39.9), and 12,876 had class III obesity (BMI ≥ 40.0). The cohort was split into training, validation, and testing datasets in an 8:1:1 ratio to develop networks for BMI classification and a direct&nbsp;estimation of BMI. Results: In detecting overweight in the testing set, the model achieved an AUC of 0.86 (95% CI 0.85, 0.87). In detecting class I, class II, and class III obesity, the model yielded AUCs of 0.84 (95% CI 0.83, 0.85), 0.84 (95% CI 0.83, 0.85), and 0.85 (95% CI 0.85, 0.86), respectively. The model to estimate BMI achieved a mean absolute error of 3.70. Conclusions: AI-enabled ECGs demonstrate efficient detection of overweight patients and all classes of obesity. Further research is needed to determine if discrepancies between the traditionally measured BMI and the AI-ECG BMI reflect the presence or lack of metabolic abnormalities.

Background: Enlargement or dilation of the left ventricle (LV) is associated with increased cardiovascular morbidity and mortality, and is an established precursor of ventricular dysfunction and heart failure in almost all major cardiovascular conditions. Although there are existing artificial intelligence frameworks that make use of deep learning to detect LV enlargement, many entail LV segmentation, which can be error-prone especially for point-of-care ultrasound (POCUS) images. Goal: To develop a fully end-to-end deep learning framework that takes as input either transthoracic echocardiographic (TTE) or POCUS 2D image clips, and classifies directly (i.e., without first segmenting the LV) whether a patient has an enlarged LV. Methods: Using a sample of retrospective TTE data enriched for LV enlargement (out of 13,868 patients total, 3,351 had an enlarged LV defined using sex-specific guideline-directed criteria), we trained a classifier that takes as input apical 2-chamber and parasternal long axis videos, and provides as output a binary label denoting whether a patient has an enlarged LV. Then, we evaluated the trained model using a holdout testing dataset of retrospective TTE data (2,087 patients) as well as data from a prospective cohort of 428 patients from whom TTE and POCUS were collected simultaneously. Results: For the retrospective TTE testing dataset, the AUC was 0.851 (95% confidence interval (CI) of 0.830-871). For the prospective cohort, the model performed comparably for both TTE and POCUS video clips (TTE: AUC of 0.865, 95% CI of 0.823-0.906; POCUS: AUC of 0.840, 95% CI of 0.797-0.883). Conclusions: The current deep learning model performed comparably well for both TTE and POCUS echocardiograms. We regard these results as a proof-of-concept of the potential utility of this model in helping point-of-care clinicians make decisions regarding whether patients should receive further evaluation for LV enlargement and associated conditions.

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.