Michelena, H.

Abstract Background Transthoracic echocardiography is the primary modality used for the assessment of the pericardial space. Guidelines recommend semi-qualitative assessment of the size of a pericardial effusion with characterization as trace, small, moderate or large. The reproducibility and association of this characterization is variable. Whether simple 2D-based quantitative measures of effusion size better associate with pericardial volumes is unclear. An accurate measure of pericardial volume would be helpful for tracking patients over time. Purpose To test whether 2D-based measures of the pericardial space could accurately predict pericardial volumes. Methods We retrospectively assessed a series of echos performed immediately prior to pericardiocentesis. 2D-based area tracings from the parasternal long and short axis at the papillary muscle level as well as 2D volumetric tracings from the apical 4 chamber view using the Simpson's method were tested with the operator blinded to the true pericardial volume (recorded at pericardiocentesis). Patients who underwent pericardiocentesis in setting of acute perforation were excluded as were patients where pericardiocentesis was unsuccessful or incomplete. Pericardial fluid area or volume was calculated by taking the difference between the measures obtained by tracing the epicardial border of the heart and pericardium (figure 1). Results The median pericardiocentesis (n=118) fluid volume was 500 mL (IQR 350 – 750 mL) with a range, 95–2300 mL. The pericardial fluid was serous (22%), sero-sanguineous (37%) and sanguineous in 41%. The effusion was malignant in 24 cases and due to inflammation in 42 cases. Pericardial area traces from either the parasternal short (r=0.56, p<.0001) or long axis (r=0.62, p<.0001) correlated modestly with measured pericardial volume. The average volume estimated echocardiographically by the Simpson’s method was 580 ± 359 mL (range, 85.8–2419 mL). There was stronger linear correlation between echocardiographic (apical 4-chamber measurement) and pericardiocentesis-derived volumes (r=0.92, p < 0.0001; figure 2A). Etiology of the pericardial effusion was not associated with estimates of pericardial volumes. There was a large overlap in pericardial volume amongst the reading physician qualitative grading of effusion size (figure 2B). Categorization of pericardial effusion size was improved when based on volumetric estimates (Kappa 0.65) rather than physician estimates (Kappa 0.32; Figure 2B). Variation between measured pericardial volume and actual pericardial volume was most related to the quality of the image and the likely extent to which the pericardial fluid was not entirely captured in the image window. Conclusion Two-dimensional transthoracic echocardiography using biplane Simpson’s method of disks can simply and reasonably accurately estimate the pericardial effusion volume. This technique may add value in the serial assessment of pericardial effusions. Figure 2

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 >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 >mild functional MR in the absence of >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 <.001] and the SR group [hazard ratio 1.19 (95% CI 1.12-1.25) per 10%, p <.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

Abstract Funding Acknowledgements Type of funding sources: None. Background Atrial functional mitral regurgitation (AFMR) has been associated with atrial fibrillation (AF) and heart failure with preserved ejection fraction. However, data on its incidence, risk factors, and clinical outcomes are scarce. Objective We aimed to study the incidence, risk factors and clinical significance of AFMR, defined as >mild functional MR in the absence of more than mild left ventricle enlargement or dysfunction, in AF and sinus rhythm (SR). Methods Adults with a new diagnosis of AF between 2010–2021 who had a transthoracic echocardiogram (TTE) were identified (n=57,109). Similarly, adults with no diagnosis of AF who had a TTE between 2010–2021 were identified for comparison (n= 364,767). Patients with >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, or with no follow up TTE ≥6 months from baseline were excluded. Furthermore, patients with AF had to have a TTE within 1 month of AF diagnosis. The presence of ≥2 of 4 abnormal diastolic function parameters [mitral septal e’ <0.07 m/sec, mitral septal E/e’ >15 in SR and >11 in AF, TR velocity >2.80 m/sec, left atrial volume index >34 ml/m2] was determined. Cox proportional hazards regression was used to determine risk factors associated with incident AFMR and all-cause death. Patients without the event of interest were censored at last follow up. Patients who developed >mild MR of other types (primary or functional ventricular) were censored at that time. Results Overall, 1,747 (median age 67 years; 34% females) patients with AF and 29,623 (median age 59; 51% females) in SR were included. Incident AFMR occurred in 167 patients with AF over median 3.1 (IQR 1.6–5.1) years [incidence rate of 2.6 per 100-person year] and in 780 patients in SR over median 3.1 (IQR 1.5–5.8) years [incidence rate 0.7 per 100-person year] (Figure 1). AF remained associated with AFMR after adjusting to confounding factors [age ≥65, female sex, ≥2 abnormal diastolic function parameters, and low hemoglobin; adjusted HR 3.23 (2.71–3.85)]. Independent risk factors associated with incident AFMR in AF were age ≥65, female sex, persistent AF, and ≥2 abnormal diastolic function parameters, whereas successful rhythm control without recurrence was a protective factor (Figure 2a). Risk factors for AFMR in SR were age ≥65 years, female sex, presence of ≥2 abnormal diastolic function parameters, and low hemoglobin (Figure 2a). Incident AFMR was independently associated with all-cause death in both AF and SR (Figure 2b). Conclusions AF confers more than 3-fold increase in the risk of AFMR. Diastolic dysfunction, older age, and female sex were independent risk factors for incident AFMR in both SR and AF. Paroxysmal AF and successful rhythm control were protective factors against AFMR in AF. AFMR is universally associated with worse mortality.

Abstract Funding Acknowledgements Type of funding sources: None. Background Aortic regurgitation (AR) is a common valvular lesion associated with left ventricular (LV) enlargement and dysfunction and increased risk of death with onset of symptoms, significant LV enlargement, or systolic dysfunction. The current guidelines are based on symptoms, LV size and LV function to evaluate the candidacy for surgery in patients with severe AR. Purpose We propose a machine-learning (ML) based algorithm to identify patients at risk for death from AR independent of aortic valve replacement (AVR) surgery (patients were censored at AVR event unless the operation occurred within 100 days of the first visit, in which case baseline AVR is included as a predictor). Methods The model was trained with 5-fold cross-validation on a dataset of 1035 patients. Model performance is reported on an independent dataset of 207 patients. Optimal predictive performance was observed with a Conditional Random Survival Forest model. A subset of 19 / 41 variables were selected for inclusion in the final model. Variable selection was performed with 10-fold cross validation using Random Survival Forest model; variables that were on an average selected by less than 6/10 splits were excluded. Results The mean age of patients was 60 ± 17 years and 198 (18%) were females. The variables included in the models were age, end-diastolic volume, end systolic dimension, body surface area, ejection fraction, NYHA class, body mass index, diastolic blood pressure, bicuspid valve, regurgitant volume, filling pressure, mid ascending aorta diameter, mitral tissue early relaxation velocity, pulmonary artery systolic pressure, and comorbidities, and the relative variable importances averaged across five splits of cross validation in each repeat were evaluated (Figure). The predictive AUC for predicting survival of the best-performing model was 0.84 at 1 year, 0.86 at 2 years, and 0.87 overall, respectively. Conclusion Using commonly observed echo parameters and patient characteristics we successfully trained a ML model to predict survival in patients with severe AR. This technique could identify high-risk patients who would benefit from early intervention, thereby improving patient outcomes. Abstract Figure.