Invasive Assessment of Right Ventricular to Pulmonary Artery Coupling Improves 1-year Mortality Prediction After Transcatheter Aortic Valve Replacement and Anticipates the Persistence of Extra-Aortic Valve Cardiac Damage.

Background: The interplay between the right ventricle and the pulmonary artery, known as right ventricular to pulmonary artery (RV-PA) coupling, is crucial for assessing right ventricular systolic function against the afterload from the pulmonary circulation. Pulmonary artery pressure levels are ideally measured by right heart catheterization. Yet, echocardiography represents the most utilized method for evaluating pulmonary artery pressure levels, albeit with limitations in accuracy. This study therefore aims to evaluate the prognostic significance of right ventricular to pulmonary artery (RV-PA) coupling expressed as tricuspid annular plane systolic excursion (TAPSE) related to systolic pulmonary artery pressure (sPAP) levels measured by right heart catheterization (TAPSE/sPAPinvasive) or estimated by transthoracic echocardiography (TAPSE/sPAPechocardiography) in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement (TAVR). Methods: Using data from a bicentric registry, this study compares TAPSE/sPAPinvasive vs. TAPSE/sPAPechocardiography in predicting 1-year all-cause mortality after TAVR. Results: Among 333 patients with complete echocardiography and right heart catheterization data obtained before TAVR, their mean age was 79.8 ± 6.74 years, 39.6% were female, and general 1-year survival was 89.8%. sPAPinvasive and sPAPechocardiography showed only moderate correlation (Pearson correlation coefficient R: 0.53, p value: <0.0001). TAPSE/sPAPinvasive was superior to TAPSE/sPAPechocardiography in predicting 1-year all-cause mortality after TAVR (area under the curve: 0.662 vs. 0.569, p value: 0.025). Patients with reduced TAPSE/sPAPinvasive levels (< 0.365 mm/mmHg) evidenced significantly lower 1-year survival rates than patients with preserved TAPSE/sPAPinvasive levels (81.8 vs. 93.6%, p value: 0.001; hazard ratio for 1-year mortality: 3.09 [95% confidence interval: 1.55-6.17]). Echocardiographic follow-up data revealed that patients with reduced RV-PA coupling suffer from persistent right ventricular dysfunction (TAPSE: 16.6 ± 4.05 mm vs. 21.6 ± 4.81 mm in patients with preserved RV-PA coupling) and severe tricuspid regurgitation (diagnosed in 19.7 vs. 6.58% in patients with preserved RV-PA coupling). Conclusions: RV-PA coupling expressed as TAPSE/sPAPinvasive can refine stratification of severe aortic stenosis patients into low-risk and high-risk cohorts for mortality after TAVR. Moreover, it can help to anticipate persistent extra-aortic valve cardiac damage, which will demand further treatment.

Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair.

AIMS: Patients with mitral regurgitation (MR) present with considerable heterogeneity in cardiac damage depending on underlying aetiology, disease progression, and comorbidities. This study aims to capture their cardiopulmonary complexity by employing a machine-learning (ML)-based phenotyping approach. METHODS AND RESULTS: Data were obtained from 1426 patients undergoing mitral valve transcatheter edge-to-edge repair (MV TEER) for MR. The ML model was developed using 609 patients (derivation cohort) and validated on 817 patients from two external institutions. Phenotyping was based on echocardiographic data, and ML-derived phenotypes were correlated with 5-year outcomes. Unsupervised agglomerative clustering revealed four phenotypes among the derivation cohort: Cluster 1 showed preserved left ventricular ejection fraction (LVEF; 56.5 ± 7.79%) and regular left ventricular end-systolic diameter (LVESD; 35.2 ± 7.52 mm); 5-year survival in Cluster 1, hereinafter serving as a reference, was 60.9%. Cluster 2 presented with preserved LVEF (55.7 ± 7.82%) but showed the largest mitral valve effective regurgitant orifice area (0.623 ± 0.360 cm2) and highest systolic pulmonary artery pressures (68.4 ± 16.2 mmHg); 5-year survival ranged at 43.7% (P-value: 0.032). Cluster 3 was characterized by impaired LVEF (31.0 ± 10.4%) and enlarged LVESD (53.2 ± 10.9 mm); 5-year survival was reduced to 38.3% (P-value: <0.001). The poorest 5-year survival (23.8%; P-value: <0.001) was observed in Cluster 4 with biatrial dilatation (left atrial volume: 312 ± 113 mL; right atrial area: 46.0 ± 8.83 cm2) although LVEF was only slightly reduced (51.5 ± 11.0%). Importantly, the prognostic significance of ML-derived phenotypes was externally confirmed. CONCLUSION: ML-enabled phenotyping captures the complexity of extra-mitral valve cardiac damage, which does not necessarily occur in a sequential fashion. This novel phenotyping approach can refine risk stratification in patients undergoing MV TEER in the future.

Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement.

OBJECTIVE: A novel artificial intelligence-based phenotyping approach to stratify patients with severe aortic stenosis (AS) prior to transcatheter aortic valve replacement (TAVR) has been proposed, based on echocardiographic and haemodynamic data. This study aimed to analyse the recovery of extra-aortic valve cardiac damage in accordance with this novel stratification system following TAVR. METHODS: The proposed phenotyping approach was previously established employing data from 366 patients with severe AS from a bicentric registry. For this consecutive study, echocardiographic follow-up data, obtained on day 147±75.1 after TAVR, were available from 247 patients (67.5%). RESULTS: Correction of severe AS by TAVR significantly reduced the proportion of patients suffering from concurrent severe mitral regurgitation (from 9.29% to 3.64%, p value: 0.0015). Moreover, pulmonary artery pressures were ameliorated (estimated systolic pulmonary artery pressure: from 47.2±15.8 to 43.3±15.1 mm Hg, p value: 0.0079). However, right heart dysfunction as well as the proportion of patients with severe tricuspid regurgitation remained unchanged. Clusters with persistent right heart dysfunction ultimately displayed 2-year survival rates of 69.2% (95% CI 56.6% to 84.7%) and 74.6% (95% CI 65.9% to 84.4%), which were significantly lower compared with clusters with little or no persistent cardiopulmonary impairment (88.3% (95% CI 83.3% to 93.5%) and 85.5% (95% CI 77.1% to 94.8%)). CONCLUSIONS: This phenotyping approach preprocedurally identifies patients with severe AS, who will not recover from extra-aortic valve cardiac damage following TAVR and whose survival is therefore significantly reduced. Importantly, not the degree of pulmonary hypertension at initial presentation, but the irreversibility of right heart dysfunction determines prognosis.

Solving the Pulmonary Hypertension Paradox in Patients With Severe Tricuspid Regurgitation by Employing Artificial Intelligence.

OBJECTIVES: This study aimed to improve echocardiographic assessment of pulmonary hypertension (PH) in patients presenting with severe tricuspid regurgitation (TR). BACKGROUND: Echocardiographic assessment of PH in patients with severe TR carries several pitfalls for underestimation, hence concealing the true severity of PH in very sick patients in particular, and ultimately obscuring the impact of PH on survival after transcatheter tricuspid valve intervention (TTVI). METHODS: All patients in this study underwent TTVI for severe TR between 2016 and 2020. To predict the mean pulmonary artery pressure (mPAP) solely based on echocardiographic parameters, we trained an extreme gradient boosting (XGB) algorithm. The derivation cohort was constituted by 116 out of 162 patients with both echocardiography and right heart catheterization data, preprocedurally obtained, from a bicentric registry. Moreover, 142 patients from an independent institution served for external validation. RESULTS: Systolic pulmonary artery pressure was consistently underestimated by echocardiography in comparison to right heart catheterization (40.3 ± 15.9 mm Hg vs 44.1 ± 12.9 mm Hg; P = 0.0066), and the assessment was most discrepant among patients with severe defects of the tricuspid valve and impaired right ventricular systolic function. Using 9 echocardiographic parameters as input variables, an XGB algorithm could reliably predict mPAP levels (R = 0.96, P < 2.2 × 10-16). Moreover, patients with elevations in predicted mPAP levels ≥29.9 mm Hg showed significantly reduced 2-year survival after TTVI (58.3% [95% CI: 41.7%-81.6%] vs 78.8% [95% CI: 68.7%-90.5%]; P = 0.026). Importantly, the poor prognosis associated with elevation in predicted mPAP levels was externally confirmed (HR for 2-year mortality: 2.9 [95% CI: 1.5-5.7]; P = 0.002). CONCLUSIONS: PH in patients with severe TR can be reliably assessed based on echocardiographic parameters in conjunction with an XGB algorithm, and elevations in predicted mPAP levels translate into increased mortality after TTVI.

Harnessing feature extraction capacities from a pre-trained convolutional neural network (VGG-16) for the unsupervised distinction of aortic outflow velocity profiles in patients with severe aortic stenosis.

Aims: Hypothesizing that aortic outflow velocity profiles contain more valuable information about aortic valve obstruction and left ventricular contractility than can be captured by the human eye, features of the complex geometry of Doppler tracings from patients with severe aortic stenosis (AS) were extracted by a convolutional neural network (CNN). Methods and results: After pre-training a CNN (VGG-16) on a large data set (ImageNet data set; 14 million images belonging to 1000 classes), the convolutional part was employed to transform Doppler tracings to 1D arrays. Among 366 eligible patients [age: 79.8 ± 6.77 years; 146 (39.9%) women] with pre-procedural echocardiography and right heart catheterization prior to transcatheter aortic valve replacement (TAVR), good quality Doppler tracings from 101 patients were analysed. The convolutional part of the pre-trained VGG-16 model in conjunction with principal component analysis and k-means clustering distinguished two shapes of aortic outflow velocity profiles. Kaplan-Meier analysis revealed that mortality in patients from Cluster 2 (n = 40, 39.6%) was significantly increased [hazard ratio (HR) for 2-year mortality: 3; 95% confidence interval (CI): 1-8.9]. Apart from reduced cardiac output and mean aortic valve gradient, patients from Cluster 2 were also characterized by signs of pulmonary hypertension, impaired right ventricular function, and right atrial enlargement. After training an extreme gradient boosting algorithm on these 101 patients, validation on the remaining 265 patients confirmed that patients assigned to Cluster 2 show increased mortality (HR for 2-year mortality: 2.6; 95% CI: 1.4-5.1, P-value: 0.004). Conclusion: Transfer learning enables sophisticated pattern recognition even in clinical data sets of limited size. Importantly, it is the left ventricular compensation capacity in the face of increased afterload, and not so much the actual obstruction of the aortic valve, that determines fate after TAVR.

Subphenotyping of Patients With Aortic Stenosis by Unsupervised Agglomerative Clustering of Echocardiographic and Hemodynamic Data.

OBJECTIVES: The aim of this retrospective analysis was to categorize patients with severe aortic stenosis (AS) according to clinical presentation by applying unsupervised machine learning. BACKGROUND: Patients with severe AS present with heterogeneous clinical phenotypes, depending on disease progression and comorbidities. METHODS: Unsupervised agglomerative clustering was applied to preprocedural data from echocardiography and right heart catheterization from 366 consecutively enrolled patients undergoing transcatheter aortic valve replacement for severe AS. RESULTS: Cluster analysis revealed 4 distinct phenotypes. Patients in cluster 1 (n = 164 [44.8%]), serving as a reference, presented with regular cardiac function and without pulmonary hypertension (PH). Accordingly, estimated 2-year survival was 90.6% (95% CI: 85.8%-95.6%). Clusters 2 (n = 66 [18.0%]) and 4 (n = 91 [24.9%]) both comprised patients with postcapillary PH. Yet patients in cluster 2 with preserved left and right ventricular structure and function showed a similar survival as those in cluster 1 (2-year survival 85.8%; 95% CI: 76.9%-95.6%), whereas patients in cluster 4 with dilatation of all heart chambers and a high prevalence of mitral and tricuspid regurgitation (12.5% and 14.8%, respectively) died more often (2-year survival 74.9% [95% CI: 65.9%-85.2%]; HR for 2-year mortality: 2.8 [95% CI: 1.4-5.5]). Patients in cluster 3, the smallest (n = 45 [12.3%]), displayed the most extensive disease characteristics (ie, left and right heart dysfunction together with combined pre- and postcapillary PH), and 2-year survival was accordingly reduced (77.3% [95% CI: 65.2%-91.6%]; HR for 2-year mortality: 2.6 [95% CI: 1.1-6.2]). CONCLUSIONS: Unsupervised machine learning aids in capturing complex clinical presentations as observed in patients with severe AS. Importantly, structural alterations in left and right heart morphology, possibly due to genetic predisposition, constitute an equally sensitive indicator of poor prognosis compared with high-grade PH.