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

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.

Artificial intelligence-enabled assessment of right ventricular to pulmonary artery coupling in patients undergoing transcatheter tricuspid valve intervention

AIMS

Right ventricular to pulmonary artery (RV-PA) coupling has been established as a prognostic marker in patients with severe tricuspid regurgitation (TR) undergoing transcatheter tricuspid valve interventions (TTVI). RV-PA coupling assesses right ventricular systolic function related to pulmonary artery pressure levels, which are ideally measured by right heart catheterization. This study aimed to improve the RV-PA coupling concept by relating tricuspid annular plane systolic excursion (TAPSE) to mean pulmonary artery pressure (mPAP) levels. Moreover, instead of right heart catheterization, this study sought to employ an extreme gradient boosting (XGB) algorithm to predict mPAP levels based on standard echocardiographic parameters.

METHODS AND RESULTS

This multicentre study included 737 patients undergoing TTVI for severe TR; among them, 55 patients from one institution served for external validation. Complete echocardiography and right heart catheterization data were available from all patients. The XGB algorithm trained on 10 echocardiographic parameters could reliably predict mPAP levels as evaluated on right heart catheterization data from external validation (Pearson correlation coefficient R: 0.68; P value: 1.3 × 10-8). Moreover, predicted mPAP (mPAPpredicted) levels were superior to echocardiographic systolic pulmonary artery pressure (sPAPechocardiography) levels in predicting 2-year mortality after TTVI [area under the curve (AUC): 0.607 vs. 0.520; P value: 1.9 × 10-6]. Furthermore, TAPSE/mPAPpredicted was superior to TAPSE/sPAPechocardiography in predicting 2-year mortality after TTVI (AUC: 0.633 vs. 0.586; P value: 0.008). Finally, patients with preserved RV-PA coupling (defined as TAPSE/mPAPpredicted > 0.617 mm/mmHg) showed significantly higher 2-year survival rates after TTVI than patients with reduced RV-PA coupling (81.5% vs. 58.8%, P < 0.001). Moreover, independent association between TAPSE/mPAPpredicted levels and 2-year mortality after TTVI was confirmed by multivariate regression analysis (P value: 6.3 × 10-4).

CONCLUSION

Artificial intelligence-enabled RV-PA coupling assessment can refine risk stratification prior to TTVI without necessitating invasive right heart catheterization. A comparison with conservatively treated patients is mandatory to quantify the benefit of TTVI in accordance with RV-PA coupling.

Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis

The integration of artificial intelligence (AI) into clinical management of aortic stenosis (AS) has redefined our approach to the assessment and management of this heterogenous valvular heart disease (VHD). While the large-scale early detection of valvular conditions is limited by socioeconomic constraints, AI offers a cost-effective alternative solution for screening by utilizing conventional tools, including electrocardiograms and community-level auscultations, thereby facilitating early detection, prevention, and treatment of AS. Furthermore, AI sheds light on the varied nature of AS, once considered a uniform condition, allowing for more nuanced, data-driven risk assessments and treatment plans. This presents an opportunity to re-evaluate the complexity of AS and to refine treatment using data-driven risk stratification beyond traditional guidelines. AI can be used to support treatment decisions including device selection, procedural techniques, and follow-up surveillance of transcatheter aortic valve replacement (TAVR) in a reproducible manner. While recognizing notable AI achievements, it is important to remember that AI applications in AS still require collaboration with human expertise due to potential limitations such as its susceptibility to bias, and the critical nature of healthcare. This synergy underpins our optimistic view of AI's promising role in the AS clinical pathway.

Machine Learning-Based Predictive Model of Aortic Valve Replacement Modality Selection in Severe Aortic Stenosis Patients

The current recommendation for bioprosthetic valve replacement in severe aortic stenosis (AS) is either surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR). We evaluated the performance of a machine learning-based predictive model using existing periprocedural variables for valve replacement modality selection. We analyzed 415 patients in a retrospective longitudinal cohort of adult patients undergoing aortic valve replacement for aortic stenosis. A total of 72 clinical variables including demographic data, patient comorbidities, and preoperative investigation characteristics were collected on each patient. We fit models using LASSO (least absolute shrinkage and selection operator) and decision tree techniques. The accuracy of the prediction on confusion matrix was used to assess model performance. The most predictive independent variable for valve selection by LASSO regression was frailty score. Variables that predict SAVR consisted of low frailty score (value at or below 2) and complex coronary artery diseases (DVD/TVD). Variables that predicted TAVR consisted of high frailty score (at or greater than 6), history of coronary artery bypass surgery (CABG), calcified aorta, and chronic kidney disease (CKD). The LASSO-generated predictive model achieved 98% accuracy on valve replacement modality selection from testing data. The decision tree model consisted of fewer important parameters, namely frailty score, CKD, STS score, age, and history of PCI. The most predictive factor for valve replacement selection was frailty score. The predictive models using different statistical learning methods achieved an excellent concordance predictive accuracy rate of between 93% and 98%.

Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography

OBJECTIVE

Machine learning (ML) approaches have the potential to uncover regular patterns in multi-layered data. Here we applied self-organizing maps (SOMs) to detect such patterns with the aim to better predict in-stent restenosis (ISR) at surveillance angiography 6 to 8 months after percutaneous coronary intervention with stenting.

METHODS

In prospectively collected data from 10,004 patients receiving percutaneous coronary intervention (PCI) for 15,004 lesions, we applied SOMs to predict ISR angiographically 6-8 months after index procedure. SOM findings were compared with results of conventional uni- and multivariate analyses. The predictive value of both approaches was assessed after random splitting of patients into training and test sets (50:50).

RESULTS

Conventional multivariate analyses revealed 10, mostly known, predictors for restenosis after coronary stenting: balloon-to-vessel ratio, complex lesion morphology, diabetes mellitus, left main stenting, stent type (bare metal vs. first vs. second generation drug eluting stent), stent length, stenosis severity, vessel size reduction, and prior bypass surgery. The SOM approach identified all these and nine further predictors, including chronic vessel occlusion, lesion length, and prior PCI. Moreover, the SOM-based model performed well in predicting ISR (AUC under ROC: 0.728); however, there was no meaningful advantage in predicting ISR at surveillance angiography in comparison with the conventional multivariable model (0.726, p = 0.3).

CONCLUSIONS

The agnostic SOM-based approach identified-without clinical knowledge-even more contributors to restenosis risk. In fact, SOMs applied to a large prospectively sampled cohort identified several novel predictors of restenosis after PCI. However, as compared with established covariates, ML technologies did not improve identification of patients at high risk for restenosis after PCI in a clinically relevant fashion.

Epiphenomenon or Prognostically Relevant Interventional Target? A Novel Proportionality Framework for Severe Tricuspid Regurgitation

Background Tricuspid regurgitation (TR) frequently develops in patients with long-standing pulmonary hypertension, and both pathologies are associated with increased morbidity and mortality. This study aimed to improve prognostic assessment in patients with severe TR undergoing transcatheter tricuspid valve intervention (TTVI) by relating the extent of TR to pulmonary artery pressures. Methods and Results In this multicenter study, we included 533 patients undergoing TTVI for moderate-to-severe or severe TR. The proportionality framework was based on the ratio of tricuspid valve effective regurgitant orifice area to mean pulmonary artery pressure. An optimal threshold for tricuspid valve effective regurgitant orifice area/mean pulmonary artery pressure ratio was derived on 353 patients with regard to 2-year all-cause mortality and externally validated on 180 patients. Patients with a tricuspid valve effective regurgitant orifice area/mean pulmonary artery pressure ratio ≤1.25 mm²/mm Hg (defining proportionate TR) featured significantly lower 2-year survival rates after TTVI than patients with disproportionate TR (56.6% versus 69.6%; P=0.005). In contrast with patients with disproportionate TR (n=398), patients with proportionate TR (n=135) showed more pronounced mPAP levels (37.9±9.06 mm Hg versus 27.9±8.17 mm Hg; P<2.2×10^-16) and more severely impaired right ventricular function (tricuspid annular plane systolic excursion: 16.0±4.11 versus 17.0±4.64 mm; P=0.012). Moreover, tricuspid valve effective regurgitant orifice area was smaller in patients with proportionate TR when compared with disproportionate TR (0.350±0.105 cm² versus 0.770±0.432 cm²; P<2.2×10^-16). Importantly, proportionate TR remained a significant predictor for 2-year mortality after adjusting for demographic and clinical variables (hazard ratio, 1.7; P=0.006). Conclusions The proposed proportionality framework promises to improve future risk stratification and clinical decision-making by identifying patients who benefit the most from TTVI (disproportionate TR). As a next step, randomized controlled studies with a conservative treatment arm are needed to quantify the net benefit of TTVI in patients with proportionate TR.