Predicting Long-Term Mortality in TAVI Patients Using Machine Learning Techniques

Background: Whereas transcatheter aortic valve implantation (TAVI) has become the gold standard for aortic valve stenosis treatment in high-risk patients, it has recently been extended to include intermediate risk patients. However, the mortality rate at 5 years is still elevated. The aim of the present study was to develop a novel machine learning (ML) approach able to identify the best predictors of 5-year mortality after TAVI among several clinical and echocardiographic variables, which may improve the long-term prognosis. Methods: We retrospectively enrolled 471 patients undergoing TAVI. More than 80 pre-TAVI variables were collected and analyzed through different feature selection processes, which allowed for the identification of several variables with the highest predictive value of mortality. Different ML models were compared. Results: Multilayer perceptron resulted in the best performance in predicting mortality at 5 years after TAVI, with an area under the curve, positive predictive value, and sensitivity of 0.79, 0.73, and 0.71, respectively. Conclusions: We presented an ML approach for the assessment of risk factors for long-term mortality after TAVI to improve clinical prognosis. Fourteen potential predictors were identified with the organic mitral regurgitation (myxomatous or calcific degeneration of the leaflets and/or annulus) which showed the highest impact on 5 years mortality

Predictive Value of Pre-Operative 2D and 3D Transthoracic Echocardiography in Patients Undergoing Mitral Valve Repair: Long Term Follow Up of Mitral Valve Regurgitation Recurrence and Heart Chamber Remodeling

: The "ideal" management of asymptomatic severe mitral regurgitation (MR) in valve prolapse (MVP) is still debated. The aims of this study were to identify pre-operatory parameters predictive of residual MR and of early and long-term favorable remodeling after MVP repair. We included 295 patients who underwent MV repair for MVP with pre-operatory two- and three-dimensional transthoracic echocardiography (2DTTE and 3DTTE) and 6-months (6M) and 3-years (3Y) follow-up 2DTTE. MVP was classified by 3DTTE as simple or complex and surgical procedures as simple or complex. Pre-operative echo parameters were compared to post-operative values at 6M and 3Y. Patients were divided into Group 1 (6M-MR < 2) and Group 2 (6M-MR ≥ 2), and predictors of MR 2 were investigated. MVP was simple in 178/295 pts, and 94% underwent simple procedures, while in only 42/117 (36%) of complex MVP a simple procedure was performed. A significant relation among prolapse anatomy, surgical procedures and residual MR was found. Post-operative MR ≥ 2 was present in 9.8%: complex MVP undergoing complex procedures had twice the percentage of MR ≥ 2 vs. simple MVP and simple procedures. MVP complexity resulted independent predictor of 6M-MR ≥ 2. Favorable cardiac remodeling, initially found in all cases, was maintained only in MR < 2 at 3Y. Pre-operative 3DTTE MVP morphology identifies pts undergoing simple or complex procedures predicting MR recurrence and favorable cardiac remodeling

3D right ventricular endocardium segmentation in cardiac magnetic resonance images by using a new inter-modality statistical shape modelling method

Objective Statistical shape modelling (SSM) has established as a powerful method for segmenting the left ventricle in cardiac magnetic resonance (CMR) images However, applying them to segment the right ventricle (RV) is not straightforward because of the complex structure of this chamber. Our aim was to develop a new inter-modality SSM-based approach to detect the RV endocardium in CMR data. Methods Real-time transthoracic 3D echocardiographic (3DE) images of 219 retrospective patients were used to populate a large database containing 4347 3D RV surfaces and train a model. The initial position, orientation and scale of the model in the CMR stack were semi-automatically derived. The detection process consisted in iteratively deforming the model to match endocardial borders in each CMR plane until convergence was reached. Clinical values obtained with the presented SSM method were compared with gold-standard (GS) corresponding parameters. Results CMR images of 50 patients with different pathologies were used to test the proposed segmentation method. Average processing time was 2 min (including manual initialization) per patient. High correlations (r2 > 0.76) and not significant bias (Bland-Altman analysis) were observed when evaluating clinical parameters. Quantitative analysis showed high values of Dice coefficient (0.87 ± 0.03), acceptable Hausdorff distance (9.35 ± 1.51 mm) and small point-to-surface distance (1.91 ± 0.26 mm). Conclusion A novel SSM-based approach to segment the RV endocardium in CMR scans by using a model trained on 3DE-derived RV endocardial surfaces, was proposed. This inter-modality technique proved to be rapid when segmenting the RV endocardium with an accurate anatomical delineation, in particular in apical and basal regions

Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation

Mitral valve prolapse (MVP) associated with severe mitral regurgitation is a debilitating disease with no pharmacological therapies available. MicroRNAs (miRNA) represent an emerging class of circulating biomarkers that have never been evaluated in MVP human plasma. Our aim was to identify a possible miRNA signature that is able to discriminate MVP patients from healthy subjects (CTRL) and to shed light on the putative altered molecular pathways in MVP. We evaluated a plasma miRNA profile using Human MicroRNA Card A followed by real-time PCR validations. In addition, to assess the discriminative power of selected miRNAs, we implemented a machine learning analysis. MiRNA profiling and validations revealed that miR-140-3p, 150-5p, 210-3p, 451a, and 487a-3p were significantly upregulated in MVP, while miR-223-3p, 323a-3p, 340-5p, and 361-5p were significantly downregulated in MVP compared to CTRL (p ≤ 0.01). Functional analysis identified several biological processes possible linked to MVP. In addition, machine learning analysis correctly classified MVP patients from CTRL with high accuracy (0.93) and an area under the receiving operator characteristic curve (AUC) of 0.97. To the best of our knowledge, this is the first study performed on human plasma, showing a strong association between miRNAs and MVP. Thus, a circulating molecular signature could be used as a first-line, fast, and cheap screening tool for MVP identification

Dataset related to the article "Outcomes of Transcatheter Aortic Valve Replacement Patients With Different Transvalvular Flow-gradient Patterns"

<p>This record contains raw data related to the article "Outcomes of Transcatheter Aortic Valve Replacement Patients With Different Transvalvular Flow-gradient Patterns".</p><p>Low-flow low-gradient (LF-LG) aortic stenosis (AS) may occur with preserved or depressed left ventricular ejection fraction (LVEF). Both situations represent the most challenging subset of patients to manage and generally have a poor prognosis. Few and controversial data exist on the outcomes of these patients compared to normal-flow high-gradient (NF-HG) AS following transcatheter aortic valve replacement (TAVR). We sought to characterize different transvalvular flow-gradient patterns and to examine their prognostic value after TAVR. We enrolled 1208 patients with severe AS and categorized as follow: 976 patients NF-HG (mean aortic pressure gradient, MPG≥40mmHg), 107 paradoxical cLF-LG (pLF-LG, MPG<40mmHg, LVEF≥50%, stroke volume index, SVi<35mL/m2), and 125 cLF-LG (MPG<40mmHg, LVEF<50%, SVi<35mL/m2). When compared with NF-HG and pLF-LG, cLF-LG had a worse symptomatic status (NYHA III-IV 86% vs 62% and 67%, p<0.001), a higher prevalence of eccentric hypertrophy and a higher level of LV global afterload reflected by a higher valvuloarterial impedance. Valvular function after TAVR was excellent over time in all patients. While 30-day mortality (p=0.911) did not differ significantly among groups, cLF-LG had a lower 5-year survival rate (LF-LG 50% vs pLF-LG 62% and NF-HG 68%, p<0.05). cLF-LG was associated with a hazard ratio for mortality of 2.41 (95% CI: 1.65-3.52, p<0.001). In conclusion, TAVR is an effective procedure regardless of transvalvular flow-gradient patterns. However, special care should be given to characterized hemodynamic of AS, as patients with pLF-LG had similar survival rates than patients with NF-HG, whereas cLF-LG is associated with a 2-fold increased risk of mortality at 5-year follow-up.</p&gt

REFERENCE VALUES FOR RIGHT VENTRICULAR GEOMETRY AND FUNCTION BY 3D ECHOCARDIOGRAPHY: A MULTICENTER STUDY OF 533 HEALTHY SUBJECTS

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Age-, body size-, and sex-specific reference values for right ventricular volumes and ejection fraction by three-dimensional echocardiography: A multicenter echocardiographic study in 507 healthy volunteers

none10nononeMaffessanti, Francesco; Muraru, Denisa; Esposito, Roberta; Gripari, Paola; Ermacora, Davide; Santoro, Ciro; Tamborini, Gloria; Galderisi, Maurizio; Pepi, Mauro; Badano, LuigiMaffessanti, Francesco; Muraru, Denisa; Esposito, Roberta; Gripari, Paola; Ermacora, Davide; Santoro, Ciro; Tamborini, Gloria; Galderisi, Maurizio; Pepi, Mauro; Badano, Luig

Novelties in 3D Transthoracic Echocardiography

Cardiovascular imaging is developing at a rapid pace and the newer modalities, in particular three-dimensional echocardiography, allow better analysis of heart structures. Identifying valve lesions and grading their severity represents crucial information and nowadays is strengthened by the introduction of new software, such as transillumination, which provide detailed morphology descriptions. Chambers quantification has never been so rapid and accurate: machine learning algorithms generate automated volume measurements, including left ventricular systolic and diastolic function, which is extremely important for clinical decisions. This review provides an overview of the latest innovations in the echocardiography field, and is helpful by providing a better insight into heart diseases