DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR.

BACKGROUND AND AIMS: This study investigated the additional prognostic value of epicardial adipose tissue (EAT) volume for major adverse cardiovascular events (MACE) in patients undergoing stress cardiac magnetic resonance (CMR) imaging. METHODS: 730 consecutive patients [mean age: 63 ± 10 years; 616 men] who underwent stress CMR for known or suspected coronary artery disease were randomly divided into derivation (n = 365) and validation (n = 365) cohorts. MACE was defined as non-fatal myocardial infarction and cardiac deaths. A deep learning algorithm was developed and trained to quantify EAT volume from CMR. EAT volume was adjusted for height (EAT volume index). A composite CMR-based risk score by Cox analysis of the risk of MACE was created. RESULTS: In the derivation cohort, 32 patients (8.7 %) developed MACE during a follow-up of 2103 days. Left ventricular ejection fraction (LVEF) < 35 % (HR 4.407 [95 % CI 1.903-10.202]; p<0.001), stress perfusion defect (HR 3.550 [95 % CI 1.765-7.138]; p<0.001), late gadolinium enhancement (LGE) (HR 4.428 [95%CI 1.822-10.759]; p = 0.001) and EAT volume index (HR 1.082 [95 % CI 1.045-1.120]; p<0.001) were independent predictors of MACE. In a multivariate Cox regression analysis, adding EAT volume index to a composite risk score including LVEF, stress perfusion defect and LGE provided additional value in MACE prediction, with a net reclassification improvement of 0.683 (95%CI, 0.336-1.03; p<0.001). The combined evaluation of risk score and EAT volume index showed a higher Harrel C statistic as compared to risk score (0.85 vs. 0.76; p<0.001) and EAT volume index alone (0.85 vs.0.74; p<0.001). These findings were confirmed in the validation cohort. CONCLUSIONS: In patients with clinically indicated stress CMR, fully automated EAT volume measured by deep learning can provide additional prognostic information on top of standard clinical and imaging parameters.

Circulating Small Extracellular Vesicles Reflect the Severity of Myocardial Damage in STEMI Patients.

Circulating small extracellular vesicles (sEVs) contribute to inflammation, coagulation and vascular injury, and have great potential as diagnostic markers of disease. The ability of sEVs to reflect myocardial damage assessed by Cardiac Magnetic Resonance (CMR) in ST-segment elevation myocardial infarction (STEMI) is unknown. To fill this gap, plasma sEVs were isolated from 42 STEMI patients treated by primary percutaneous coronary intervention (pPCI) and evaluated by CMR between days 3 and 6. Nanoparticle tracking analysis showed that sEVs were greater in patients with anterior STEMI (p = 0.0001), with the culprit lesion located in LAD (p = 0.045), and in those who underwent late revascularization (p = 0.038). A smaller sEV size was observed in patients with a low myocardial salvage index (MSI, p = 0.014). Patients with microvascular obstruction (MVO) had smaller sEVs (p < 0.002) and lower expression of the platelet marker CD41-CD61 (p = 0.039). sEV size and CD41-CD61 expression were independent predictors of MVO/MSI (OR [95% CI]: 0.93 [0.87-0.98] and 0.04 [0-0.61], respectively). In conclusion, we provide evidence that the CD41-CD61 expression in sEVs reflects the CMR-assessed ischemic damage after STEMI. This finding paves the way for the development of a new strategy for the timely identification of high-risk patients and their treatment optimization.

Outcomes of Transcatheter Aortic Valve Replacement Patients With Different Transvalvular Flow-Gradient Patterns.

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 with normal flow-high gradient (NF-HG) AS after transcatheter aortic valve replacement (TAVR). We sought to characterize different transvalvular flow-gradient patterns and to examine their prognostic value after TAVR. We enrolled 1,208 patients with severe AS and categorized as follow: 976 patients NF-HG (mean aortic pressure gradient [MPG] ≥40 mm Hg), 107 paradoxical LF-LG (pLF-LG, MPG <40 mm Hg, LVEF ≥50%, stroke volume index <35 ml/m2), and 125 classical LF-LG (cLF-LG) (MPG <40 mm Hg, LVEF <50%, stroke volume index <35 ml/m2). When compared with NF-HG and pLF-LG, cLF-LG had a worse symptomatic status (New York Heart Association III to 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% confidence interval 1.65 to 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 twofold increased risk of mortality at 5-year follow-up.

Early Biological Valve Failure: Structural Valve Degeneration, Thrombosis, or Endocarditis?

Biological valve failure (BVF) is an inevitable condition that compromises the durability of biological heart valves (BHVs). It stems from various causes, including rejection, thrombosis, and endocarditis, leading to a critical state of valve dysfunction. Echocardiography, cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging play pivotal roles in the diagnostic multimodality workup of BVF. By providing a comprehensive overview of the pathophysiology of BVF and the diagnostic approaches in different clinical scenarios, this review aims to aid clinicians in their decision-making process. The significance of early detection and appropriate management of BVF cannot be overstated, as these directly impact patients' prognosis and their overall quality of life. Ensuring timely intervention and tailored treatments will not only improve outcomes but also alleviate the burden of this condition on patients' life. By prioritizing comprehensive assessments and adopting the latest advancements in diagnostic technology, medical professionals can significantly enhance their ability to manage BVF effectively.

The prognostic role of right ventricular dysfunction in patients with hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) primarily affects the left ventricle (LV) sparing the right ventricle (RV) in vast majority of cases. However, several studies employing CMR have revealed that myocardial hypertrophy may also involve the RV. To assess RV size and function in a large prospectively cohort of HCM patients and to evaluate whether these parameters in association with other MR findings can predict cardiac events. Two participating centers prospectively included patients with known or suspected HCM between 2011 and 2017. CMR studies were performed with three different scanners. Outcome measures were a composite of ventricular arrhythmias, hospitalization for HF and cardiac death. Of 607 consecutive patients with known or suspected HCM, 315 had complete follow-up information (mean 65 ± 20 months). Among them, 115 patients developed major cardiac events (MACE) during follow-up. At CMR evaluation, patients with events had higher left atrium (LA) diameter (41.5 ± 8 mm vs. 37.17 ± 7.6 mm, p < 0.0001), LV mass (156.7 vs. 144 g, p = 0.005) and myocardial LGE (4.3% vs. 1.9%, p = 0.001). Similarly, patients with events had lower RV stroke volume index (42.7 vs. 47.0, p = 0.0003) and higher prevalence of both RV hypertrophy (16.4% vs. 4.7%, p = 0.0005) and reduced RV ejection fraction (12.2% vs. 4.4%, p = 0.006). In the multivariate analysis, LA diameter and RV stroke volume index were the strongest predictors of events (p < 0.001 and p = 0.0006, respectively). Anatomic and functional RV anomalies detected and characterized with CMR may have may have a major role in predicting the prognosis of HCM patients.

The Role of Multimodality Imaging in Left-Sided Prosthetic Valve Dysfunction.

Prosthetic valve (PV) dysfunction (PVD) is a complication of mechanical or biological PV. Etiologic mechanisms associated with PVD include fibrotic pannus ingrowth, thrombosis, structural valve degeneration, and endocarditis resulting in different grades of obstruction and/or regurgitation. PVD can be life threatening and often challenging to diagnose due to the similarities between the clinical presentations of different causes. Nevertheless, identifying the cause of PVD is critical to treatment administration (thrombolysis, surgery, or percutaneous procedure). In this report, we review the role of multimodality imaging in the diagnosis of PVD. Specifically, this review discusses the characteristics of advanced imaging modalities underlying the importance of an integrated approach including 2D/3D transthoracic and transesophageal echocardiography, fluoroscopy, and computed tomography. In this scenario, it is critical to understand the strengths and weaknesses of each modality according to the suspected cause of PVD. In conclusion, for patients with suspected or known PVD, this stepwise imaging approach may lead to a simplified, more rapid, accurate and specific workflow and management.

Cardiac tumors: imaging findings, clinical correlations and surgical treatment in a 15 years single-center experience.

BACKGROUND: The aims of this study were: to present the clinical and pathological characteristics of cardiac tumors in a single-center series of patients; to describe the association of imaging characteristics, clinical presentation and surgical treatment; to analyze if second level imaging tests, computed tomography (CT) and cardiac magnetic resonance (CMR); and to improve the diagnostic accuracy when compared to first-line imaging technique (transthoracic echocardiography [TTE]). METHODS: We reviewed the medical and surgical records, TTE, CT and CMR examinations of 86 patients with a histological diagnosis of cardiac tumors between 2004 and 2019. RESULTS: The majority were benign tumors (81%) with myxoma accounting for 66% of cases. Among malignancies, metastasis (8%) and primary tumors (10%) were equally recognized. Symptoms at presentation (45% of patients) were associated to larger diameters at TTE. Malignancies were larger (mean diameter 37±14 mm vs. 27±13 mm, P<0.01), more frequently exhibited irregular shape (67% vs. 17%, P<0.01), frayed or polylobulated surface (73% vs. 38%, P=0.035), heterogeneous aspect (67% vs. 32%, P=0.012). A maximum diameter >28 mm and a minimum diameter >19.5 mm emerged as possible cut-off values for the differentiation of benign and malignant tumors. The ability of TTE, CT and CMR features in identifying malignancies was moderate (diagnostic accuracy of 84%, 81%, 76% respectively). The mean survival time after surgery was 1.6±1.4 years in malignancies and 6.8±4.7 years in benign tumors. CONCLUSIONS: Cardiac tumors are rare and mostly benign; their nature and clinics related to TTE appearance. CT and CMR may be used synergically with TTE. Surgery is curative in benign tumors, survival remains scarce in malignancies.

3D echocardiography in mitral valve prolapse.

Mitral valve prolapse (MVP) is the leading cause of mitral valve surgery. Echocardiography is the principal imaging modality used to diagnose MVP, assess the mitral valve morphology and mitral annulus dynamics, and quantify mitral regurgitation. Three-dimensional (3D) echocardiographic (3DE) imaging represents a consistent innovation in cardiovascular ultrasound in the last decades, and it has been implemented in routine clinical practice for the evaluation of mitral valve diseases. The focus of this review is the role and the advantages of 3DE in the comprehensive evaluation of MVP, intraoperative and intraprocedural monitoring.

Feasibility and Accuracy of the Automated Software for Dynamic Quantification of Left Ventricular and Atrial Volumes and Function in a Large Unselected Population.

We aimed to evaluate the feasibility and accuracy of machine learning-based automated dynamic quantification of left ventricular (LV) and left atrial (LA) volumes in an unselected population. We enrolled 600 unselected patients (12% in atrial fibrillation) clinically referred for transthoracic echocardiography (2DTTE), who also underwent 3D echocardiography (3DE) imaging. LV ejection fraction (EF), LV, and LA volumes were obtained from 2D images; 3D images were analyzed using dynamic heart model (DHM) software (Philips) resulting in LV and LA volume-time curves. A subgroup of 140 patients also underwent cardiac magnetic resonance (CMR) imaging. Average time of analysis, feasibility, and image quality were recorded, and results were compared between 2DTTE, DHM, and CMR. The use of DHM was feasible in 522/600 cases (87%). When feasible, the boundary position was considered accurate in 335/522 patients (64%), while major (n = 38) or minor (n = 149) border corrections were needed. The overall time required for DHM datasets was approximately 40 seconds. As expected, DHM LV volumes were larger than 2D ones (end-diastolic volume: 173 ± 64 vs. 142 ± 58 mL, respectively), while no differences were found for LV EF and LA volumes (EF: 55% ± 12 vs. 56% ± 14; LA volume 89 ± 36 vs. 89 ± 38 mL, respectively). The comparison between DHM and CMR values showed a high correlation for LV volumes (r = 0.70 and r = 0.82, p < 0.001 for end-diastolic and end-systolic volume, respectively) and an excellent correlation for EF (r = 0.82, p < 0.001) and LA volumes. The DHM software is feasible, accurate, and quick in a large series of unselected patients, including those with suboptimal 2D images or in atrial fibrillation.

Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm.

BACKGROUND: Despite the low spatial resolution of 2D-multisegment late gadolinium enhancement (2D-MSLGE) sequences, it may be useful in uncooperative patients instead of standard 2D single segmented inversion recovery gradient echo late gadolinium enhancement sequences (2D-SSLGE). The aim of the study is to assess the feasibility and comparison of 2D-MSLGE reconstructed with artificial intelligence reconstruction deep learning noise reduction (NR) algorithm compared to standard 2D-SSLGE in consecutive patients with ischemic cardiomyopathy (ICM). METHODS: Fifty-seven patients with known ICM referred for a clinically indicated CMR were enrolled in this study. 2D-MSLGE were reconstructed using a growing level of NR (0%,25%,50%,75%and 100%). Subjective image quality, signal to noise ratio (SNR) and contrast to noise ratio (CNR) were evaluated in each dataset and compared to standard 2D-SSLGE. Moreover, diagnostic accuracy, LGE mass and scan time were compared between 2D-MSLGE with NR and 2D-SSLGE. RESULTS: The application of NR reconstruction ≥50% to 2D-MSLGE provided better subjective image quality, CNR and SNR compared to 2D-SSLGE (p < 0.01). The best compromise in terms of subjective and objective image quality was observed for values of 2D-MSLGE 75%, while no differences were found in terms of LGE quantification between 2D-MSLGE versus 2D-SSLGE, regardless the NR applied. The sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 2D-MSLGE NR 75% were 87.77%,96.27%,96.13%,88.16% and 94.22%, respectively. Time of acquisition of 2D-MSLGE was significantly shorter compared to 2D-SSLGE (p < 0.01). CONCLUSION: When compared to standard 2D-SSLGE, the application of NR reconstruction to 2D-MSLGE provides superior image quality with similar diagnostic accuracy.

Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair.

BACKGROUND: Mitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence. METHODS: 1000 patients who underwent MV repair at our institution between 2008 and 2018 were enrolled. Patients were followed longitudinally for up to three years. Clinical and echocardiographic data were included in the analysis. Endpoints were MV repair surgical failure with consequent MV replacement or moderate/severe MR (>2+) recurrence at one-month and moderate/severe MR recurrence after three years. RESULTS: 817 patients (DS1) had an echocardiographic examination at one-month while 295 (DS2) also had one at three years. Data were randomly divided into training (DS1: n = 654; DS2: n = 206) and validation (DS1: n = 164; DS2 n = 89) cohorts. For intra-operative or early MV repair failure assessment, the best area under the curve (AUC) was 0.75 and the complexity of mitral valve prolapse was the main predictor. In predicting moderate/severe recurrent MR at three years, the best AUC was 0.92 and residual MR at six months was the most important predictor. CONCLUSIONS: Machine learning algorithms may improve prognosis after MV repair procedure, thus improving indications for correct candidate selection for MV surgical repair.

Head to Head Comparison between Different 3-Dimensional Echocardiographic Rendering Tools in the Imaging of Percutaneous Edge-to-Edge Mitral Valve Repair.

MitraClip (MC) is the most common percutaneous treatment for severe mitral regurgitation (MR). An accurate two-dimensional and three-dimensional echocardiographic (3DTEE) imaging is mandatory for the optimal procedural result. Recently transillumination 3DTEE rendering (3DTr) has been introduced integrating a virtual light source into the dataset and with the addition of glass effect (3DGl) allows to adjust tissue transparency improving depth perception and anatomical structure delineation in comparison with the standard 3DTEE (3DSt). The aim of this retrospective study in 30 patients undergoing MC, was to compare 3DSt, 3DTr, and 3DGl in mitral valve (MV) evaluation and procedural result assessment. 3DTEE acquisitions obtained before and after MC were processed with 3DSt, 3DTr, and 3DGl rendering. Each reconstruction was scored for quality and for ability to recognize MV anatomy, MR origin, clip position, dimension and grasping. Imaging quality was judged good or optimal in 52%, 76%, and 96% in 3DSt, 3DTr, and 3DGl reconstructions respectively. In 26/30 patients a diagnostic incremental value was found with 3DTr vs. 3DSt and in 15/26 with 3DGl vs. 3DTr and 3DSt. Only 3DGl with perpendicular cropping of the clip allowed to visualize and measure the grasped portion of each mitral leaflets. 3DTEE imaging during MC may be improved by 3DTr and 3DGl providing a better evaluation of MV, of leaflet grasping and of residual MR jets after MC.

Aortic Valve Sclerosis as an Important Predictor of Long-Term Mortality in Patients With Carotid Atheromatous Plaque Requiring Carotid Endarterectomy.

Background: A strong association between aortic valve sclerosis (AVSc), the earliest manifestation of calcific aortic valve disease, and atherosclerosis exists. The aim of the study was to evaluate the predictive capabilities of AVSc on long-term all-cause mortality, in patients requiring carotid endarterectomy (CEA). Methods and Results: 806 consecutive CEA patients were enrolled. Preoperative echocardiography was used to assess AVSc. Computed tomography angiography was applied for plaque characterization. Kaplan-Meier curves, Cox linear regression, and area under the receiving operator characteristic (AUC) curve analyses were used to evaluate the predictive capability of AVSc. Overall, 348 of 541 patients had AVSc (64%). Age, diabetes, and estimated glomerular filtration rate (eGFR) were associated with AVSc. In the 5-year follow-up, AVSc group had a mortality rate of 16.7% while in no-AVSc group was 7.8%. Independent predictors of all-cause mortality were age, sex, eGFR, left ventricular ejection fraction, and AVSc. After adjustments, AVSc was associated with a significant increase in all-cause mortality risk (hazard ratio, HR = 1.9; 95%CI: 1.04-3.54; p = 0.038). We stratify our cohort based on carotid atheromatous plaque-type: soft, calcified, and mixed-fibrotic. In patients with mixed-fibrotic plaques, the mortality rate of AVSc patients was 15.5% compared to 2.4% in no-AVSc patients. In this group, AVSc was associated with an increased long-term all-cause mortality risk with an adjusted HR of 12.8 (95%CI: 1.71-96.35; p = 0.013), and the AUC, combing eGFR and AVSc was 0.77 (p < 0.001). Conclusions: Our findings indicate that AVSc together with eGFR may be used to improve long-term risk stratification of patients undergoing CEA surgery.

Detection of Mechanical Prosthetic Valve Dysfunction.

The long-term outcome of mechanical aortic and mitral prosthetic valve (A-PV, M-PV) dysfunction (PVD) remains a serious complication associated with high morbidity and mortality. We sought to evaluate the incremental diagnostic value of combined transthoracic echocardiography (TTE) and fluoroscopy (F) in patients with suspected PVD. A total of 354 patients (178 A-PV, 176 M-PV) were imaged by TTE and F within 5 days of hospital admission. PVD was confirmed by transesophageal echocardiography, computed tomography, effective thrombolysis, or surgical inspection. PVD was confirmed in 101 patients (57%) with M-PV and 99 (55%) with A-PV. Regardless of the mechanism of PVD, TTE shows good sensitivity and specificity, with accuracy of 80% for M-PV and 91% for A-PV. F shows high specificity, but low sensitivity with accuracy of 68% for M-PV and 78% for A-PV. The integration of TTE + F significantly improved accuracy both for M-PV (83%) and A-PV (96%). At ROC analysis, the combined model of TTE + F showed the highest area under the curve for the detection of PVD compared with TTE and F alone (p < 0.001). In conclusion, in patients with a clinical suspicion of PVD, the combined model of TTE + F offers incremental value over TTE or F alone. This multimodality imaging approach overcomes limitations of TTE or F alone and provides prompt identification of patients who may require further imaging assessment and/or closer follow up.

Tricuspid annular dilation in patients undergoing early mitral valve surgery: is it an old story?

Patients with mitral valve prolapse (MVP), undergoing early surgery for severe regurgitation, are usually characterized by a low degree of right chambers' remodeling. In this selected population, the mechanisms leading to tricuspid annular (TA) dilatation (TAD) are not well understood. In this setting, we aimed to evaluate, using three-dimensional echocardiography (3DE), how right chambers affect TA size and might contribute to functional tricuspid regurgitation (FTR) progression. We studied 159 patients treated with early isolated surgery for MVP, characterized by: sinus rhythm; normal biventricular function; normal or elevated pulmonary artery pressure; tricuspid regurgitation (TR) ≤ mild; no concomitant cardiac disease. All patients reached a 3-year echocardiographic follow-up. Based on two-dimensional echocardiography, patients were divided in Group 1 (N = 68, 43%, TAD, TA ≥ 21 mm/m2) and Group 2 (N = 91, 57%, no TAD, TA < 21 mm/m2). By 3DE, Group 1 showed larger TA size, right atrial (RA) volume and right ventricular (RV) conical remodeling compared to Group 2 (p < 0.05). The multivariate analysis revealed that RA volume, RV basal diameter and function were independently correlated to TA size (p < 0.05). At the 3-year follow-up there was a low incidence of FTR, with a trend towards FTR progression in Group 1 (p = 0.07). In patients undergoing early surgery for MVP, TAD seems to result from distinctive early-onset geometrical changes of the right chambers, preceding TR, RV dilatation and pulmonary hypertension at rest. An integrated approach, including right chambers' assessment by 3DE, might help to better recognized patients at higher risk for TAD and, potentially for FTR.

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.

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.

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.