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

The future of valvular heart disease assessment and therapy

Valvular heart disease (VHD) is becoming more prevalent in an ageing population, leading to challenges in diagnosis and management. This two-part Series offers a comprehensive review of changing concepts in VHD, covering diagnosis, intervention timing, novel management strategies, and the current state of research. The first paper highlights the remarkable progress made in imaging and transcatheter techniques, effectively addressing the treatment paradox wherein populations at the highest risk of VHD often receive the least treatment. These advances have attracted the attention of clinicians, researchers, engineers, device manufacturers, and investors, leading to the exploration and proposal of treatment approaches grounded in pathophysiology and multidisciplinary strategies for VHD management. This Series paper focuses on innovations involving computational, pharmacological, and bioengineering approaches that are transforming the diagnosis and management of patients with VHD. Artificial intelligence and digital methods are enhancing screening, diagnosis, and planning procedures, and the integration of imaging and clinical data is improving the classification of VHD severity. The emergence of artificial intelligence techniques, including so-called digital twins-eg, computer-generated replicas of the heart-is aiding the development of new strategies for enhanced risk stratification, prognostication, and individualised therapeutic targeting. Various new molecular targets and novel pharmacological strategies are being developed, including multiomics-ie, analytical methods used to integrate complex biological big data to find novel pathways to halt the progression of VHD. In addition, efforts have been undertaken to engineer heart valve tissue and provide a living valve conduit capable of growth and biological integration. Overall, these advances emphasise the importance of early detection, personalised management, and cutting-edge interventions to optimise outcomes amid the evolving landscape of VHD. Although several challenges must be overcome, these breakthroughs represent opportunities to advance patient-centred investigations.

A complexity evaluation system for mitral valve repair based on preoperative echocardiographic and machine learning

BACKGROUND

To develop a novel complexity evaluation system for mitral valve repair based on preoperative echocardiographic data and multiple machine learning algorithms.

METHODS

From March 2021 to March 2023, 231 consecutive patients underwent mitral valve repair. Clinical and echocardiographic data were included in the analysis. The end points included immediate mitral valve repair failure (mitral replacement secondary to mitral repair failure) and recurrence regurgitation (moderate or greater mitral regurgitation [MR] before discharge). Various machine learning algorithms were used to establish the complexity evaluation system.

RESULTS

A total of 231 patients were included in this study; the median ejection fraction was 66% (63-70%), and 159 (68.8%) patients were men. Mitral repair was successful in 90.9% (210 of 231) of patients. The linear support vector classification model has the best prediction results in training and test cohorts and the variables of age, A2 lesions, leaflet height, MR grades, and so on were risk factors for failure of mitral valve repair.

CONCLUSION

The linear support vector classification prediction model may allow the evaluation of the complexity of mitral valve repair. Age, A2 lesions, leaflet height, MR grades, and so on may be associated with mitral repair failure.

Mitral valve repair for degenerative mitral regurgitation with Carpentier's functional classification type II in elderly patients: a single center experience

OBJECTIVE

Mitral valve (MV) repair for Carpentier functional classification Type II (C-II) mitral regurgitation (MR) is widely accepted because of its efficacy. It is unclear whether MV repair has the same benefits in elderly patients as in younger patients because of their lower life expectancy. Herein, we examined the midterm results of MV repair for C-II mitral regurgitation, especially in patients aged ≧70 years.

METHOD

A retrospective review was performed on 176 patients who underwent MV repair for C-II mitral regurgitation with a median age of 65 years; 55 (31%) patients were ≧70 years, and 124 were male (71%). Lesions of the mitral valve were isolated from the anterior leaflet (48 patients), posterior leaflet (113 patients), and both leaflets (15 patients), and included seven patients with Barlow's disease. We compared the outcomes between patients aged ≧70 years (≧70 years; median age, 76 years) and those aged < 70 years (median age, 60 years).

RESULTS

In terms of the durability of MV repair in elderly patients, there were no significant differences in the rates of freedom from reoperation or MR recurrence at 5 years between patients aged < 70 years and those aged ≧70 years (reoperation:98% in < 70 years versus 89% in ≧70 years; P = 0.4053; MR recurrence:95% in < 70 years versus 81% in ≧70 years; P = 0.095). The mitral valve complexity was divided into two grades: Simple (isolated posterior mitral lesion) and Complex (isolated anterior lesion or both lesions). In patients aged < 70 years, there was no significant difference in the rate of freedom from MR recurrence at 5 years between the Simple and Complex groups (96% vs. 91%; P = 0.1029). In contrast, in patients aged ≧70 years, the MR recurrence rate at 3 years in Complex was significantly higher in the Complex group than in the Simple (100% vs. 80%; P = 0.0265).

CONCLUSIONS

We studied the outcomes of MV repair for C-II in MR. In elderly patients, MR recurrence was higher in complex lesions than in simple lesions. MV replacement may be considered for elderly patients with complex mitral valve lesions, if appropriately selected.

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.

Application of AI in cardiovascular multimodality imaging

Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.

Multimodality Imaging of Sudden Cardiac Death and Acute Complications in Acute Coronary Syndrome

Sudden cardiac death (SCD) is a potentially fatal event usually caused by a cardiac arrhythmia, which is often the result of coronary artery disease (CAD). Up to 80% of patients suffering from SCD have concomitant CAD. Arrhythmic complications may occur in patients with acute coronary syndrome (ACS) before admission, during revascularization procedures, and in hospital intensive care monitoring. In addition, about 20% of patients who survive cardiac arrest develop a transmural myocardial infarction (MI). Prevention of ACS can be evaluated in selected patients using cardiac computed tomography angiography (CCTA), while diagnosis can be depicted using electrocardiography (ECG), and complications can be evaluated with cardiac magnetic resonance (CMR) and echocardiography. CCTA can evaluate plaque, burden of disease, stenosis, and adverse plaque characteristics, in patients with chest pain. ECG and echocardiography are the first-line tests for ACS and are affordable and useful for diagnosis. CMR can evaluate function and the presence of complications after ACS, such as development of ventricular thrombus and presence of myocardial tissue characterization abnormalities that can be the substrate of ventricular arrhythmias.