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The heart team (HT) approach, recommended for managing cardiovascular diseases, emphasizes multidisciplinary collaboration. Despite its potential benefits, evidence on its effectiveness and implementation is varied and sparse. This review assesses the HT approach's impact on patient outcomes and care delivery in cardiovascular care. A systematic review was conducted across MEDLINE, EMBASE, PubMed, Cochrane and Google Scholar up to July 2023, focusing on studies that implemented an HT approach in coronary and heart valve disease management. Exclusion criteria included non-human studies, case reports and studies not focusing on HT outcomes. From 6270 identified articles, 20 met the inclusion criteria. These studies demonstrated significant variability in HT composition and organization, coupled with a lack of standardized metrics for evaluating clinical outcomes and the impact of the HT. Significant variability was observed in HT composition, with 13 of the 20 studies did not utilize structured templates, those that did demonstrated more consistent decision-making. In mitral valve interventions, HTs were linked to reduced in-hospital mortality and improved long-term survival (5-year survival probability of 0.74 vs 0.70, P  = 0.04). In aortic valve interventions, 80% of patients underwent tailored valve procedures following HT evaluation. The HT approach in cardiovascular care demonstrates improved patient outcomes, particularly in specialized interventions for mitral and aortic valve diseases and coronary artery disease management. Despite these positive findings, the variability in HT implementation and the need for standardized outcome metrics call for further advances to optimize this collaborative care model. [ABSTRACT FROM AUTHOR]

Background: Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform decision-making under uncertain conditions. The potential of machine learning is significant, and breast surgeons must strive to be informed with up-to-date knowledge and its applications. Methods: A systematic database search of Embase, MEDLINE, the Cochrane database, and Google Scholar, from inception to December 2021, was conducted of original articles that explored the use of machine learning and/or artificial intelligence in breast surgery in EMBASE, MEDLINE, Cochrane database and Google Scholar. Results: The search yielded 477 articles, of which 14 studies were included in this review, featuring 73 847 patients. Four main areas of machine learning application were identified: predictive modelling of surgical outcomes; breast imaging-based context; screening and triaging of patients with breast cancer; and as network utility for detection. There is evident value of machine learning in preoperative planning and in providing information for surgery both in a cancer and an aesthetic context. Machine learning outperformed traditional statistical modelling in all studies for predicting mortality, morbidity, and quality of life outcomes. Machine learning patterns and associations could support planning, anatomical visualization, and surgical navigation. Conclusion: Machine learning demonstrated promising applications for improving breast surgery outcomes and patient-centred care. Neveretheless, there remain important limitations and ethical concerns relating to implementing artificial intelligence into everyday surgical practices. [ABSTRACT FROM AUTHOR]

Open in new tab Download slide Open in new tab Download slide OBJECTIVES Mitral and tricuspid ring annuloplasty dehiscence with consequent recurrent valve regurgitation is a rare but challenging procedural failure. The incidence and predisposing risk factors for annuloplasty ring dehiscence include technical and pathological ones. METHODS A systematic database search with pooled analysis was conducted of original articles that only included dehiscence rate of mitral and tricuspid ring in EMBASE, MEDLINE, Cochrane database and Google Scholar, from inception to November 2020. The outcomes included were dehiscence rate in mitral and tricuspid, type of ring implanted, dehiscence rate by pathology and by ring size and shape. RESULTS Our search yielded 821 relevant studies. Thirty-three studies met the inclusion criteria with a total of 10 340 patients (6543 mitral, 1414 tricuspid) of which 87 (mitral) and 30 (tricuspid) had dehiscence. Overall, dehiscence rate was 1.43%, diagnosed at a median of 4.5 ± 1.0 months postoperatively. A significant difference in mitral dehiscence rate was found by ring type (semi-rigid 1.86%, rigid 2.32%; flexible 0.43%; P  < 0.001). There was no significant difference in rate of dehiscence by ring size (P  = 0.067) and shape in mitral (P  = 0.281) but there was higher dehiscence rate in ischaemic compared to non-ischaemic mitral regurgitation (3.91% vs 1.63%; P  = 0.022). Among tricuspid studies, 9 of 10 studies did not report any dehiscence. CONCLUSIONS Although rigid, semi-rigid and flexible annuloplasty rings provide acceptable valve repair outcomes, mitral annuloplasty ring dehiscence is clinically more common among rigid rings. Understanding the multifactorial nature of ring dehiscence will help in identifying the patients at high risk and improve their clinical outcomes. [ABSTRACT FROM AUTHOR]