Journal and News Scan

This article explores emerging innovations aimed at addressing the critical challenges of organ transplantation, particularly organ scarcity, rejection, and the burden of lifelong immunosuppression. Key advances include artificial intelligence (AI) for optimizing organ allocation, refining rejection monitoring, and personalizing immunosuppressive therapy; xenotransplantation using multigene–edited donor pigs with improved immunosuppression to overcome hyperacute rejection; and regenerative medicine approaches such as stem cell therapies, three-dimensional organoids, and bioprinting to create patient-specific tissues that reduce the risk of rejection. Additionally, biomaterials and cell encapsulation offer targeted immunosuppression, potentially reducing systemic therapy needs. The authors emphasize that while these strategies hold transformative potential, successful translation requires rigorous clinical validation, ethical oversight, and interdisciplinary collaboration. 
 
This is highly relevant to CTSNet’s global cardiothoracic surgery audience because it highlights technologies that could redefine heart and lung transplantation, potentially mitigating donor shortages, improving graft longevity, and expanding access to life-saving therapies for patients with end-stage cardiothoracic disease. 

Surgical outcomes traditionally rely on time-to-event analysis models, such as the Cox Proportional Hazards (CPH) model, which adjusts for covariates. These models, however, have certain limitations that must be taken into account when interpreting their results. Artificial intelligence (AI) and machine learning (ML) are rapidly growing areas of medicine that can be used to model complex, multidimensional, nonlinear data and overcome some of the limitations of CPH models. One such AI model is Random Survival Forest (RSF). The authors analyzed 444 patients undergoing primary mitral valve repair for degenerative mitral regurgitation and evaluated the use of RSF versus CPH (2008–2024) for a primary outcome of mitral repair failure (MRF). The authors found that ML outperforms traditional methods and is more useful to identify clinically actionable predictors. They also discuss some of the current limitations of ML, including the lack of hazard ratios or p-values to quantify linear variable effects. As AI models continue to evolve, further integration of these models will likely be seen, especially in the study of surgical outcomes. 

As minimally invasive mitral valve surgery (MIMVS) continues to gain acceptance, concomitant tricuspid valve (TV) repair in patients with significant tricuspid regurgitation (TR) is increasingly encountered. The authors explored the current practice patterns regarding TV repair during MIMVS and found that, generally, the indications for TV repair were followed, although there were institutional variations. Key reasons for omitting TV repair included absence of severe tricuspid regurgitation (odds ratio 3.31 for moderate TR, OR 4.06 for mild TR), a lower NYHA class (OR 0.61 for NYHA III-IV), and mitral valve disease type (OR 0.38). TV repair was associated with longer ICU (48 vs 23 hours, P < 0.001) and hospital stays (11 vs 8 days, P < 0.001), but 30-day mortality was similar between groups (4.3 percent for tricuspid valve repair vs 1.8 percent for no tricuspid valve repair, P = 0.2). 

Cleveland Clinic surgeons have successfully performed the world’s first transcervical robotic aortic valve replacement (AVR) procedures in four patients, utilizing a small incision in the neck for minimally invasive access. This innovative approach allows for quicker recovery, with patients resuming normal activity within a week and experiencing significantly less pain compared to traditional surgeries. The surgeries involved robotic assistance for precision, combining the benefits of surgical AVR with reduced trauma and faster healing times.