Journal and News Scan

In this multicenter retrospective study of 1,454 consecutive patients undergoing isolated coronary artery bypass grafting (CABG), the SYNTAX Score II was externally validated for predicting four-year mortality. Mortality was 8.4 percent, with tertiles of SYNTAX Score II significantly stratifying survival, although the anatomical SYNTAX Score alone was not predictive. Independent predictors of mortality included age, creatinine clearance, left ventricular ejection fraction (LVEF), and chronic obstructive pulmonary disease (COPD). Calibration analysis showed systematic overestimation of mortality, particularly at higher scores (observed/expected ratio 0.61), while discrimination was acceptable (c-statistic 0.73), comparable to European System for Cardiac Operative Risk Evaluation (EuroSCORE) II, logistic EuroSCORE, and Age, Creatinine, and Ejection Fraction (ACEF). These findings suggest that although the SYNTAX Score II provides reasonable prognostic accuracy in real-world CABG patients, its predictions should be interpreted with caution, especially at higher values, due to its improved surgical outcomes since its development. Simpler scores, such as ACEF, may offer equivalent predictive value while being easier to use. Additionally, newer recalibrated models like SYNTAX Score II 2020 may better reflect contemporary practices. 

Robotic-assisted thoracoscopic surgery provides significant advantages for complex pulmonary resections such as high-definition 3D visualization and enhanced precision, resulting in reduced morbidity and quicker recovery compared to open thoracotomy. Although sublobar resections such as segmentectomies are technically challenging due to anatomical variations, the introduction of 3D reconstruction imaging facilitates tailored surgical planning. This video tutorial showcases a robotic left segmentectomy for a metastatic pulmonary lesion, highlighting how 3D imaging aids in surgical accuracy and decision-making.  

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.