Shimei Wen, Bin Cao, Rengyun Xiang
Dear Editor, We read with great interest the recent article by Jwan A Naser et al.1 titled ‘Impact of contemporary guideline-directed medical therapy on secondary mitral and tricuspid regurgitation in heart failure with reduced ejection fraction’ published in the European Journal of Heart Failure. The authors provide compelling real-world evidence that in patients with a recent diagnosis of HFrEF, contemporary GDMT facilitates significant regression of ≥moderate secondary mitral regurgitation (MR) and tricuspid regurgitation (TR) in the majority, while incident significant regurgitation develops in a minority during follow-up. This work underscores the dynamic nature of valvular pathology in heart failure and the central role of optimized medical therapy. We wish to extend the discussion by exploring how emerging digital health technologies, particularly artificial intelligence (AI) and digital twins, could transform the personalization and monitoring of such therapy, building upon the foundation laid by this study. The finding that the number of GDMT medications at follow-up correlated with MR/TR regression highlights the importance of sustained, optimized treatment.1 This is precisely where digital twin technology holds immense promise. A cardiovascular digital twin is a patient-specific computational model that integrates multimodal data—including imaging, hemodynamic, and biomarkers—to create a virtual replica of an individual's heart.2 Such technology could enable clinicians to simulate the longitudinal effects of different GDMT regimens on ventricular remodelling, papillary muscle geometry, and ultimately, mitral and tricuspid valve coaptation. Virtual therapy evaluation on a ‘digital twin cohort’ could help identify which patients are most likely to achieve valvular regression with medical therapy alone versus those who may require earlier consideration of device-based interventions, thereby personalizing the treatment pathway suggested by the study's subgroup analyses.