What is the cornerstone of decision making in patients requiring myocardial revascularisation? - Personalized evidence based medicine –

This thesis aims to enhance personalized decision-making for myocardial revascularization in patients with coronary artery disease (CAD), by identifying individual treatment responses across a heterogeneous population. Part 1 evaluates diagnostic tools guiding revascularization, particularly the diagnostic performance of various angiography-derived fractional flow reserve (FFR) software in a prospective cohort. It also introduces the pull-back pressure gradient (PPG) index as a novel physiological metric to predict percutaneous coronary intervention (PCI) outcomes. Part 2 reviews subgroup analyses from the SYNTAX trial, which compared PCI and coronary artery bypass grafting (CABG) in patients with complex CAD. These analyses uncover treatment effect heterogeneity based on clinical and lesion characteristics, emphasizing the importance of considering multifactorial interactions in revascularization strategy. Part 3 investigates the applicability of the SYNTAX Score II 2020 (SSII-2020) in real-world settings using non-randomized registry data. The analysis explores differences between randomized and registry populations, estimating appropriate treatment allocations and highlighting practical considerations in applying SSII-2020 to diverse patient populations. Part 4 develops an individualized decision-support tool using machine learning, integrating clinical, anatomical, and biomarker data to predict long-term mortality and treatment benefit from PCI or CABG in complex CAD. Part 5 explores device-specific strategies, focusing on bioresorbable scaffolds (BRS) and drug-coated balloons (DCB) as alternatives to drug-eluting stents (DES). It addresses their potential benefits and limitations, and the challenge of identifying patients most likely to benefit from these novel technologies. Collectively, this work contributes to the advancement of precision medicine in coronary revascularization through diagnostic refinement, prognostic modeling, and individualized treatment selection.

Publisher

University of Galway

Rights

CC BY-NC-ND

cbn

Collections

University of Galway Theses (PhD Theses)