Formation and Transformation of Neointima after Drug-eluting Stent Implantation: Insights from Optical Coherence Tomographic Studies.
Seung Yul LeeMeyong-Ki HongYang Soo JangPublished in: Korean circulation journal (2017)
After coronary stent implantation, neointima formation resembles the wound healing process as it involves the sequential processes of inflammation, granulation, and remodeling. Because antiproliferative drugs and polymers of drug-eluting stents (DESs) delay vascular healing compared with bare metal stents, fibrin deposition can remain long after stent implantation, or inflammation can be excessive. Delayed vascular healing can be associated with adverse clinical outcomes including DES thrombosis or restenosis, and poor endothelization of DES neointima can accelerate neoatherosclerotic change inside the neointima, further contributing to luminal restenosis or neointimal instability. Despite the lack of correlation between pathologic and optical coherence tomography (OCT) findings, OCT assessments of neointima under various circumstances can reveal vascular responses to stent therapy. Homogeneous, heterogeneous, and layered neointima patterns can be recognized by OCT and can change with time. Homogeneous neointima might be associated with better clinical outcomes after DES implantation, whereas non-homogeneous neointima or neoatherosclerotic change can be associated with poorer clinical outcomes. However, limited data are currently available, and further studies are required to comprehensively address these questions.
Keyphrases
- smooth muscle
- optical coherence tomography
- oxidative stress
- diabetic retinopathy
- coronary artery
- stem cells
- emergency department
- pulmonary embolism
- heart failure
- gene expression
- machine learning
- wound healing
- neoadjuvant chemotherapy
- high resolution
- dna methylation
- single cell
- mesenchymal stem cells
- physical activity
- genome wide
- mass spectrometry
- transcatheter aortic valve replacement
- atrial fibrillation
- data analysis
- weight loss
- deep learning