Why the study?
To investigate predictors of no-reflow in primary PCI using optical coherence tomography.
Do high-risk plaque morphological features identified by optical coherence tomography (OCT) predict the no-reflow phenomenon in patients undergoing percutaneous coronary intervention?
Do high-risk plaque morphological features identified by optical coherence tomography (OCT) predict the no-reflow phenomenon in patients undergoing percutaneous coronary intervention?
Pre-intervention optical coherence tomography (OCT) identification of thin-capped fibroatheroma, plaque rupture, and thrombus can effectively predict the occurrence of the no-reflow phenomenon after primary PCI.
May identify high-risk lesions for no-reflow but should not yet change practice; leaves open prospective trials of OCT-guided prevention.
Aims: The aim of this study was to perform a systematic review and meta-analysis to investigate predictors of no-reflow in primary PCI using optical coherence tomography (OCT). Methods and results: We conducted a systematic search of published or presented studies in PubMed, Embase, and Cochrane Central databases for studies that compared no reflow and normal flow in patients with coronary artery disease following PCI guided by OCT.A total of five observational studies total number of patients = 356; No-reflow group = 96, Normal flow group =260) were included in the analysis. On pooled analysis, the presence of TCFA were significantly greater in the no reflow group than in the normal flow group, and the no reflow group had higher frequencies of plaque rupture and thrombus.In addition,we found that larger lipid arc (MD=66.51°, 95% CI: 3.05 to 129.96, p<0.00001) and length (MD=3.86mm, 95% CI: 2.01 to 5.70, p<0.0001) were more frequently observed in no reflow group. Conclusion: The thin-capped fibroatheroma (TCFA) and large lipid plaque were good morphological features in identifying high-risk lesions for no-reflow after PCI. And these two features can serve as high-risk factors for no-reflow and guide clinical early prediction and prevention .
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Qin et al. (2023) studied this question.
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