This is a retrospective, multicenter, observational study aimed at assessing stent apposition for coronary stent implantation by an optical coherence tomography system constructed by deep learning algorithms and evaluating the prognosis of patients after stent implantation in conjunction with multimodal diagnostic and therapeutic information.
In this study, we planned to retrospectively collect 2,000 subjects who underwent optical coherence tomography-guided percutaneous coronary stent implantation with an optical coherence tomography system constructed by a deep learning algorithm from 3 centers to assess stent apposition for coronary stent implantation, and to classify subjects into a group with poor stent apposition (axial distance \>400 μm or length \>1 mm) and a group with good stent apposition. All subjects were followed up within 12 months after the procedure.
Study Type
OBSERVATIONAL
Enrollment
2,000
Stenting will be performed with OCT guidance according to the algorithm described in the protocol. A deep learning-based OCT system was used to measure the adherence of coronary stents.
MACE
Patients were followed up within 1 year after OCT and PCI. The follow-up included major adverse cardiac events: All causes were death, recurrent myocardial infarction, target vessel reconstruction, and stent thrombosis.
Time frame: Post-procedure within 1 year
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