Identification of Neoatherosclerosis in ISR Patients Based on Artifical Intelligence

NCT04220437 | Unknown

• 1 other identifier: AI-301-ISR
• Study Type: observational
• Target: 90
• Locations: 1 country
• Sites: 1

Brief Summary

Based on the large population of patients, in-stent restenosis (ISR) is still an important problem in the field of cardiovascular disease. How to reduce the incidence of ISR and the treatment of ISR has become the focus and hot spot. The 2018 ESC Guidelines for Cardiovascular Intervention recommends treatment of ISR under the guidance of intravascular ultrasound (IVUS), or optical coherent tomography (OCT). Circulation published a new Waksman ISR classification based on mechanisms and components of the restenosis tissue, which provides guidance for treatment strategy. Because of its good resolution, OCT makes it more accurate to distinguish the components of vascular tissue, thus providing a decision-making basis for interventional therapy. OCT examination can obtain the characteristics of the ISR more precisely. Neoatherosclerosis (NA), is one of the ISR types and accounts for more stent failure and target lesion failure than other types. Identification NA is important for decision-making of interventional therapy. However, the acquisition and analysis of OCT images not only need the digital angiography machine (DSA) equipped with the majority of hospitals, but also need professional OCT imaging equipment and technicians. Patients with severely CKD cannot bear OCT examination because of the large amount of contrast agent. OCT catheter is more than ten times the price of the CAG catheter. Therefore, identification of NA by the use of artificial intelligence (AI) is of significance to set therapeutic strategy for ISR patients, especially in patients with CKD. Our study retrospectively analyzed CAG images and OCT images of ISR patients obtained from Jan 1st,2015 to Oct 31st,2020. Identify NA by analyzing OCT images, build up U-net and V-net to analyze the CAG and OCT images, and finally build up an identification system of NA based on CAG images by AI. This study has been approved by Ethics Committee of Chinese PLA General Hospital (S2018-033-01)

Conditions

In-stent Restenosis; Neo-atherosclerosis; Retrospective

Keywords

Neoatherosclerosis; in-stent restenosis; Optical Coherence Tomography; angiography; artificial intelligence

Outcome Measures

Primary Outcomes (6)

• The identification of NA

  a neointima containing a diffuse border and a signal-poor region, with the struts underneath invisible because of the marked signal attenuation

  through the study completion, an average of 3 years

• neovascularizaion

  diameter 50-300um, cavity in the stent area, not connected with the vasular

  through the study completion, an average of 3 years

• ISR segment in the CAG images

  the segement in the stent area or within 5mm beside the stent,diameter stenosis rate\>50%

  through the study completion, an average of 3 years

• lipid-core arc

  To quantify the circumferential extent of NA, the lipid-core arc was measured at a 0.2-mm interval throughout the segments showing NA.

  through the study completion, an average of 3 years

• Thin-cap fibroatheroma-like neointima

  defined as a neointima characterized by a fibrous cap thickness at the thinnest part of \<65 μm and an angle of lipid-laden neointima of \>180 degrees

  through the study completion, an average of 3 years

• macrophage arc

  measured at 0.2-mm intervals and divided into 5 groups: grade 0, no macrophages; grade 1, localized macrophage accumulation, \<30 degrees; grade 2, clustered accumulation, ≥30 and \<90 degrees; grade 3, clustered accumulation, ≥90 and \<270 degrees; and grade 4, clustered accumulation, ≥270 degrees.

  through the study completion, an average of 3 years

Study Arms (1)

CAG and OCT group

Images of CAG and OCT patients obtained from ISR patients were retrospectively collected and analyzed.

Other: no interventin

Interventions

no interventin OTHER

Our stusy analysed the images obtained from ISR patients, no extra intervention was given based on this study.

CAG and OCT group

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

100 patients were enrolled

You may qualify if:

• \- all gender
• ys old to 80ys old
• diagnosed of in-stent restenosis based on CAG
• both CAG images and OCT images were obtained in the same patient on the same day

You may not qualify if:

• CAG images and OCT images were not obtained on the same day in the same patient
• low quality in CAG images
• low qualitiy in OCT images

Sponsors & Collaborators

• Chinese PLA General Hospital: lead

Study Sites (1)

The General Hospital of PLA

Beijing, 100853, China

RECRUITING

Study Officials

• Yundai Chen, M.D

  Chinese PLA General Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Yingqian Zhang, M.D

CONTACT

15652505966; niniya731@163.com

Hui Hui, PH.D

CONTACT

010-55499309; hui.hui@is.ac.cn

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: December 21, 2019
• First Posted: January 7, 2020
• Study Start: February 1, 2015
• Primary Completion: January 31, 2021
• Study Completion: February 28, 2021
• Last Updated: January 15, 2021

Record last verified: 2021-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)