Identification of Neoatherosclerosis in ISR Patients Based on Artifical Intelligence

January 13, 2021 updated by: Yun Dai Chen, Chinese PLA General Hospital

Identification of Neoatherosclerosis in In-stent Restenosis Patients Based on Artifical Intelligence

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)

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Drug Eluting Stents (DES) reduce the rate of in-stent restenosis (ISR) to 3.6-10%. Based on the large population of patients, 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). The European Expert Consensus on Intravascular Imaging, published in 2018, recommends finding the underlying mechanisms of ISR through intravascular imaging guidance (IVUS or OCT), and determining therapeutic strategies based on the mechanisms. Circulation published a new Waksman ISR classification based on mechanisms and components of the restenosis tissue, which provides guidance of treatment strategy. The use of intravascular imaging to identify and classify the types and mechanisms is very important for ISR 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 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 of 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 Jan 31st,2020. Offline OCT analysis was performed using dedicated software (Light Lab Imaging Inc, Westford, MA). All images were analyzed at every frame in the stents by 2 independent investigators, who were blinded to the angiographic and clinical findings. 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)

Study Type

Observational

Enrollment (Anticipated)

90

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

  • Name: Yingqian Zhang, M.D
  • Phone Number: 15652505966 15652505966
  • Email: niniya731@163.com

Study Contact Backup

  • Name: Hui Hui, PH.D
  • Phone Number: 010-55499309 010-55499309
  • Email: hui.hui@is.ac.cn

Study Locations

  • China
      • Beijing, China, 100853
        • Recruiting
        • The General Hospital of PLA
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 80 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

100 patients were enrolled

Description

Inclusion Criteria:

- all gender

18ys 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

Exclusion Criteria:

  • 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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Other
  • Time Perspectives: Retrospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
CAG and OCT group
Images of CAG and OCT patients obtained from ISR patients were retrospectively collected and analyzed.
Other: no interventin
Our stusy analysed the images obtained from ISR patients, no extra intervention was given based on this study.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The identification of NA
Time Frame: through the study completion, an average of 3 years
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
Time Frame: through the study completion, an average of 3 years
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
Time Frame: through the study completion, an average of 3 years
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
Time Frame: through the study completion, an average of 3 years
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
Time Frame: through the study completion, an average of 3 years
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
Time Frame: through the study completion, an average of 3 years
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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Chinese PLA General Hospital

Investigators

  • Principal Investigator: Yundai Chen, M.D, Chinese PLA General Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 1, 2015

Primary Completion (Anticipated)

January 31, 2021

Study Completion (Anticipated)

February 28, 2021

Study Registration Dates

First Submitted

December 21, 2019

First Submitted That Met QC Criteria

January 5, 2020

First Posted (Actual)

January 7, 2020

Study Record Updates

Last Update Posted (Actual)

January 15, 2021

Last Update Submitted That Met QC Criteria

January 13, 2021

Last Verified

January 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AI-301-ISR

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

No

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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