Computer Prediction of Restenosis Following Peripheral Angioplasty

This study is currently recruiting participants. (see Contacts and Locations)
Verified January 2014 by University Health Network, Toronto
Sponsor:
Information provided by (Responsible Party):
Dheeraj Rajan, University Health Network, Toronto
ClinicalTrials.gov Identifier:
NCT01202344
First received: September 10, 2010
Last updated: January 17, 2014
Last verified: January 2014

September 10, 2010
January 17, 2014
September 2010
September 2014   (final data collection date for primary outcome measure)
Simulation Accuracy [ Time Frame: less than 6 hours after the procedure ] [ Designated as safety issue: No ]
From the pre-intervention CT-scans, the target artery, calcium and lumen will be segmented, meshed and used to simulate the angioplasty steps. Simulation accuracy will be evaluated by comparing geometrical descriptors of the artery and lumen size and shape calculated in the simulation to those measured on the post-intervention CT scan images.
Same as current
Complete list of historical versions of study NCT01202344 on ClinicalTrials.gov Archive Site
Logistic Regression Analysis [ Time Frame: 6 months ] [ Designated as safety issue: No ]

Logistic regression analysis will be performed using NCSS statistical software to identify which explanatory variable(s), selected from the simulation results, can be used to predict binary restenosis, the categorical dependent variable.

Binary restenosis will be determined by comparing the CT-scan images obtained 1 hour post-intervention to those obtained at the 6 month follow-up study.

Same as current
Not Provided
Not Provided
 
Computer Prediction of Restenosis Following Peripheral Angioplasty
Computer Prediction of Restenosis Following Peripheral Angioplasty

The purpose of this study is to develop a computer program that might be able to accurately assess the risk of artery re-narrowing following angioplasty or stenting based on computer images. After angioplasty (a procedure to re-open narrowed or blocked blood vessels) the patients will have extra images taken in order to assess the results of the procedure; which will then be used to see whether or not these images can help predict outcomes such as the patient having to come back to the hospital to have the procedure done again.

The study involves medical imaging of patients undergoing an angioplasty intervention in a peripheral artery. It is similar to an observational study, except that additional imaging is performed which is above the standard-of-care. Some risks may be associated with the additional imaging due to a small increase in radiation exposure and intravenous contrast administration. No investigational drug or device will be tested in this study. No control group will be used.

Logistic regression analysis will be performed using NCSS statistical software to identify which explanatory variable(s), selected from the simulation results, can be used to predict binary restenosis, the categorical dependent variable.

For each subject, binary restenosis will be determined by comparing the CT-scan images obtained 1 hour post-intervention to those obtained at the 6 month follow-up study. The CT-scan images will be segmented and a mesh of the target vessel will be reconstructed as described in objective 1. The lumen area will be measured in every cross-section of the mesh perpendicular to the vessel centerline, with 2 mm steps between cross-sections. The minimum lumen diameter will be calculated from the minimum lumen area. If the minimum lumen diameter at follow-up is less than 50% of the minimum lumen diameter post-intervention, then the binary restenosis is positive. Otherwise it is negative.

Objective 1: Evaluate the accuracy of computer predictions of artery dilatation and stent implantation from CT-scan images. This information is hypothesized to be indicative of the accuracy of other quantities predicted by computer simulation of angioplasty, such as those used as independent variables in objective 2.

Objective 2: Establish a regression model with 80% sensitivity and 80% specificity for predicting binary restenosis based on one or several injury parameters in patients undergoing angioplasty. The candidate injury parameters are:

  • endothelium denudation (in % of total endothelium area)
  • descriptors of the magnitude and spatial distribution of stretch ratio in the arterial wall
  • descriptors of the magnitude and spatial distribution of intramural stress in the arterial wall All injury parameters are predicted by computer simulation of angioplasty.
Interventional
Not Provided
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: Open Label
Primary Purpose: Diagnostic
Peripheral Angioplasty
Other: Additional x-ray images
Approximately 3 additional x-ray images following angioplasty (within 1 hour)
  • Restenosis
    Patients who have restinosis immediately following angioplasty.
    Intervention: Other: Additional x-ray images
  • No Restenosis
    Patients who do not have restinosis immediately following angioplasty.
    Intervention: Other: Additional x-ray images

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruiting
15
Not Provided
September 2014   (final data collection date for primary outcome measure)

Inclusion Criteria:

  • scheduled for percutaneous dilation of a peripheral artery;
  • age more than 18 years;
  • informed consent signed by the subject;
  • target lesion in native artery;
  • baseline lumen diameter greater than 4 mm.

Exclusion Criteria:

  • previous revascularization of the target lesion;
  • subject undergoing chemotherapy.
Both
18 Years and older
No
Contact: Dheeraj Rajan, MD 416-340-4911 dheeraj.rajan@uhn.on.ca
Canada
 
NCT01202344
08-0335-BE
Not Provided
Dheeraj Rajan, University Health Network, Toronto
Dheeraj Rajan
Not Provided
Principal Investigator: Dheeraj Rajan, MD University Health Network, Toronto
University Health Network, Toronto
January 2014

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP