CI

At a glance

ClinicalIndex Comparison Record
N/ACompleted· 1,216 enrolled
Drug / intervention
CT-FFR assessmentother
Likely dose
Not stated in record
Structured eligibility isn't available for this trial yet — see the full criteria in the Eligibility tab below.

Standardized by ClinicalIndex from the ClinicalTrials.gov record · verify against the source.

Search/NCT03901326
NCT03901326N/ACompleted

The Effect of On-site CT-derived Fractional Flow Reserve on the Management Making for the Patients With Stable Chest Pain (TARGET Trial)

Chinese PLA General Hospital·interventional·Posted Apr 3, 2019·Updated May 31, 2024

In Brief

A clinical study evaluating CT-FFR assessment for Coronary Artery Disease. Completed, enrolled 1,216 participants across 1 site.

Detailed Summary

The primary of this registry is to evaluate whether the availability of CTA/CT-FFR procedure could effectively optimize the flow of clinical practice of stable chest pain versus conventional clinical pathway in decision making, avoid the overuse of invasive procedure, finally improve clinical prognosis and reduce total medical expenditure. This registry is randomized, open labeled, prospective designed and will be performed in 6 Chinese hospitals. Approximately 1200 subjects will be enrolled and subsequently assigned to either routine clinically-indicated diagnostic care group (CID arm) or CTA/CT-FFR care group (CTA/CT-FFR arm) via computer-generated random numbers (1:1 ratio)

Study Details

Timeline

N/ACompletedFinished
2020202120222023202420252026
First PostedApr 3, 2019
Enrollment StartMay 10, 2019
Primary CompletionOct 30, 2022
Study CompletionOct 31, 2022
TodayJul 2, 2026
Enrollment to primary: 3.5 yearsPosted 7.2 years ago

Interventions

CT-FFR assessmentother

When subjects are randomized to the CTA/CT-FFR arm, FFR based on the coronary CTA imaging will be measured. DEEPVESSEL FFR workstation is very dedicated software utilizing the original CTA imaging to meter simulated FFR values based on a machine learning algorithm. The first step is to extract a 3D coronary artery model and generate coronary centerlines which are similar to the routine reconstruction of coronary CTA. The centerlines are extracted using a minimal path extraction filter. Then a novel path-based deep learning model, referred to DEEPVESSEL FFR, is used to predict the simulated FFR values on the vascular centerlines. Deep learning algorithm is used to establish characteristic sample database of coronary hemodynamics characteristic parameters. When deep training model is proved to be valid, it is applied to a new lesion-specific measurement. Lesion-specific CT-FFR is defined as simulated FFR value at distance of 20mm away from the lesion of interest.