Ignite Creation Date:
2025-12-24 @ 10:10 PM
Ignite Modification Date:
2025-12-25 @ 7:45 PM
Study NCT ID:
NCT05592535
Status:
COMPLETED
Last Update Posted:
2022-10-24
First Post:
2022-10-17
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Outcomes With Fractional Flow Reserve in Chronic Coronary Syndrome
Sponsor:
Uppsala University
Organization:
Study Overview
Official Title:
Prognostic Impact of Fractional Flow Reserve in Chronic Coronary Syndrome: Quasi-Experimental Findings Using a Regression Discontinuity Design
Status:
COMPLETED
Status Verified Date:
2022-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The use of fractional flow reserve (FFR) to assess the functional relevance of coronary stenoses has been demonstrated to reduce the risk urgent revascularization in chronic coronary syndrome patients.\[1\] The goal of this study is to assess whether the utility of using FFR during percutaneous coronary intervention (PCI) in chronic coronary syndrome patients is confirmed in a real-life scenario. This study will implement a regression discontinuity design (RDD). RDD is a quasi-experimental study design able to provide robust findings on causality using observational data.
Detailed Description:
All patients in this study were included in the Swedish Coronary Angiography and Angioplasty Registry (SCAAR), a sub-registry of SWEDEHEART. \[2\] Data regarding FFR assessments are documented in SCAAR in terms of FFR values and coronary segments investigated with FFR. The use of FFR during PCI is left at the discretion of the operator. Since a nondeterministic assignment to revascularization is expected at the cut-off, a fuzzy RDD design will be used in the analyses. Moreover, FFR values equal to 0.80 (at the cut-off) will be excluded from the analysis since treatment assignment exactly at the cut-off may substantially vary across operators and this may create distortions in the treatment discontinuity. Local linear regression estimates with Kernel triangulation and asymmetric bandwidth selection will be used in the analysis. Bandwidth selection will be based on a fully data-driven approach that minimizes the bias-variance trade-off.\[3\] Estimates from RDD will be presented as risk differences \[RD\] complemented by 95% robust confidence intervals.\[4\]
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: