Clinical trial NCT06498830: Application of CTA-based Radiomic Phenotyping of PCAT and Fluid Dynamics in Atherosclerotic Disease (APPLE)
Official title: Application of CTA-based Radiomic Phenotyping of Peri-coronary Adipose Tissue and Fluid Dynamics in Atherosclerotic Disease
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Computed Tomography Angiography
Inflammation of Adipose Tissue
CFD
Lead sponsor: Jinling Hospital, China
Sponsor class: OTHER
First posted: 2024-07-12
Enrollment: 2000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: patients who underwent serial CCTA (at least 2 times) and enrolled those without plaque at baseline

Brief summary: This study (APPLE study) intends to retrospectively enroll more than 2000 patients who who underwent ≥2 coronary computed tomography angiography (CCTA) with ≥3 months interval from 11 hospitals in more than 4 provinces in China.

Detailed description: A multicenter, retrospective, observational trial will be conducted (APPLE study). To investigate whether a combined model constructed on the basis of pericoronary adipose tissue (PCAT) radiomics, fluid dynamics and clinical risk factors can predict the formation of atherosclerotic plaque. It will be carried out in 11 hospitals in 4 provinces in China. The Boruta algorithm and correlation proof clustering analysis were used to screen the imaging histological features, and a random forest model was used to construct an imaging histological prediction model for PCAT and fluid dynamics and to construct radiomics' score. To investigate the incremental value of the radiomics' score beyond the traditional prediction model, the radiomics' score was combined with the traditional logistic regression prediction model. Receiver operating characteristic (ROC) curve analysis with integrated discrimination improvement (IDI) and category net reclassification index (NRI) were used to compare the performance of the predictive models. A ML-prediction model incorporates FAI, fluid dynamics and patient clinical characteristics to identify high-risk patients in advance for patients receiving routine CCTA and guide the more precise use of preventative treatments, including anti-inflammatory therapies.

Eligibility criteria: Inclusion Criteria:

* patients underwent CCTAs at least twice;
* patients without any concomitant obstructive CAD, or any concomitant atherosclerotic lesions in the LAD on the baseline CCTA;
* patients without previous percutaneous coronary intervention or coronary artery bypass grafting, implanted cardiac devices, and anomalous coronary arteries as evidenced by conventional CCTA.

Exclusion Criteria:

* image quality of CCTA was inadequate for either MB morphological or FAI or CFD analysis in either cardiac phase;
* patients received other tube voltages except for 100 kVp and 120 kVp;
* the interscan interval between serial CCTAs\< 3 months;
* missing CCTA data.

Primary outcomes:
measure: the formation and regression of any atherosclerotic plaque on the follow-up CCTA
timeFrame: 1 day
Secondary outcomes:
measure: Total plaque volume
timeFrame: 1 day
Other outcomes:
Locations:
city: Nanjing
country: China
facility: Research Institute Of Medical Imaging Jinling Hospital
state: Jiangsu
Study officials:
affiliation: Jinling Hospital, Medical School of Nanjing University, Nanjing, China
name: Longjiang Zhang, MD
role: STUDY_CHAIR
References: