Rationale, Design, and Baseline Characteristics of Chinese Registry in Early Detection and Risk Stratification of Coronary Plaques (C-STRAT) Study
Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide. Noninvasive imaging modalities, particularly coronary computed tomographic angiography (CCTA), have become pivotal in the diagnosis and management of CAD. CCTA provides detailed information on coronary atherosclerotic plaques, offering improved diagnostic accuracy and sensitivity for identifying obstructive CAD. Modern CCTA scanners, with their superior temporal and spatial resolution, have further enhanced the prognostic value of CCTA in predicting future adverse CAD events. However, much of the existing evidence on CCTA’s prognostic utility is based on older scanner technology and data from developed countries. With the advent of artificial intelligence (AI) and machine learning, there is a growing need for large datasets to integrate these technologies into cardiovascular imaging, particularly CT imaging, to improve CAD management.
The C-STRAT (Chinese Registry in Early Detection and Risk Stratification of Coronary Plaques) study was designed to address these gaps. Registered in the Chinese clinical trial registry authority (ChiCTR1800015864) and approved by the Ethical Committees of Chinese PLA General Hospital (No: S2018-033-01), C-STRAT is a multisite registry study involving 13 tertiary hospitals across China. These hospitals are located in North, East, Central, South, and West China, ensuring a diverse and representative patient cohort. The study aimed to enroll consecutive patients aged 18 to 75 years with stable chest pain or chest pain equivalent syndromes, who were scheduled to undergo CCTA as per physician orders. Patients with unstable hemodynamics, those requiring urgent evaluation for suspected acute coronary syndrome (ACS), or those with a history of CAD were excluded. Recruitment took place from May 2017 to October 2019.
All enrolled subjects underwent CCTA examination, with patient information collected prospectively by investigators. This included demographic data, medical history, and other relevant clinical information documented in case report forms (CRFs). An encrypted website platform (www.ct-registry.cn) was established to collect, manage, and monitor the large dataset. The platform’s operation was approved by the principal investigator and institutional review board.
CCTA images were interpreted according to the Society of Cardiovascular Computed Tomography (SCCT) guidelines. A coronary artery tree model was used to visually estimate the three major coronary arteries and their branches. The American Heart Association (AHA) 17-segment model was employed to analyze all coronary arteries with a diameter of ≥2 mm. The Coronary Artery Disease-Reporting and Data System (CAD-RADS) was used to classify coronary artery stenosis. CAD-RADS 0 indicates no coronary stenosis, CAD-RADS 5 indicates totally occlusive lesions, and CAD-RADS 1–4 correspond to lumen stenosis of 1%–24% (minimal stenosis), 25%–49% (mild stenosis), 50%–69% (moderate stenosis), and 70%–99% (severe stenosis), respectively. CAD-RADS 3–5 are considered obstructive CAD. In cases of disagreement between interpreters, a consensus was reached through discussion.
Follow-up of enrolled subjects was conducted by dedicated physicians or research nurses. The C-STRAT database was locked in December 2019 to identify subjects for long-term follow-up. The follow-up interval is 24 to 36 months, with an anticipated 5% loss to follow-up. The primary endpoint is major adverse cardiovascular events (MACE). Ascertainment of death and other outcomes was determined through direct interviews, telephone contacts with the patient’s immediate family, or review of medical records. Case records were photocopied for data retention.
The primary objective of the C-STRAT study is to identify the association between CCTA imaging findings and long-term prognosis in a large Chinese population with suspected CAD, thereby improving risk stratification by combining imaging indicators with clinical characteristics. Secondary objectives include:
- Assessing the current status of CCTA application in China.
- Evaluating the rate of invasive testing and utilization of other examination resources following CCTA.
- Determining the accuracy of CAD probability in the Chinese population based on pretest probability models established by European and American guidelines.
- Developing rapid detection technologies for plaque features and lumen stenosis using AI and machine learning.
A total of 30,039 subjects (55.3% male) were recruited, with an average age of 58.8 years. The prevalence of hypertension, hyperlipidemia, diabetes, stroke, peripheral vascular disease, and CAD family history was 43.4%, 32.5%, 15.4%, 4.5%, 5.8%, and 16.9%, respectively. 35.5% of subjects were current smokers, and 40.8% consumed alcohol. Regarding the reasons for CCTA examination, 23.7% of subjects had typical chest pain, 34.5% had atypical chest pain, and 41.8% had non-chest pain symptoms. The prevalence of obstructive CAD (CAD-RADS 3–5) was 22.0%, while nonobstructive CAD (CAD-RADS 0–2) was observed in 76.6% of subjects. Notably, 36.4% of subjects had no coronary artery stenosis, and only 1.4% had uninterpretable CCTA images due to poor image quality.
The C-STRAT study highlights the increasing use of CCTA in China and the need to manage the growing volume of CCTA-derived information to improve risk stratification and CAD management. By providing a large, prospective, multisite patient cohort, C-STRAT aims to establish and validate new models for pretest prediction in stable chest pain patients, optimizing diagnostic and treatment decision-making in clinical practice.
The study also draws comparisons with the CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry) study, which demonstrated that CCTA findings add incremental prognostic value beyond demographic and clinical characteristics. However, CONFIRM was conducted decades ago, and imaging quality has since improved with new-generation CT scanners. C-STRAT observed a lower proportion of asymptomatic patients (24.6%) compared to CONFIRM (34.2%), suggesting that CCTA is less likely to be recommended for asymptomatic patients in China. Additionally, C-STRAT predominantly used prospective gating (100%), compared to only 13% in CONFIRM.
The China-PAR (Prediction for Arteriosclerotic Cardiovascular Disease Risk in China) project has developed and validated a 10-year risk prediction equation for ASCVD based on the Chinese population. C-STRAT aims to explore whether combining CT imaging findings with the China-PAR equations can enhance risk prediction in its cohort.
The integration of AI in cardiovascular imaging is another focus of C-STRAT. AI has the potential to optimize image acquisition, improve image post-processing, enable accurate segmentation, and explore novel prognostic biomarkers. By providing high-quality imaging and clinically relevant big data, C-STRAT aims to assist in the development of AI technologies that can automatically identify plaque features, enhance risk assessment, and detect “vulnerable patients.”
In conclusion, the C-STRAT registry is the largest prospective multisite observational study related to CCTA imaging to date. Its primary goal is to explore new early diagnostic technologies and identify associations between CT imaging findings and clinical prognosis. The study also aims to evaluate the current utilization of noninvasive imaging in China, establish a large-scale Chinese population cohort, and provide new insights for future randomized controlled studies. Ultimately, C-STRAT seeks to optimize and improve risk stratification strategies for CAD in China.
doi.org/10.1097/CM9.0000000000001307
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