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The use of pre-test probability (PTP) and coronary artery calcium (CAC) scores is guideline-recommended in the evaluation of coronary artery disease (CAD) and stable chest pain. The utility of these scores is population dependent. Previous studies have predominantly been limited to Western populations, despite Asia forming 60% of the global population. However, Asian populations have differing coronary artery phenotypes and may therefore have different PTPs with varying implications for risk stratification. Known difference in CAC implications support a global approach. Hence, this study aims to evaluate a contemporary PTP in diverse real-world Asian, Western and other cohorts and to evaluate the incremental value of CAC in predicting CAD and events. Primarily, the study will compare population specific PTPs and CAC for prediction of coronary computed tomography angiography (CTA) CAD. This could be compared with existing guideline-recommended PTPs alone or with consideration of risk factors or CAC. The study will also evaluate the accuracy of the prediction of major adverse cardiovascular events (MACE) using PTP models, risk factors and/or CAC. Lastly, the study will investigate the accuracy of zero CAC and other minimal risk tools to de-risk cardiovascular disease (CVD) in various populations.
The study will investigate multiple international cohorts of patients referred for noninvasive testing using coronary CTA or other non-invasive imaging modalities. Locally-calibrated PTP models in consideration of risk factors or CAC will be separately tailored to each different cohort, and will be evaluated.
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This study is an aggregated registry comprising of a retrospective medical record review of individuals from multiple sites. The general approach is to create a large, consolidated, global registry of existing cohorts of patients referred for noninvasive testing using computed tomography (CT). Anonymized images and structured data, including demographics, risk factors, outcomes and CT results will be obtained from multiple sites. The types of images to be analyzed and quantified are non-contrast CT (NCCT) scans and coronary CT angiography (CCTA). CAC score will use Agatston's method while CAD will be assessed using registry data of CCTA reads.
The data collected will include risk factors and demographics such as age, sex, ethnicity, hypertension, smoking, diabetes, dyslipidemia and family history of CAD. Outcomes such as death and myocardial infarctions will be included in the dataset if available. All data received will be anonymized and de-identified. Study team members will check through the study data to ensure that all study data is accurately collected and complete.
The data elements of different cohorts may not harmonize or match with each other. There could be missing data elements or different data inputs. As such, omission or imputation may be used to perform analyses. To minimize data heterogeneity in format, sites will be provided with a standard template and data dictionary. This will complete the initial data harmonization and expected data elements. The collected dataset would then be harmonized by the biostatistics team prior to analysis.
The approximate total study size n = 200,000. Assuming an area under the receiver operating curve (AUC) of 0.70 for existing PTP and CAC methods, this proposal is adequately powered to detect an increase of 0.05 in AUC using a two-sided z-test at a significance level of 0.05. Continuous variables will be expressed as mean and standard deviation. Categorical variables will be expressed as absolute numbers and percentages. Distributions will be tested for normality using Shapiro-Wilk statistics. Non-normally distributed variables will be represented as median with 25th to 75th interquartile range. Comparison of normally distributed continuous variables will be performed using Student's t test for paired and unpaired data. Non-normally distributed variables will be compared using Mann-Whitney Rank Sum tests and Kruskal-Wallis tests. Comparison of categorical data will be performed using Chi-square and Fisher's Exact Tests where appropriate.
Differences in outcomes over time will be analyzed by the Kaplan-Meier analysis with log-rank test for each outcome. Using Cox regressions analysis univariate and multivariate regression analyses will be performed. Univariate analysis will include pre-event variables with p values <0.10. Variables that showed a significant (p<0.05) correlation with the endpoints, after univariate analysis, will be considered in the multivariate models. Odds ratios and 95% confidence interval will be calculated. Statistical significance was established as p<0.05. Advanced machine learning techniques (e.g. neural networks) may be applied.
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