Ignite Creation Date:
2024-05-06 @ 6:37 PM
Last Modification Date:
2024-10-26 @ 2:51 PM
Study NCT ID:
NCT05722145
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2024-04-16
First Post:
2023-01-09
Brief Title:
Global Pretest Probability Study of Coronary Artery Disease
Sponsor:
National Heart Centre Singapore
Organization:
National Heart Centre Singapore
Study Overview
Official Title:
Global Pretest Probability Study of Coronary Artery Disease
Status:
ENROLLING_BY_INVITATION
Status Verified Date:
2024-04
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:
GPS-CAD
Brief Summary:
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 andor 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
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
Detailed Description:
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 Agatstons 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 200000 Assuming an area under the receiver operating curve AUC of 070 for existing PTP and CAC methods this proposal is adequately powered to detect an increase of 005 in AUC using a two-sided z-test at a significance level of 005 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 Students 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 Fishers 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 010 Variables that showed a significant p005 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 p005 Advanced machine learning techniques eg neural networks may be applied
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 200000 Assuming an area under the receiver operating curve AUC of 070 for existing PTP and CAC methods this proposal is adequately powered to detect an increase of 005 in AUC using a two-sided z-test at a significance level of 005 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 Students 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 Fishers 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 010 Variables that showed a significant p005 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 p005 Advanced machine learning techniques eg neural networks may be applied
Study Oversight
Has Oversight DMC:
None
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?:
None