Rationale and pathways forward in the implementation of coronary artery calcium-based enrichment of randomized trials. [Review]

MedStar author(s):
Citation: American Heart Journal. 243:54-65, 2022 01.PMID: 34587511Institution: MedStar Union Memorial HospitalDepartment: Internal Medicine ResidencyForm of publication: Journal ArticleMedline article type(s): Journal Article | ReviewSubject headings: *Calcium | *Coronary Artery Disease | Coronary Angiography | Coronary Artery Disease/ep [Epidemiology] | Coronary Vessels/dg [Diagnostic Imaging] | Humans | Randomized Controlled Trials as Topic | Risk Assessment/mt [Methods] | Risk FactorsYear: 2022Local holdings: Available online from MWHC library: 1995 - present, Available in print through MWHC library: 1999 - 2006ISSN:
  • 0002-8703
Name of journal: American heart journalAbstract: The Food and Drug Administration recommends prognostic enrichment of randomized controlled trials (RCTs), aimed at restricting the study population to participants most likely to have events and therefore derive benefit from a given intervention. The coronary artery calcium (CAC) score is powerful discriminator of cardiovascular risk, and in this review we discuss how CAC may be used to augment widely used prognostic enrichment paradigms of RCTs of add-on therapies in primary prevention. We describe recent studies in this space, with special attention to the ability of CAC to further stratify risk among guideline-recommended candidates for add-on risk-reduction therapies. Given the potential benefits in terms of sample size, cost reduction, and overall RCT feasibility of a CAC-based enrichment strategy, we discuss approaches that may help maximize its advantages while minimizing logistical barriers and other challenges. Specifically, use of already existing CAC data to avoid the need to re-scan participants with previously documented high CAC scores, use of increasingly available, large clinical CAC databases to facilitate the identification of potential RCT participants, and implementation of machine learning approaches to measure CAC in existing computed tomography images performed for other purposes, will most likely boost the implementation of a CAC-based enrichment paradigm in future RCTs. Copyright (c) 2021. Published by Elsevier Inc.All authors: Anugula D, Bhatt DL, Bittencourt MS, Blaha MJ, Blankstein R, Blumenthal RS, Cainzos-Achirica M, Grandhi G, Mszar R, Nasir K, Patel KV, Ray KKOriginally published: American Heart Journal. 243:54-65, 2022 Jan.Fiscal year: FY2022Digital Object Identifier: Date added to catalog: 2021-11-01
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Item type Current library Collection Call number Status Date due Barcode
Journal Article MedStar Authors Catalog Article 34587511 Available 34587511

Available online from MWHC library: 1995 - present, Available in print through MWHC library: 1999 - 2006

The Food and Drug Administration recommends prognostic enrichment of randomized controlled trials (RCTs), aimed at restricting the study population to participants most likely to have events and therefore derive benefit from a given intervention. The coronary artery calcium (CAC) score is powerful discriminator of cardiovascular risk, and in this review we discuss how CAC may be used to augment widely used prognostic enrichment paradigms of RCTs of add-on therapies in primary prevention. We describe recent studies in this space, with special attention to the ability of CAC to further stratify risk among guideline-recommended candidates for add-on risk-reduction therapies. Given the potential benefits in terms of sample size, cost reduction, and overall RCT feasibility of a CAC-based enrichment strategy, we discuss approaches that may help maximize its advantages while minimizing logistical barriers and other challenges. Specifically, use of already existing CAC data to avoid the need to re-scan participants with previously documented high CAC scores, use of increasingly available, large clinical CAC databases to facilitate the identification of potential RCT participants, and implementation of machine learning approaches to measure CAC in existing computed tomography images performed for other purposes, will most likely boost the implementation of a CAC-based enrichment paradigm in future RCTs. Copyright (c) 2021. Published by Elsevier Inc.

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