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Updated: Aug 12, 2026
Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Coronary artery calcification and cardiovascular risk factors: impact of the analytic approach
Muredach P Reilly1, Megan L Wolfe, A Russell Localio
1Cardiovascular Division, Department of Medicine, Center for Experimental Therapeutics, University of Pennsylvania School of Medicine, 909 BRB 2/3, 421 Curie Blvd., Philadelphia, PA 19004-6160, USA. muredach@spirit.gcrc.upenn.edu
Insights
Analyzing coronary artery calcification (CAC) requires careful methods. Certain analytical approaches for CAC scores are more effective in identifying cardiovascular risk factors than others, ensuring consistent results.
Area of Science:
- Cardiology
- Biostatistics
- Epidemiology
Background:
- Coronary artery calcification (CAC) is a marker for coronary atherosclerosis.
- Analyzing CAC is complex due to population distribution, hindering cross-study comparisons.
- Identifying novel risk factors for cardiovascular disease is crucial.
Purpose of the Study:
- To evaluate the impact of different analytical methods on detecting associations between CAC and cardiovascular risk factors.
- To identify reliable analytical approaches for CAC data analysis.
- To improve the interpretation and comparison of CAC studies.
Main Methods:
- Applied multivariable analyses to CAC data from 914 asymptomatic subjects.
- Utilized linear regression with different CAC score transformations.
- Employed tobit regression, logistic regression (zero cut-point), and ordinal logistic regression (CAC categories).
Main Results:
- Standard methods like linear regression of log CAC scores and logistic regression with a zero cut-point missed some risk factor associations.
- Linear/tobit regression of log (CAC + 1) and ordinal regression of CAC categories identified more associations.
- The latter methods provided more consistent results in linking CAC to cardiovascular risk factors.
Conclusions:
- Commonly used CAC analysis methods may fail to detect significant associations with cardiovascular risk factors.
- Specific analytical approaches, including log-transformed CAC scores and categorized CAC, yield more consistent and comprehensive results.
- Recommends using at least two distinct multivariable methods for robust CAC analysis.
Abstract:
Coronary artery calcification (CAC) may help identify novel risk factors for coronary atherosclerosis. However, analysis of CAC is challenging because of the distribution of CAC in the population. This has resulted in difficulty in interpreting and comparing results across studies. We applied several analytic approaches to CAC data in order to determine the impact of analytic methods on the association with established cardiovascular risk factors in 914 asymptomatic subjects in the Study of Inherited Risk Factors for Coronary Atherosclerosis. Multivariable analyses included: (1) linear regression of different transformations of CAC scores; (2) tobit regression of the log of (CAC + 1); (3) logistic regression using CAC zero as a cut-point; and (4) ordinal logistic regression using CAC categories. Linear regression of the log CAC scores and logistic regression of CAC zero cut-point failed to detect associations with some risk factors. In contrast, linear and tobit regression of the log (CAC + 1) and ordinal regression of CAC categories identified more associations and provided consistent results. Commonly applied methods of CAC analysis may fail to detect associations with cardiovascular risk factors. We present analytic approaches that are likely to provide consistent results and recommend the use of at least two distinct multivariable methods.
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