Are coronary artery calcium tests beneficial?
CARDIOVASCULAR deaths in Australia have seen a relative reduction of 82% from their peak in 1968. Much of this decline is due to a general decrease in smoking, blood pressure and cholesterol levels in the population. The increased use of hypotensive and lipid-lowering treatments also played an important role.
Deaths and disabilities from cardiovascular disease (CVD) could be further reduced by improving detection of people at high risk of treatment with hypotensive and lipid-lowering drugs.
Our current best way to determine who is at high risk (and therefore could benefit from treatment) is a CVD risk calculator. Single risk factors, such as blood pressure and lipid levels, are poor predictors of overall (or absolute) cardiovascular risk, and clinicians are not able to intuitively combine these factors with precision. The Framingham Risk Equation, based on the Framingham Heart Study and Framingham Offspring Study cohorts, accurately predicted cardiovascular events in a recent Australian population-based study. Other risk equations such as QRISK3, developed for the UK population, and PREDICT, developed for the New Zealand population, also include risk factors associated with health equity, such as ethnicity and status. self-identified socio-economic.
Many other new factors have been proposed as potentially useful additions to risk assessment. An important recent proposal has been coronary artery calcium (CAC) assays, which are increasingly being used in clinical practice in Australia and overseas. However, the amount of information added by the scans beyond traditional risk assessment using a cardiovascular risk calculator is unclear.
To find out, we undertook a systematic review and meta-analysis of published studies that provided relevant data on the added value of CAC scans, beyond that provided by traditional cardiovascular risk scores. Using the 2018 US Preventive Services Task Force review of the topic as a starting point, we reviewed 2772 titles and abstracts and 53 full-text articles. We included six community-based cohort studies from the United States, the Netherlands, Germany, and South Korea, with cardiovascular event rates ranging from 0.9% to 9.4% over a follow-up of 4 in 10 years.
The studies showed a modest gain in discrimination when CT scans were added to the risk equation (the models’ ability to separate those who had and those who did not have a cardiovascular event), and this seemed consistent from one study to another. The C statistic (area under the receiver operating characteristic [ROC] curve) for CVD risk models without CAC analysis results ranged from 0.693 to 0.80. The pooled C-statistic gain from adding a CAC score was 0.036 overall, with a possibly greater gain for studies using coronary heart disease events as an outcome (C-statistic gain of 0.049 ) than for studies using cardiovascular events (gain of the C statistic, 0.029).
We also found that the test may have additional prognostic value for some participants. But the clinical significance of these small gains in discrimination and prognostic value was unclear.
Among participants reclassified from low risk by the risk score to intermediate or high risk by the result of the CAC analysis, only 3.6% to 14.5% had a cardiovascular event during follow-up, while 85.5 96.4% did not. Among participants reclassified from intermediate or high risk by the risk score to low risk by the CAC analysis result, 91.4-99.2% had no cardiovascular event during follow-up, while 0 .8 to 8.6% had a cardiovascular event.
In summary, our study suggests that CAC analyzes offer a small improvement in risk prediction based on traditional CVD risk assessment equations. These potential (but uncertain) benefits must be weighed against the potential harms resulting from unnecessary testing, diagnosis and treatment, radiation risk and costs. An additional consideration is that scans have a large carbon footprint and their indiscriminate use contributes to healthcare emissions.
CAC scans may play a role in improving risk assessment in some patients. This possible role is suggested by modest improvement in discrimination (C statistic) and evidence of prognostic value with hazard ratios ranging from 1.29 to 1.75 per standard deviation, after adjusting for other CVD risk factors. .
The groups most likely to benefit are patients for whom, after the standard cardiovascular risk score assessment, there is a reasonable likelihood that a CAC analysis could help “clarify whether the risk is high enough to warrant primary prevention drugs”. Further refinement of the incremental gains assessment will likely require individual patient data to define who may benefit from a CAC analysis. Methods are needed to avoid using strata defined by CVD risk equations to assess incremental gain, because such stratified analysis is seriously flawed.
Currently, there is no direct evidence that adding CAC scores to traditional risk scores provides clinical benefit.
Katy Bell is a clinical epidemiologist and health services researcher at the University of Sydney School of Public Health. She has expertise in evaluating the clinical effectiveness of health care and her research aims to identify sustainable models of health care that benefit health and do no harm.
Anna Mae Scott is an Assistant Professor in the Institute of Evidence-Based Healthcare at Bond University. She is an epidemiologist with an interest in conducting and developing new methodologies for evidence syntheses.
Dr Lin Zhu is a statistician and postdoctoral researcher at the University of Sydney School of Public Health.
Paul Glasziou is Professor of Evidence-Based Medicine at Bond University and was a part-time general practitioner for many years. His primary interests include identifying and removing barriers to the use of high quality research in daily clinical practice and improving the research process through automated reviews and better reporting.
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