CARDIOVASCULAR deaths in Australia have seen an 82% relative reduction from their peak in 1968. Much of this decline has been due to widespread decreases in smoking, blood pressure and cholesterol levels in the population. Increased use of blood pressure-lowering and lipid-lowering therapy has also played a significant role.
Deaths and disability from cardiovascular disease (CVD) might be reduced even further by improved detection of people at high risk for treatment with blood pressure-lowering and lipid-lowering medication.
Our best current means to determine who is at high risk (and thus might 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 accurately. The Framingham risk equation, based on the Framingham Heart Study and the Framingham Offspring Study cohorts, accurately predicted CVD events in a recent Australian population study. Other risk equations such as QRISK3, developed for the UK population, and PREDICT, developed for the New Zealand population, also include health equity-associated risk factors, such as self-identified ethnicity and socio-economic status.
Many other new factors have been proposed as potentially useful additions to risk assessment. One prominent recent proposal has been coronary artery calcium (CAC) scans, which are increasingly used in clinical practice in Australia and overseas. However, it has been unclear how much information the scans add beyond that from traditional risk assessment using a CVD risk calculator.
To find out, we undertook a systematic review and meta-analysis of published studies that provided relevant data on the incremental value of CAC scans, beyond that provided by traditional cardiovascular risk scores. Using the 2018 US Preventive Services Task Force review on the topic as our starting point, we screened 2772 titles and abstracts, and 53 full text articles. We included six community-based cohort studies from the US, the Netherlands, Germany and South Korea, with CVD event rates ranging from 0.9% to 9.4% over 4–10 years’ follow-up.
The studies showed modest gain in discrimination when scans were added to the risk equation (ability of the models to separate those who did and did not have a CVD event), and this appeared consistent across studies. The C statistic (area under the receiver operating characteristic [ROC] curve) for the CVD risk models without CAC scan results ranged from 0.693 to 0.80. The pooled gain in C statistic from adding a CAC score was 0.036 overall, with possible larger gain for studies using coronary heart disease events as the outcome (gain in C statistic of 0.049) than for studies using CVD events (gain in C statistic, 0.029).
We also found that the test may have incremental prognostic value for some participants. But the clinical meaning 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 CAC scan result, only 3.6% to 14.5% had a CVD 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 CAC scan result, 91.4–99.2% did not have a CVD event during follow-up, while 0.8–8.6% did have a CVD event.
In summary, our study suggests that the CAC scans offer a small improvement to risk prediction based on traditional CVD risk assessment equations. These potential (but uncertain) benefits need to be weighed against potential harms from unnecessary tests, diagnoses and treatments, radiation risk, and costs. An additional consideration is that the scans have a large carbon footprint, and their indiscriminate use contributes to health care emissions.
CAC scans may have a role for refining risk assessment in selected patients. This possible role is suggested by modest improvement in discrimination (C statistic) and evidence of prognostic value with hazard ratios that ranged from 1.29 to 1.75 per standard deviation, after adjustment for other CVD risk factors.
The groups most likely to benefit are patients for whom, after standard CVD risk score assessment, reasonable likelihood exists that a CAC scan could help “in clarifying whether the risk is high enough to justify primary prevention medications”. Further refinement to assessing the incremental gains is likely to need individual patient data to define who may benefit from a CAC scan. Methods are needed that avoid using strata defined by CVD risk equations to assess the incremental gain, because such stratified analysis is seriously flawed.
At present, there is no direct evidence that adding the CAC scores to traditional risk scores provides clinical benefit.
Katy Bell is a clinical epidemiologist and health services researcher in the School of Public Health at the University of Sydney. She has expertise in the evaluation of the clinical effectiveness of health care, and her research aims to identify sustainable models of health care that benefit health and not cause harm.
Anna Mae Scott is an Assistant Professor at the Institute of Evidence-Based Healthcare at Bond University. She is an epidemiologist, with interest in conducting and developing new methodology for evidence syntheses.
Dr Lin Zhu is a statistician and postdoctoral researcher in the School of Public Health at the University of Sydney.
Paul Glasziou is Professor of Evidence-Based Medicine at Bond University was a part-time General Practitioner for many years. His key interests include identifying and removing the barriers to using high quality research in everyday clinical practice, and improving the process of research through automated reviews and better reporting.
The statements or opinions expressed in this article reflect the views of the authors and do not necessarily represent the official policy of the AMA, the MJA or InSight+ unless so stated.
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