Development and Internal Validation of a Risk Score to Detect Asymptomatic Carotid Stenosis

Abstract

Objective Asymptomatic carotid stenosis (ACS) is associated with an increased risk of ischaemic stroke and myocardial infarction. Risk scores have been developed to detect individuals at high risk of ACS, thereby enabling targeted screening, but previous external validation showed scope for refinement of prediction by adding additional predictors. The aim of this study was to develop a novel risk score in a large contemporary screened population. Methods A prediction model was developed for moderate (≥50%) and severe (≥70%) ACS using data from 596 469 individuals who attended screening clinics. Variables that predicted the presence of ≥50% and ≥70% ACS independently were determined using multivariable logistic regression. Internal validation was performed using bootstrapping techniques. Discrimination was assessed using area under the receiver operating characteristic curves (AUROCs) and agreement between predicted and observed cases using calibration plots. Results Predictors of ≥50% and ≥70% ACS were age, sex, current smoking, diabetes mellitus, prior stroke/transient ischaemic attack, coronary artery disease, peripheral arterial disease, blood pressure, and blood lipids. Models discriminated between participants with and without ACS reliably, with an AUROC of 0.78 (95% confidence interval [CI] 0.77–0.78) for ≥ 50% ACS and 0.82 (95% CI 0.81–0.82) for ≥ 70% ACS. The number needed to screen in the highest decile of predicted risk to detect one case with ≥50% ACS was 13 and that of ≥70% ACS was 58. Targeted screening of the highest decile identified 41% of cases with ≥50% ACS and 51% with ≥70% ACS. Conclusion The novel risk model predicted the prevalence of ACS reliably and performed better than previous models. Targeted screening among the highest decile of predicted risk identified around 40% of all cases with ≥50% ACS. Initiation or intensification of cardiovascular risk management in detected cases might help to reduce both carotid related ischaemic strokes and myocardial infarctions.

Interleukin-6 Predicts Carotid Plaque Severity, Vulnerability, and Progression

“…Selection of independent variables to include in the multivariable models was based on available evidence of association with atherosclerosis. 24,25 Considering our previous work suggesting that the presence of high-risk features is not dependent on plaque severity, we did not include plaque severity in the model to predict baseline plaque vulnerability. 15 However, we considered the baseline ipsilateral plaque severity score and the baseline ipsilateral plaque vulnerability in the logistic regression model to predict plaque progression at 5 years and explored the interaction with log IL-6.…”

Why do guidelines recommend screening for abdominal aortic aneurysms, but not for asymptomatic carotid stenosis? A plea for a randomized controlled trial

“…A more recent report introduced a new risk score, the Prevalence of Asymptomatic Carotid Artery Stenosis (PACAS) risk score, which also included important predictors of AsxCS, such as peripheral arterial disease [48]. This risk score identified age, sex, current smoking, diabetes mellitus, prior stroke/TIA, coronary artery disease, peripheral arterial disease, blood pressure and blood lipids as predictors of ≥50% and ≥ 70% AsxCS [48]. The NNS in the highest decile of predicted risk to detect one individual with ≥50% was 13 and for ≥70% it was 58 [48].…”

The US Preventive Services Task Force Recommendation Statement About Screening Asymptomatic Adults for Carotid Stenosis

“…The USPSTF recommendation statement identifies the need for development and validation of risk prediction models to identify a population at high risk of asymptomatic carotid artery stenosis. However, recent studies have published risk prediction models, with external validation in an independent population and determination of the best-performing model . A risk prediction model that includes age, sex, vascular risk factors, and previous vascular disease as predictors can reliably predict the presence of asymptomatic carotid artery stenosis .…”

“…However, recent studies have published risk prediction models, with external validation in an independent population and determination of the best-performing model . A risk prediction model that includes age, sex, vascular risk factors, and previous vascular disease as predictors can reliably predict the presence of asymptomatic carotid artery stenosis . Using this risk prediction model to target screening participants at the 10% highest predicted risk instead of screening all participants, the number needed to screen was reduced from 53 to 13 people to detect 1 participant with greater than 50% asymptomatic carotid artery stenosis and from 294 to 57 people to detect 1 participant with greater than 70% asymptomatic carotid artery stenosis.…”

“…Using this risk prediction model to target screening participants at the 10% highest predicted risk instead of screening all participants, the number needed to screen was reduced from 53 to 13 people to detect 1 participant with greater than 50% asymptomatic carotid artery stenosis and from 294 to 57 people to detect 1 participant with greater than 70% asymptomatic carotid artery stenosis. Targeted screening of those in the highest 10% of predicted risk identified 41% of participants with greater than 50% and 51% of participants with greater than 70% asymptomatic carotid artery stenosis …”