Detection of asymptomatic carotid stenosis in patients with lower-extremity arterial disease: development and external validations of a risk score

Abstract

Abstract Background Recommendations for screening patients with lower-extremity arterial disease (LEAD) to detect asymptomatic carotid stenosis (ACS) are conflicting. Prediction models might identify patients at high risk of ACS, possibly allowing targeted screening to improve preventive therapy and compliance. Methods A systematic search for prediction models for at least 50 per cent ACS in patients with LEAD was conducted. A prediction model in screened patients from the USA with an ankle : brachial pressure index of 0.9 or less was subsequently developed, and assessed for discrimination and calibration. External validation was performed in two independent cohorts, from the UK and the Netherlands. Results After screening 4907 studies, no previously published prediction models were found. For development of a new model, data for 112 117 patients were used, of whom 6354 (5.7 per cent) had at least 50 per cent ACS and 2801 (2.5 per cent) had at least 70 per cent ACS. Age, sex, smoking status, history of hypercholesterolaemia, stroke/transient ischaemic attack, coronary heart disease and measured systolic BP were predictors of ACS. The model discrimination had an area under the receiver operating characteristic (AUROC) curve of 0.71 (95 per cent c.i. 0.71 to 0.72) for at least 50 per cent ACS and 0.73 (0.72 to 0.73) for at least 70 per cent ACS. Screening the 20 per cent of patients at greatest risk detected 12.4 per cent with at least 50 per cent ACS (number needed to screen (NNS) 8] and 5.8 per cent with at least 70 per cent ACS (NNS 17). This yielded 44.2 and 46.9 per cent of patients with at least 50 and 70 per cent ACS respectively. External validation showed reliable discrimination and adequate calibration. Conclusion The present risk score can predict significant ACS in patients with LEAD. This approach may inform targeted screening of high-risk individuals to enhance the detection of ACS.