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.

中文翻译：

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下肢动脉疾病患者无症状性颈动脉狭窄的检测：风险评分的发展和外部验证。

背景技术关于筛查下肢动脉疾病（LEAD）患者以检测无症状性颈动脉狭窄（ACS）的建议相互矛盾。预测模型可能会识别出患有ACS的高风险患者，可能允许进行有针对性的筛查以改善预防性治疗和依从性。方法对LEAD患者中至少50％ACS的预测模型进行了系统搜索。随后在美国筛查的踝关节：肱动脉压指数等于或小于0.9的患者中建立了预测模型，并进行了判别和校准评估。外部验证是在来自英国和荷兰的两个独立队列中进行的。结果在筛选了4907个研究后，未发现以前发表的预测模型。为了开发新模型，使用112 117例患者的数据，其中6354例（5.7％）的ACS至少为50％，2801例（2.5％）的ACS至少为70％。年龄，性别，吸烟状况，高胆固醇血症病史，中风/短暂性脑缺血发作，冠心病和测得的收缩压是ACS的预测指标。对于至少50％的ACS，模型判别在接收器工作特性（AUROC）曲线下的面积为0.71（95％ci 0.71至0.72），对于至少70％的ACS，模型判别面积为0.73（0.72至0.73）。筛查风险最高的20％的患者，使用ACS至少为50％时检测到12.4％（需要筛查的人数（NNS）8]，使用ACS至少为70％时检测到5.8％的患者（NNS 17），结果为44.2。分别有至少50％和70％的ACS的患者占46.9％。外部验证显示出可靠的辨别力和足够的校准。结论目前的危险评分可以预测LEAD患者的ACS。这种方法可以指导对高危个体进行有针对性的筛查，以增强对ACS的检测。