Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Department of Vascular Surgery, University Medical Center Utrecht, Utrecht, the Netherlands.
Br J Surg. 2021 Aug 19;108(8):960-967. doi: 10.1093/bjs/znab040.
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.
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.
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.
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.
下肢动脉疾病（LEAD）患者无症状颈动脉狭窄（ACS）筛查的推荐意见存在冲突。预测模型可能会识别出 ACS 高危患者，从而可能通过针对性筛查来改善预防性治疗和依从性。
系统检索了至少 50%的 LEAD 患者 ACS 的预测模型。随后，从美国接受踝臂血压指数（ABI）检测为 0.9 或更低的筛查患者中开发并评估了预测模型的区分度和校准度。在来自英国和荷兰的两个独立队列中进行了外部验证。
经过筛选 4907 项研究，未发现先前发表的预测模型。为了开发新模型，使用了 112117 名患者的数据，其中 6354 名（5.7%）患者至少有 50%的 ACS，2801 名（2.5%）患者至少有 70%的 ACS。年龄、性别、吸烟状况、高胆固醇血症史、卒中和短暂性脑缺血发作、冠心病和测量的收缩压是 ACS 的预测因素。该模型的区分度在接受者操作特征（ROC）曲线下面积（AUROC）为 0.71（95%可信区间 0.71 至 0.72），用于预测至少 50%的 ACS，0.73（0.72 至 0.73）用于预测至少 70%的 ACS。对风险最高的 20%患者进行筛查，可检出至少 50%ACS 的患者占 12.4%（所需筛查人数（NNS）为 8），检出至少 70%ACS 的患者占 5.8%（NNS 为 17）。这分别使至少 50%和 70%ACS 的患者比例达到 44.2%和 46.9%。外部验证显示了可靠的区分度和适当的校准度。
目前的风险评分可以预测 LEAD 患者的 ACS。这种方法可能有助于对高危人群进行有针对性的筛查，以提高 ACS 的检出率。
