Huang Jian, Li Zhuoran, Liu Xiaozhu, Kuang Lirong, Peng Shengxian
Scientific Research Department, First People's Hospital of Zigong City, Zigong, China.
Department of Ultrasound, Sir Run Run Shaw Hospital, Zhejiang University College of Medicine, Hangzhou, China.
Front Neurol. 2025 Mar 24;16:1565395. doi: 10.3389/fneur.2025.1565395. eCollection 2025.
Delays in diagnosing severe carotid artery stenosis (CAS) are prevalent, particularly in low-income regions with limited access to imaging examinations. CAS is a major contributor to the recurrence and poor prognosis of ischemic stroke (IS). This retrospective cohort study proposed a non-invasive dynamic prediction model to identify potential high-risk severe carotid artery stenosis in patients with ischemic stroke.
From July 2017 to March 2021, 739 patients with ischemic stroke were retrospectively recruited from the Department of Neurology at Liuzhou Traditional Chinese Medical Hospital. Risk factors for severe CAS were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression (MLR) methods. The model was constructed after evaluating multicollinearity. The model's discrimination was assessed using the C-statistic and area under the curve (AUC). Its clinical utility was evaluated through the decision curve analysis (DCA) and the clinical impact curve (CIC). Calibration was examined using a calibration plot. To provide individualized predictions, a web-based tool was developed to estimate the risk of severe CAS.
Among the patients, 488 of 739 (66.0%) were diagnosed with severe CAS. Six variables were incorporated into the final model: history of stroke, serum sodium, hypersensitive C-reactive protein (hsCRP), C-reactive protein (CRP), basophil percentage, and mean corpuscular hemoglobin concentration (MCHC). Multicollinearity was ruled out through correlation plots, variance inflation factor (VIF) values, and tolerance values. The model demonstrated good discrimination, with a C-statistic/AUC of 0.70 in the test set. The DCA and CIC indicated that clinical decisions based on the model could benefit IS patients. The calibration plot showed strong concordance between predicted and observed probabilities. The web-based prediction model exhibited robust performance in estimating the risk of severe CAS.
This study identified six key risk factors for severe CAS in IS patients. In addition, we developed a web-based dynamic nomogram to predict the individual risk of severe CAS. This tool can potentially support tailored, risk-based, and time-sensitive treatment strategies.
严重颈动脉狭窄（CAS）的诊断延误很常见，尤其是在难以获得影像检查的低收入地区。CAS是缺血性卒中（IS）复发和预后不良的主要原因。这项回顾性队列研究提出了一种非侵入性动态预测模型，以识别缺血性卒中患者潜在的高危严重颈动脉狭窄。
2017年7月至2021年3月，从柳州市中医医院神经内科回顾性招募了739例缺血性卒中患者。使用最小绝对收缩和选择算子（LASSO）和多变量逻辑回归（MLR）方法确定严重CAS的危险因素。在评估多重共线性后构建模型。使用C统计量和曲线下面积（AUC）评估模型的辨别力。通过决策曲线分析（DCA）和临床影响曲线（CIC）评估其临床效用。使用校准图检查校准情况。为提供个性化预测，开发了一个基于网络的工具来估计严重CAS的风险。
在这些患者中，739例中有488例（66.0%）被诊断为严重CAS。六个变量被纳入最终模型：卒中史、血清钠、超敏C反应蛋白（hsCRP）、C反应蛋白（CRP）、嗜碱性粒细胞百分比和平均红细胞血红蛋白浓度（MCHC）。通过相关图、方差膨胀因子（VIF）值和容忍度值排除了多重共线性。该模型显示出良好的辨别力，测试集中的C统计量/AUC为0.70。DCA和CIC表明，基于该模型的临床决策可能使IS患者受益。校准图显示预测概率与观察概率之间有很强的一致性。基于网络的预测模型在估计严重CAS风险方面表现出强大的性能。
本研究确定了IS患者严重CAS的六个关键危险因素。此外，我们开发了一个基于网络的动态列线图来预测严重CAS的个体风险。该工具可能有助于支持量身定制的、基于风险的和对时间敏感的治疗策略。
