Jining Medical University, Shandong, China.
Women's & Children's health care hospital of LinYi, LinYi, China.
Clin Neuroradiol. 2023 Jun;33(2):519-528. doi: 10.1007/s00062-022-01241-3. Epub 2022 Dec 15.
Acute large vessel occlusion due to underlying intracranial atherosclerotic stenosis (ICAS-LVO) increases the difficulty of revascularization, resulting in frequent re-occlusion. The establishment of its pathogenesis before endovascular treatment (EVT) is beneficial for patients. We aimed at developing and validating a clinical prediction model for ICAS-LVO patients before EVT.
Patients with acute large vessel occlusion at Jining No. 1 People's Hospital from January 2019 to September 2021 were retrospectively included as the training cohort. The 70 patients who met the inclusion and exclusion criteria were included in the validation cohort (October 2021 to May 2022). Demographics, onset form, medical history, digital subtraction angiography (DSA) imaging data, and laboratory test data were collected. Preprocedural parameters for the ICAS-LVO risk prediction model were established by stepwise logistic regression controlling for the confounding effects. Then, we constructed a nomogram model and evaluated its performance via the Hosmer-Lemeshow goodness-of-fit test, area under the ROC curve (AUC) analysis.
The 231 acute LVO patients were included in the final analysis, 74 (32.3%) patients were ICAS-LVO. A preoperative diagnosis prediction model consisting of five predictors for ICAS-LVO, including fluctuating symptoms, NIHSS < 16, atrial fibrillation, tapered sign, and ASITN/SIR score ≥ 2. The model depicted an acceptable calibration (Hosmer-Lemeshow test, p = 0.451) and good discrimination (AUC, 0.941; 95% confidence interval, 0.910-0.971). The optimal cut-off value for the ICAS-LVO scale was 2 points, with 86.5% sensitivity, 91.1% specificity, and 90.5% accuracy. In the validation cohort, the discriminative ability was promising with an AUC value of 0.897, implying a good predictive performance.
The established ICAS-LVO scale, which is composed of five predictors: fluctuating symptoms, NIHSS < 16, atrial fibrillation, tapered sign, and ASITN/SIR score ≥ 2, has a good predictive value for ICAS-LVO in Chinese populations.
颅内动脉粥样硬化性狭窄（ICAS）导致的急性大血管闭塞（LVO）增加了再通的难度，导致频繁再闭塞。在血管内治疗（EVT）前建立其发病机制有利于患者。我们旨在为 EVT 前的 ICAS-LVO 患者开发和验证一个临床预测模型。
回顾性纳入 2019 年 1 月至 2021 年 9 月在济宁市第一人民医院就诊的急性大血管闭塞患者作为训练队列。纳入符合纳入和排除标准的 70 例患者作为验证队列（2021 年 10 月至 2022 年 5 月）。收集患者的人口统计学、发病形式、病史、数字减影血管造影（DSA）影像学数据和实验室检查数据。通过逐步逻辑回归控制混杂因素，建立 ICAS-LVO 风险预测模型的术前参数。然后，我们构建了一个列线图模型，并通过 Hosmer-Lemeshow 拟合优度检验、ROC 曲线下面积（AUC）分析来评估其性能。
最终分析纳入 231 例急性 LVO 患者，74 例（32.3%）为 ICAS-LVO。一个由五个预测因子组成的术前诊断预测模型，包括波动性症状、NIHSS<16、心房颤动、锥形征和 ASITN/SIR 评分≥2。该模型具有良好的校准度（Hosmer-Lemeshow 检验，p=0.451）和良好的鉴别力（AUC，0.941；95%置信区间，0.910-0.971）。ICAS-LVO 评分的最佳截断值为 2 分，具有 86.5%的敏感性、91.1%的特异性和 90.5%的准确性。在验证队列中，该模型具有良好的预测性能，AUC 值为 0.897。
该模型由五个预测因子组成：波动性症状、NIHSS<16、心房颤动、锥形征和 ASITN/SIR 评分≥2，在中国人群中对 ICAS-LVO 具有良好的预测价值。
