Data_Sheet_1_HR-MRI-based nomogram network calculator to predict stroke recurrence in high-risk non-disabling ischemic cerebrovascular events patients.docx

Background and objectiveTo investigate the use of high-resolution magnetic resonance imaging (HR-MRI) to identify the characteristics of culprit plaques in intracranial arteries, and to evaluate the predictive value of the characteristics of culprit plaques combined with the modified Essen score for the recurrence risk of high-risk non-disabling ischemic cerebrovascular events (HR-NICE) patients.
MethodsA retrospective analysis was conducted on 180 patients with HR-NICE at the First Affiliated Hospital of Xinxiang Medical University, including 128 patients with no recurrence (non-recurrence group) and 52 patients with recurrence (recurrence group). A total of 65 patients with HR-NICE were collected from the Sixth Affiliated Hospital of Shanghai Jiaotong University as a validation group, and their modified Essen scores, high-resolution magnetic resonance vessel wall images, and clinical data were collected. The culprit plaques were analyzed using VesselExplorer2 software. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for recurrence, and a nomogram was constructed using R software to evaluate the discrimination of the model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to evaluate the model performance. Calibration curves and Decision Curve Analysis (DCA) were used to evaluate the model efficacy.
ResultsIntra-plaque hemorrhage (OR = 3.592, 95% CI = 1.474–9.104, p = 0.006), homocysteine (OR = 1.098, 95% CI = 1.025–1.179, p = 0.007), and normalized wall index (OR = 1.114, 95% CI = 1.027–1.222, p = 0.015) were significantly higher in the recurrent stroke group than in the non-recurrent stroke group, and were independent risk factors for recurrent stroke. The performance of the nomogram model (AUC = 0.830, 95% CI: 0.769–0.891; PR-AUC = 0.628) was better than that of the modified Essen scoring model (AUC = 0.660, 95% CI: 0.583–0.738) and the independent risk factor combination model (AUC = 0.827, 95% CI: 0.765–0.889). The nomogram model still had good model performance in the validation group (AUC = 0.785, 95% CI: 0.671–0.899), with a well-fitting calibration curve and a DCA curve indicating good net benefit efficacy for patients.
ConclusionHigh-resolution vessel wall imaging combined with a modified Essen score can effectively assess the recurrence risk of HR-NICE patients, and the nomogram model can provide a reference for identifying high-risk populations with good clinical application prospects.

**背景与目的**：本研究旨在探讨高分辨率磁共振成像（high-resolution magnetic resonance imaging, HR-MRI）识别颅内动脉罪犯斑块特征的应用价值，并评估罪犯斑块特征联合改良Essen评分对高危非致残性缺血性脑血管事件（high-risk non-disabling ischemic cerebrovascular events, HR-NICE）患者复发风险的预测价值。
**研究方法**：本研究回顾性分析新乡医学院第一附属医院收治的180例HR-NICE患者，其中无复发患者128例（无复发组），复发患者52例（复发组）；另收集上海交通大学附属第六人民医院的65例HR-NICE患者作为验证组，采集其改良Essen评分、高分辨率磁共振血管壁图像及临床资料。采用VesselExplorer2软件对罪犯斑块进行分析，通过单因素及多因素logistic回归筛选复发的独立危险因素，利用R软件构建列线图（nomogram）模型以评估其区分度；采用受试者工作特征曲线（receiver operating characteristic curve, ROC）下面积（area under the curve, AUC）评估模型性能，通过校准曲线与决策曲线分析（Decision Curve Analysis, DCA）评估模型临床获益效能。
**研究结果**：复发卒中组的斑块内出血（OR=3.592，95%CI：1.474~9.104，p=0.006）、同型半胱氨酸（OR=1.098，95%CI：1.025~1.179，p=0.007）及管壁标准化指数（normalized wall index, NWI）均显著高于无复发卒中组，且均为卒中复发的独立危险因素。本研究构建的列线图模型（AUC=0.830，95%CI：0.769~0.891；PR-AUC=0.628）性能优于改良Essen评分模型（AUC=0.660，95%CI：0.583~0.738）与独立危险因素联合模型（AUC=0.827，95%CI：0.765~0.889）。该列线图模型在验证组中仍表现出良好性能（AUC=0.785，95%CI：0.671~0.899），校准曲线拟合良好，决策曲线分析结果显示其可为患者带来良好的净临床获益。
**结论**：高分辨率血管壁成像联合改良Essen评分可有效评估HR-NICE患者的复发风险，所构建的列线图模型可为高危人群筛查提供参考，具有良好的临床应用前景。