Zhou Zhuo-Hua, Cheng Xue-Ru, Guan Jia-Xin, Zhao Lu, Wang Yan-Ling, Wang Jia-Lin
From the Department of Ophthalmology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
From the Department of Ophthalmology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Am J Ophthalmol. 2025 Jun;274:91-100. doi: 10.1016/j.ajo.2025.02.034. Epub 2025 Mar 1.
Ischemic stroke is a cerebrovascular disease with high mortality and disability. Due to similar physiological characteristics, ocular vascular characteristics are important indicators for monitoring cerebrovascular diseases. This study aimed to develop a nomogram prediction model for ischemic stroke based on ocular hemodynamic characteristics.
Retrospective clinical cohort study.
A total of 337 patients were included in this study and randomly divided into 235 training and 102 validation cohorts. The general data were collected, and the hemodynamic parameters of ophthalmic artery, central retinal artery and posterior ciliary artery were detected by ultrasound. The retinal vascular diameter was extracted from the color fundus image, and the relevant laboratory indexes of the patients were collected. Logistic regression analysis was used to determine the risk factors of ischemic stroke. A nomogram was constructed based on the identified risk factors, and the accuracy and clinical applicability of the model were analyzed using the receiver operating curve (ROC), Hosmer-Lemeshow test, and decision curve analysis (DCA).
Independent risk factors for ischemic stroke including hypertension (OR 2.17, 95% confidence interval [CI] 1.16 to 4.08; P = .016), hyperlipidemia (OR 2.21, 95% CI 1.18 to 4.14; P = .013), and resistance index of ophthalmic artery (OR 5.98, 95% CI 3.27 to 10.93; P < .001) were identified by multivariate regression analysis. The area under the ROC curve of the training cohort was 0.790 (95% CI 0.733 to 0.847) and that of the validation cohort was 0.773 (95% CI 0.679 to 0.866), revealing the consistent ability of the nomogram to predict ischemic stroke. The mean absolute error of the training and validation cohorts were 0.020 and 0.013, respectively. In addition, the DCA curve showed good clinical benefit.
The nomogram combining traditional factors and ophthalmic artery resistance index has a preferable predictive performance for ischemic stroke. This suggests that the model combined with ocular hemodynamics can effectively promote the early diagnosis and intervention of ischemic stroke.
缺血性中风是一种具有高死亡率和高致残率的脑血管疾病。由于生理特征相似，眼部血管特征是监测脑血管疾病的重要指标。本研究旨在基于眼部血流动力学特征建立缺血性中风的列线图预测模型。
回顾性临床队列研究。
本研究共纳入337例患者，随机分为235例训练队列和102例验证队列。收集一般资料，采用超声检测眼动脉、视网膜中央动脉和睫状后动脉的血流动力学参数。从彩色眼底图像中提取视网膜血管直径，并收集患者的相关实验室指标。采用Logistic回归分析确定缺血性中风的危险因素。基于确定的危险因素构建列线图，并使用受试者工作特征曲线（ROC）、Hosmer-Lemeshow检验和决策曲线分析（DCA）分析模型的准确性和临床适用性。
多因素回归分析确定缺血性中风的独立危险因素包括高血压（比值比[OR]2.17，95%置信区间[CI]1.16至4.08；P = 0.016）、高脂血症（OR 2.21，95%CI 1.18至4.14；P = 0.013）和眼动脉阻力指数（OR 5.98，95%CI 3.27至10.93；P < 0.001）。训练队列的ROC曲线下面积为0.790（95%CI 0.733至0.847），验证队列的ROC曲线下面积为0.773（95%CI 0.679至0.866），表明列线图预测缺血性中风的能力一致。训练队列和验证队列的平均绝对误差分别为0.020和0.013。此外，DCA曲线显示出良好的临床效益。
结合传统因素和眼动脉阻力指数的列线图对缺血性中风具有较好的预测性能。这表明结合眼部血流动力学的模型可以有效促进缺血性中风的早期诊断和干预。
