Chen Shuliang, Ma Chunye, Zhang Ce, Shi Rui
Department of Neurology.
Clinical Drug Trial Institution, The Second Hospital of Dalian Medical University, Dalian, China.
Medicine (Baltimore). 2020 Sep 25;99(39):e22413. doi: 10.1097/MD.0000000000022413.
Stroke is the acute onset of neurological deficits and is associated with high morbidity, mortality, and disease burden. In the present study, we aimed to develop a scientific, nomogram for non-invasive predicting risk for early ischemic stroke, in order to improve stroke prevention efforts among high-risk groups. Data were obtained from a total of 2151 patients with early ischemic stroke from October 2017 to September 2018 and from 1527 healthy controls. Risk factors were examined using logistic regression analyses. Nomogram and receiver operating characteristic (ROC) curves were drawn, cutoff values were established. Significant risk factors for early ischemic stroke included age, sex, blood pressure, history of diabetes, history of genetic, history of coronary heart disease, history of smoking. A nomogram predicting ischemic stroke for all patients had an internally validated concordance index of 0.911. The area under the ROC curve for the logistic regression model was 0.782 (95% confidence interval [CI]: 0.766-0.799, P < .001), with a cutoff value of 2.5. The nomogram developed in this study can be used as a primary non-invasive prevention tool for early ischemic stroke and is expected to provide data support for the revision of current guidelines.
中风是神经功能缺损的急性发作，与高发病率、死亡率和疾病负担相关。在本研究中，我们旨在开发一种科学的列线图，用于无创预测早期缺血性中风的风险，以加强高危人群的中风预防工作。数据来自2017年10月至2018年9月共2151例早期缺血性中风患者以及1527名健康对照。使用逻辑回归分析检查危险因素。绘制列线图和受试者工作特征（ROC）曲线，确定临界值。早期缺血性中风的显著危险因素包括年龄、性别、血压、糖尿病史、遗传史、冠心病史、吸烟史。所有患者的缺血性中风预测列线图的内部验证一致性指数为0.911。逻辑回归模型的ROC曲线下面积为0.782（95%置信区间[CI]：0.766 - 0.799，P＜0.001），临界值为2.5。本研究开发的列线图可作为早期缺血性中风的主要无创预防工具，有望为现行指南的修订提供数据支持。
