摘要
目的 构建卒中后癫痫发作预测模型,探讨导致卒中后癫痫发作的危险因素.方法 回顾性分析中江县人民医院2020年7月-2022年9月入院的符合纳排标准的1 741例卒中患者,随访卒中发生后1年内是否发生了卒中后癫痫发作.记录患者性别、年龄、诊断、美国国立卫生研究院卒中量表(National institute of health stroke scale,NIHSS)评分、日常生活活动(Activity of daily living,ADL)评分、检验、影像检查数据.以是否发生卒中后癫痫发作为结果,对上述数据进行分析.采用最小绝对收缩和选择算子(Least absolute shrinkage and selection operator,LASSO)回归分析筛选预测变量,进行多因素Logistic回归分析.按7∶3的比例将数据随机拆分为训练集与验证集,并构建模型、计算C指数、绘制列线图、校准图、受试者工作特征曲线、决策曲线,进而评估模型的性能及临床应用价值.结果 LASSO回归筛选得到了 NIHSS评分、同型半胱氨酸(Homocysteine,Hcy)、天门冬氨酸氨基转移酶(Aspartate aminotransferase,AST)、血小板计数、高尿酸血症、低钠血症、额叶病灶、颞叶病灶、桥脑病灶9个系数非零的预测变量.多变量逻辑回归分析显示,NIHSS评分、Hcy、高尿酸血症、低钠血症、桥脑病灶与卒中后癫痫发作呈正相关,AST、血小板计数与卒中后癫痫发作呈负相关.建立了用于卒中后癫痫发作预测的列线图.训练集与验证集 C 指数分别为 0.854[95％CI(0.841,0.947)]、0.838[95％CI(0.800,0.988)],ROC 曲线下面积分别为0.842[95％CI(0.777,0.899)]、0.829[95％CI(0.694,0.936)].结论 这9个变量有可能用于卒中后癫痫发作的预测,同时,提供了关于其危险因素的新见解.
Abstract
Objective Constructing a prediction model for seizures after stroke,and exploring the risk factors that lead to seizures after stroke.Methods A retrospective analysis was conducted on 1 741 patients with stroke admitted to People's Hospital of Zhongjiang from July 2020 to September 2022 who met the inclusion and exclusion criteria.These patients were followed up for one year after the occurrence of stroke to observe whether they experienced seizures.Patient data such as gender,age,diagnosis,National Institute of Health Stroke Scale(NIHSS)score,Activity of daily living(ADL)score,laboratory tests,and imaging examination data were recorded.Taking the occurrence of seizures as the outcome,an analysis was conducted on the above data.The Least absolute shrinkage and selection operator(LASSO)regression analysis was used to screen predictive variables,and multivariate Logistic regression analysis was performed.Subsequently,the data were randomly divided into a training set and a validation set in a 7∶3 ratio.Construct prediction model,calculate the C-index,draw nomogram,calibration plot,receiver operating characteristic(ROC)curve,and decision curve analysis(DCA)to evaluate the model's performance and clinical application value.Results Through LASSO regression,nine non-zero coefficient predictive variables were identified:NIHSS score,homocysteine(Hcy),aspartate aminotransferase(AST),platelet count,hyperuricemia,hyponatremia,frontal lobe lesions,temporal lobe lesions,and pons lesions.Multivariate logistic regression analysis revealed that NIHSS score,Hcy,hyperuricemia,hyponatremia,and pons lesions were positively correlated with seizures after stroke,while AST and platelet count were negatively correlated with seizures after stroke.A nomogram for predicting seizures after stroke was established.The C-index of the training set and validation set were 0.854[95％CI(0.841,0.947)]and 0.838[95％CI(0.800,0.988)],respectively.The areas under the ROC curves were 0.842[95％CI(0.777,0.899)]and 0.829[95％CI(0.694,0.936)]respectively.Conclusion These nine variables can be used to predict seizures after stroke,and they provide new insights into its risk factors.
基金项目
德阳市科学技术局2022年度德阳市科技计划(2022SCZ109)
NETL
NSTL
万方数据