摘要
目的基于最小绝对收缩和选择算子(Lasso)回归构建颅内静脉和静脉窦血栓形成(CVST)预后的预测模型并验证其效能。方法回顾性分析2012年12月至2023年5月重庆医科大学附属第一医院神经外科收治的98例CVST患者的临床资料。患者的预后通过改良Rankin量表评分(mRS)评估。对比预后良好组(mRS<3分,81例)与预后不良组患者(mRS≥3分,17例)的基线资料、首发症状、入院时根据颅内静脉和硬脑膜窦血栓形成的国际研究建立的风险评分(ISCVT-RS)、入院时颅内静脉血栓形成分级量表(CVT-GS)、入院时格拉斯哥昏迷评分(GCS)、治疗方案、出院时mRS、影像学资料及实验室检查资料。应用二元logistic回归筛选出预测因子构建预后预测模型1,再应用Lasso回归筛选出预测因子构建预测模型2。运用受试者工作特征曲线(ROC)和曲线下面积(AUC)评估两种预测模型的效能。校准曲线用于评估预测模型的校准度。采用Bootstrap自抽样方法对模型进行内部验证。采用决策曲线分析(DCA)方法评估预测模型的临床获益。结果与预后良好组比较,预后不良组意识障碍、肢体乏力、脑实质出血、静脉性梗死、乙状窦血栓形成、横窦血栓形成及颈内静脉血栓形成的患者占比均较高,入院时ISCVT-RS、CVT-GS评分、GCS、外周血白细胞计数、中性粒细胞计数、血糖浓度、D-二聚体均较高,而淋巴细胞计数和血清白蛋白浓度均较低,差异均具有统计学意义(均P<0.05)。多因素logistic回归分析显示,肢体乏力(OR=9.40,95%CI:1.13~78.42)、外周血中性粒细胞计数升高(OR=1.70,95%CI:1.18~2.44)和血糖浓度升高(OR=1.36,95%CI:1.03~1.79)为CVST患者预后的危险因素,而外周血淋巴细胞计数升高(OR=0.05,95%CI:0.01~0.29)以及血清白蛋白浓度升高(OR=0.82,95%CI:0.70~0.96)为CVST患者预后的保护因素,基于以上5个预测因子构建预测模型1。预测模型2基于静脉性梗死、外周血中
Objective To construct a prediction model for the prognosis of cerebral venous and dural sinus thrombosis(CVST)based on least absolute shrinkage and selection operator(Lasso)regression and to verify its effectiveness.Methods The clinical data of 98 CVST patients admitted to the Neurosurgery Department of the First Affiliated Hospital of Chongqing Medical University from December 2012 to May 2023 were retrospectively analyzed.The patients’outcomes were assessed based on the modified Rankin Scale(mRS).We compared the baseline data,symptoms at onset,and symptoms on admission of patients in the good prognosis group(mRS<3 points,81 cases)and the poor prognosis group(mRS≥3 points,17 cases)based on the International Study on Cerebral Vein and Dural Sinus Thrombosis-Risk Score(ISCVT-RS),cerebral venous and dural sinus thrombosis grading scale(CVT-GS)on admission,Glosgow Coma Scale(GCS)score at admission,treatment plan,mRS score on discharge,imaging data and laboratory test data.Binary logistic regression was used to screen out predictors to build a prognosis prediction model 1,and then Lasso regression was used to screen out predictors to build a prediction model 2.The receiver operating characteristic curve(ROC)and area under the curve(AUC)were used to evaluate and compare the performance of the two prediction models.Calibration curves are used to evaluate the calibration of a predictive model.The model was internally verified using Bootstrap self-sampling method.Decision curve analysis(DCA)was used to evaluate the clinical benefit of predictive models.Results Compared with the good prognosis group,the poor prognosis group had higher proportions of patients with consciousness disorder,limb weakness,cerebral parenchymal hemorrhage,venous infarction,sigmoid sinus thrombosis,transverse sinus thrombosis and internal jugular vein thrombosis,and ISCVT-RS score on admission,CVT-GS score,GCS,white blood cell count,neutrophil count,blood glucose concentration,and D-dimer were all increased,while lymphocyte count and serum al
作者
张家淳
郑鉴峰
李钢
李静
陈桂虎
郭宗铎
孙晓川
Zhang Jiachun;Zheng Jianfeng;Li Gang;Li Jing;Chen Guihu;Guo Zongduo;Sun Xiaochuan(Department of Neurosurgery,the First Affiliated Hospital of Chongqing Medical University,Chongqing 400016,China)
出处
《中华神经外科杂志》
CSCD
北大核心
2024年第5期492-499,共8页
Chinese Journal of Neurosurgery
基金
国家自然科学基金(82071397)。
关键词
窦血栓形成
颅内
静脉血栓形成
预后
比例危险度模型
列线图
Sinus thrombosis,intracranial
Venous thrombosis
Prognosis
Proportional hazards models
Nomogram
