基于临床和影像学的大血管闭塞性卒中病因学分型的列线图预测模型的建立  

Establishment of a nomogram prediction model for the etiological type of large vessel occlusive stroke based on clinical and imaging parameters

作　　者：李玲 曹若瑶 逯瑶 蒋云 祁鹏 王国选 于克祯 陈涓 Li Ling;Cao Ruoyao;Lu Yao;Jiang Yun;Qi Peng;Wang Guoxuan;Yu Kezhen;Chen Juan(Department of Radiology,Beijing Shijitan Hospital,Capital Medical University,Beijing 100038,China;Department of Radiology,Beijing Hospital,National Center of Gerontology,Institute of Geriatric Medicine,Chinese Academy of Medical Science,Beijing 100730,China;Graduate School of Peking Union Medical College,Chinese Academy of Medical Sciences,Beijing 100730,China;Department of Neurology,Beijing Hospital,National Center of Gerontology,Institute of Geriatric Medicine,Chinese Academy of Medical Sciences,Beijing 100730,China;Department of Neurosurgery,Beijing Hospital,National Center of Gerontology,Institute of Geriatric Medicine,Chinese Academy of Medical Sciences,Beijing 100730,China)

机构地区：[1]首都医科大学附属北京世纪坛医院放射科,北京100038 [2]国家老年医学中心,中国医学科学院老年医学研究院,北京医院放射科,北京100730 [3]中国医学科学院北京协和医学院研究生院,北京100730 [4]国家老年医学中心,中国医学科学院老年医学研究院,北京医院神经内科,北京100730 [5]国家老年医学中心,中国医学科学院老年医学研究院,北京医院神经外科,北京100730

出　　处：《国际脑血管病杂志》2023年第6期409-417,共9页International Journal of Cerebrovascular Diseases

基　　金：中央高校基本科研业务费专项资金资助(3332022168);中华国际医学交流基金会“2020SKY影像科研基金”(Z-2014-07-2003-02);北京医院国家自然科学基金预研专项(BJ-2020-131);北京医院临床研究“助航”专项(BJ-2023-076)。

摘　　要：目的构建基于临床及影像学参数的预测急性缺血性卒中(acute ischemic stroke,AIS)病因分型的列线图模型。方法回顾性纳入2016年3月至2021年12月在北京医院接受血管内治疗的AIS患者,根据其血栓性质分为大动脉粥样硬化(large artery atherosclerosis,LAA)和心源性栓塞(cardioembolism,CE)。通过LASSO回归筛选与病因分型最相关的临床和影像学参数,并通过多变量logistic回归建立预测AIS病因分型的列线图预测模型,探讨相关临床和影像学参数在分型中的预测价值。通过受试者工作特征曲线、校准曲线和临床决策曲线评估预测模型的诊断效能。结果共纳入136例接受血管内治疗的前循环大血管闭塞AIS患者,其中CE 62例(45.6%),LAA 74例(54.4%)。将单变量分析中P<0.10的变量纳入LASSO回归以筛选相关变量,最终性别、基线美国国立卫生研究院卒中量表(National Institute of Health Stroke Scale,NIHSS)评分、半暗带/梗死核心比、脑钠肽(brain natriuretic peptide,BNP)和血小板计数(platelet,PLT)进入多变量logistic回归模型。结果显示,性别[优势比(odds ratio,OR)2.632,95%置信区间(confidence interval,CI)1.048~6.607;P=0.039]、基线NIHSS评分(OR 1.078,95%CI 1.002~1.160;P=0.043)、BNP(OR 1.004,95%CI 1.002~1.007;P<0.001)、PLT(OR 0.991,95%CI 0.982~0.999;P=0.031)为区分LAA与CE的预测因素;此外,半暗带/梗死核心比(OR 0.886,95%CI 0.785~1.000;P=0.050)在预测模型中也起到重要作用。通过受试者工作特征曲线分析该预测模型的诊断效能,曲线下面积为0.881(95%CI 0.815~0.930;P<0.001)。Bootstrap内部验证显示,真实值与预测值符合度的平均绝对误差为0.027,符合度良好;校准曲线、临床决策曲线及Hosmer-Lemeshow检验(P=0.562)显示,模型的预测值及实际值一致性良好。结论CE患者女性更常见、NIHSS评分及BNP更高、PLT和半暗带/梗死核心比更低,结合以上指标建立的列线图模型可较好地鉴别LAA与CE,对临床决Objective To develop a nomogram model based on clinical and imaging parameters to predict the etiological type of acute ischemic stroke(AIS).Methods Patients with AIS received endovascular treatment in Beijing Hospital from March 2016 to December 2021 were retrospectively included.According to the etiological type,they were divided into large artery atherosclerosis(LAA)and cardioembolism(CE).The clinical and imaging parameters mostly relevant to the etiological type were selected by LASSO regression,and a nomogram model for predicting the etiological type of AIS was established by multifactorial logistic regression to investigate the predictive value of relevant clinical imaging parameters.In addition,the diagnostic efficacy of the prediction model was assessed by receiver operator characteristic(ROC)curves,calibration curves,and clinical decision curves.Results A total of 136 AIS patients with anterior circulation large vessel occlusion received endovascular treatment were included,including 62 patients with CE(45.6%)and 74 with LAA(54.4%).Variables with P<0.10 in the univariate analysis were included in LASSO regression to screen for relevant variables.The gender,baseline National Institute of Health Stroke Scale(NIHSS)score,penumbra to ischemic core ratio,brain natriuretic peptide(BNP),and platelet(PLT)count were included into the multivariate logistic regression model.The results revealed that gender(odds ratio[OR]2.632,95%confidence interval[CI]1.048-6.607;P=0.039),baseline NIHSS score(OR 1.078,95%CI 1.002-1.160;P=0.043),BNP(OR 1.004,95%CI 1.002-1.007.P<0.001),PLT(OR 0.991,95%CI 0.982-0.999;P=0.031)as the predictors to distinguish LAA from CE.In addition,the penumbra to infarct core ratio(OR 0.886,95%CI 0.785-1.000;P=0.050)also played an important role in predicting the model.The diagnostic efficacy of this predictive model was analyzed by the ROC curves,with an area under the curve of 0.881(95%CI 0.815-0.930,P<0.001).Bootstrap internal validation showed that the good compliance with a mean absolute error of

关 键 词：缺血性卒中 颅内动脉硬化 颅内栓塞 体层摄影术 X线计算机 CT血管造影术 灌注成像 多模态成像 列线图表 

分 类 号：R743.3[医药卫生—神经病学与精神病学]