Risk Factors for Lung Cancer Associated Stroke

doi:10.3969/j.issn.1673-5765.2022.06.013

Abstract

Abstract:

Objective To explore the risk factors for lung cancer associated stroke (LCAS).

Methods The consecutive LCAS inpatients who were admitted in Department of Neurology, Affiliated Hospital of Xuzhou Medical University from May 2016 to May 2021 were included in the observation group. According to 1:1 ratio, the age- and gender-matched inpatients with simple lung cancer at the same time were selected as the control group. Demographic information, laboratory tests, lung cancer related clinical data (such as pathological types, treatment methods, etc.) were collected. Logistic regression analysis was used to determine the risk factors of LCAS, and the forest plot showed the effect size of each risk factor. Constructing the predictive model, and the ROC curve was used to evaluate the value of predictive model.

Results A total of 142 eligible patients with LCAS were included, with a mean age of 68.4±9.0 years and 99 males (69.7%); and 142 patients with simple lung cancer were included, with a mean age of 68.6±9.1 years and 99 males (69.7%). Logistic regression analysis showed that carcino embryonic antigen (CEA) (OR 1.050, 95%CI 1.001-1.102, P=0.047), recombinant cytokeratin fragment antigen 21-1 (CYFRA21-1) (OR 1.096, 95%CI 1.020-1.177, P=0.012), plasma D-dimer (OR 1.615, 95%CI 1.129-2.311, P=0.009), white blood cells counts (WBC)(OR 1.193, 95%CI 1.030-1.381, P=0.018) increasing were independent risk factors for LCAS, and plasma fibrinogen (FIB) level increasing was an independent protective factor for LCAS (OR 0.661, 95%CI 0.494-0.884, P=0.005). The regression equation was used to establish a model to predict the risk of LCAS: Logit(P)=-0.856+0.049×CEA+0.092×CYFRA21-1-0.415×FIB+0.480×D-dimer+0.176×WBC. The area under ROC curve of the prediction model was 0.796 (95%CI 0.726-0.866, P<0.001).

Conclusions CEA, CYFRA21-1, D-dimer level, WBC counts and plasma FIB level were independent influencing factors of LCAS. The risk predictive model constructed with the above five indicators can effectively predict the risk of LCAS.

Key words: Lung cancer; Stroke; Risk factor; Forest plot

摘要：

目的探讨肺癌相关脑梗死（lung cancer associated stroke，LCAS）的危险因素。

方法连续性收集2016年5月-2021年5月于徐州医科大学附属医院神经内科住院的LCAS患者为观察组（LCAS组）；1∶1选择同期住院且年龄、性别与LCAS组相匹配的单纯肺癌（lung cancer，LC）患者为对照组。收集2组的人口学信息、实验室检查、肺癌相关临床资料。肺癌相关临床资料包括：病理类型、治疗方法等指标。采用logistic回归分析LCAS的危险因素，森林图显示危险因素的效应强弱，建立预测模型并绘制ROC曲线评价预测模型的评估价值。

结果共纳入LCAS组患者142例，男99例，女43例，平均年龄68.4±9.0岁；LC组患者142例，男99例，女43例，平均年龄68.6±9.1岁。logistic回归分析显示，癌胚抗原（carcino embryonic antigen，CEA）（OR 1.050，95%CI 1.001～1.102，P =0.047）、肺癌抗原（recombinant cytokeratin fragment antigen 21-1，CYFRA21-1）（OR 1.096，95%CI 1.020～1.177，P =0.012）、D-二聚体（OR 1.615，95%CI 1.129～2.311，P =0.009）、白细胞计数（OR 1.193，95%CI 1.030～1.381，P =0.018）水平升高是LCAS的独立危险因素；血浆纤维蛋白原（fibrinogen，FIB）水平升高是LCAS的独立保护因素（OR 0.661，95%CI 0.494～0.884，P =0.005）。通过回归方程建立预测LCAS发生风险的模型：logit（P）=-0.856+0.049×CEA+0.092×CYFRA21-1-0.415×FIB+0.480×D-二聚体+0.176×白细胞计数。该预测模型AUC为0.796（95%CI 0.726～0.866，

P <0.001）。

结论 CEA、CYFRA21-1、D-二聚体、白细胞计数、FIB水平是肺癌相关脑梗死的独立影响因素。联合上述5个指标建立的风险模型可有效预测LCAS的发生风险。

关键词: 肺癌; 脑梗死; 危险因素; 森林图

XU Ran, LI Yueyue, FENG Fanfan, KAN Weihao, DONG Ruiguo. Risk Factors for Lung Cancer Associated Stroke[J]. Chinese Journal of Stroke, 2022, 17(06): 628-633.

徐冉, 李悦悦, 冯凡凡, 阚伟豪, 董瑞国. 肺癌相关脑梗死的危险因素分析[J]. 中国卒中杂志, 2022, 17(06): 628-633.

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