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1.上海中医药大学附属龙华医院,上海市徐汇区宛平南路725号, 200032
2.上海中医药大学附属市中医医院
3.上海中医药大学
Received:26 June 2021,
Revised:2021-10-03,
Published:02 January 2022
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罗添乐,周奕阳,杨蕴等.外周免疫评分对经中医药治疗的非小细胞肺癌患者预后的影响及相关预测模型的构建[J].中医杂志,2022,63(01):35-42.
LUO Tianle,ZHOU Yiyang,YANG Yun,et al.The Influence of the Peripheral Immune Score on the Prognosis of Non-small Cell Lung Cancer Treated by Traditional Chinese Medicine and the Construction of the Related Prediction Model[J].Journal of Traditional Chinese Medicine,2022,63(01):35-42.
罗添乐,周奕阳,杨蕴等.外周免疫评分对经中医药治疗的非小细胞肺癌患者预后的影响及相关预测模型的构建[J].中医杂志,2022,63(01):35-42. DOI: 10.13288/j.11-2166/r.2022.01.008.
LUO Tianle,ZHOU Yiyang,YANG Yun,et al.The Influence of the Peripheral Immune Score on the Prognosis of Non-small Cell Lung Cancer Treated by Traditional Chinese Medicine and the Construction of the Related Prediction Model[J].Journal of Traditional Chinese Medicine,2022,63(01):35-42. DOI: 10.13288/j.11-2166/r.2022.01.008.
目的
2
构建经中医药治疗的非小细胞肺癌(NSCLC)患者预后的多因素评估模型。
方法
2
回顾性收集至少经过6个月中医药治疗的NSCLC患者的临床资料,包括性别、年龄、免疫功能、TNM分期、治疗方式、无进展生存期(PFS)、是否发生复发或转移等。根据制定的外周免疫评分标准评价患者的免疫功能,并对患者外周免疫评分、性别、年龄、临床分期与是否复发或转移进行相关性分析;采用单因素Cox回归分析外周免疫评分与PFS之间的关系。将纳入病例分为建模组和验证组,分别采用单因素与多因素Cox回归分析年龄、性别、外周免疫评分、TNM分期、治疗方式与患者是否发生复发或转移的关系,据此建立列线图预测模型,分析其一致性系数(C-index),并以受试者工作特征曲线(ROC)下面积(AUC)和校准图来验证模型的准确性。
结果
2
共纳入175例患者,根据外周免疫评分将患者分为3组,其中免疫低下(<0分)组123例(70.29%)、免疫正常(=0分)组24例(13.71%)、免疫亢进(>0分)组28例(16.00%)。相关性分析显示,外周免疫评分与患者是否复发或转移呈负相关(
P
=0.008,
r
=-0.201),而临床分期、性别、年龄等因素与患者是否复发或转移无相关性;免疫正常组的PFS最长,免疫低下组的PFS最短。单因素分析显示,性别、治疗方式、TNM分期、外周免疫评分是影响患者是否复发或转移的潜在预测因素(
P
<0.1);多因素分析显示,性别、治疗方式、TNM分期、外周免疫评分是影响患者是否复发或转移的独立因素(
P
<0.05)。建立列线图预测模型,建模组、验证组的C-index分别为0.779、0.718,建模组ROC曲线显示1年、3年AUC分别为0.831、0.798,验证组ROC曲线显示1年、3年AUC分别为0.542、0.649。
结论
2
以性别、TNM分期、外周免疫评分、治疗方式为因素建立的模型可以预测经中医药治疗后的NSCLC患者的预后,其中外周免疫评分可作为评估预后的独立因素。
Objective
2
To construct the multivariate prognostic model for non-small cell lung cancer (NSCLC) treated by traditional Chinese medicine (TCM).
Methods
2
Data of NSCLC patients who have been treated by TCM for at least 6 months were retrospectively collected, including gender, age, immune function, TNM staging, treatment methods, progression-free survival (PFS), and whether metastasis occurs. The immune function was evaluated according to the peripheral immune score standard, and the correlations among the peripheral immune score, gender, age, clinical stage PFS and replapse or metastasis were assessed. Univariate Cox regression was used to analyze the correlation between the peripheral immune score and PFS. The patients were divided into modeling group and verification group, and the correlations among age, gender, peripheral immune score, TNM stage, treatment methods, relapse and metastasis were analyzed through univariate and multivariate analysis, based on which the nomogram prediction model was established. The consistency coefficient C-index was analyzed, and the area under the the receiver operating characteristic (ROC) curve (AUC) and the calibration chart were used to verify the accuracy of the model.
Results
2
A total of 175 patients were included and were divided into the immunocompromised group (<0 points, 123 cases, 70.29%), the normal immune group (=0 points, 24 cases, 13.71%) and the hyperimmune group (>0, 28 cases, 16%) according to the immune score. Correlation analysis showed that the peripheral immune score was negatively correlated with the patient's PFS (
P
=0.008,
r
=-0.201), but the factors such as the clinical stage, gender, and age were not significantly correlated with relapse or metastasis. The normal immune group had the longest PFS while the immunocompromised group had the shortest. Univariate analysis showed that gender, treatment method, TNM stage and immune score were potential predictors of relapse or metastasis in NSCLC patients (
P
<0.1); multivariate analysis showed that gender, treatment method, TNM stage and immune score were independent factors for relapse or metastasis (
P
<0.05). The nomogram prediction model was established, and the results showed that the C-index of the modeling group and the verification group were 0.779 and 0.718, respectively. The ROC curve showed that the 1-year and 3-year AUC of the modeling group were 0.831 and 0.798, while those of the verification group were 0.542 and 0.649, respectively.
Conclusion
2
The model established based on gender, TNM stage, peripheral immune score and treatment methods can predict the prognosis of NSCLC patients treated by TCM, and the peripheral immune score can be used as an independent factor to evaluate the prognosis.
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