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吴琼, 马军燕, 董量, 李春阳, 王志武. 基于病证结合信息构建广泛期小细胞肺癌安罗替尼疗效预测模型[J]. 肿瘤防治研究, 2023, 50(5): 483-489. DOI: 10.3971/j.issn.1000-8578.2023.22.1373
引用本文: 吴琼, 马军燕, 董量, 李春阳, 王志武. 基于病证结合信息构建广泛期小细胞肺癌安罗替尼疗效预测模型[J]. 肿瘤防治研究, 2023, 50(5): 483-489. DOI: 10.3971/j.issn.1000-8578.2023.22.1373
WU Qiong, MA Junyan, DONG Liang, LI Chunyang, WANG Zhiwu. Prediction Model of Treatment Effect of Anlotinib on Extensive-stage Small Cell Lung Cancer Based on Combination of Disease and Syndrome Information[J]. Cancer Research on Prevention and Treatment, 2023, 50(5): 483-489. DOI: 10.3971/j.issn.1000-8578.2023.22.1373
Citation: WU Qiong, MA Junyan, DONG Liang, LI Chunyang, WANG Zhiwu. Prediction Model of Treatment Effect of Anlotinib on Extensive-stage Small Cell Lung Cancer Based on Combination of Disease and Syndrome Information[J]. Cancer Research on Prevention and Treatment, 2023, 50(5): 483-489. DOI: 10.3971/j.issn.1000-8578.2023.22.1373

基于病证结合信息构建广泛期小细胞肺癌安罗替尼疗效预测模型

Prediction Model of Treatment Effect of Anlotinib on Extensive-stage Small Cell Lung Cancer Based on Combination of Disease and Syndrome Information

  • 摘要:
    目的 构建中医证素参与的安罗替尼治疗既往接受过多线化疗进展的广泛期小细胞肺癌(ES-SCLC)患者预后的列线图预测模型。
    方法 回顾性分析至少经过2个周期安罗替尼治疗的127例ES-SCLC患者临床资料。采用Kaplan-Meier法分析各个因素与总生存时间的关系,通过Cox回归分析筛选患者预后的独立影响因素,应用R语言构建列线图预测模型,采用C-index指数对模型进行评估,并以校准曲线来验证模型的准确性。
    结果 K-M法单因素生存分析显示,年龄、PS评分、脑转移、气虚病性证素、阴虚病性证素、血瘀病性证素是安罗替尼治疗ES-SCLC的相关危险因素。多因素Cox回归分析显示,PS评分(HR=2.188, P=0.003)、脑转移(HR=1.891, P=0.016)、血瘀病性证素(HR=1.691, P=0.028)是独立的预后不良因素。基于这三个独立影响因素建立预测安罗替尼治疗ES-SCLC患者预后的列线图模型,预测风险接近实际风险,显示出较高吻合度。
    结论 以PS评分、血瘀病性证素、脑转移为独立因素建立的列线图模型可以预测安罗替尼二三线治疗ES-SCLC患者的预后。

     

    Abstract:
    Objective To construct a nomogram prediction model for the treatment effect of anlotinib with the participation of traditional Chinese medicine syndrome elements on the patients with extensive-stage small cell lung cancer (ES-SCLC) who previously received multiple lines of chemotherapy.
    Methods The clinical data of 127 patients with ES-SCLC who received at least two cycles of anlotinib treatment were retrospectively studied. Kaplan-Meier method was used to analyze the relationship between each factor and the overall survival time. Cox regression analysis was applied to screen the independent influencing factors of the prognosis of patients with ES-SCLC. R language was employed to build a nomogram prediction model, C-index was used to evaluate the model, and calibration curve was adopted to verify the accuracy of the model.
    Results Age, PS score, brain metastases, qi deficiency syndrome, yin deficiency syndrome, and blood stasis syndrome were related risk factors for ES-SCLC treated with anlotinib. PS score, brain metastasis, and blood stasis syndrome were independent prognostic factors. On the basis of these three independent influencing factors, a nomogram model was established to predict the prognosis of patients with ES-SCLC treated with anlotinib. The predicted risk was close to the actual risk, showing a high degree of coincidence.
    Conclusion The nomogram model established with PS score, blood stasis syndrome elements, and brain metastasis as independent factors can predict the prognosis of patients with ES-SCLC receiving second- and third-line treatment of anlotinib.

     

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