中国癌症杂志 ›› 2021, Vol. 31 ›› Issue (1): 52-62.doi: 10.19401/j.cnki.1007-3639.2021.01.007

• 论著 • 上一篇    下一篇

乳腺癌患者化疗致肝损伤的危险因素分析

刘加葳,李 丹,翟 青   

  1. 复旦大学附属肿瘤医院药剂科,复旦大学上海医学院肿瘤学系,上海 200032
  • 出版日期:2021-01-30 发布日期:2021-02-22
  • 通信作者: 翟 青 E-mail:zhaiqing63@126.com

Risk factors analysis of liver injury induced by chemotherapy in patients with breast cancer

LIU Jiawei, LI Dan, ZHAI Qing#br#   

  1. Department of Pharmacy, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
  • Online:2021-01-30 Published:2021-02-22
  • Contact: ZHAI Qing E-mail: zhaiqing63@126.com

摘要: 背景与目的:乳腺癌患者化疗方案复杂且通常采用多药联合治疗,化疗周期长,不良反应较多。在肿瘤患者的临床诊疗实践中,药物性肝损伤也是常见的不良反应之一。在药物引起的肝损伤中,抗肿瘤药物引起的肝损伤占15%。为了解临床乳腺癌患者化疗致肝损伤的情况,探索其高危影响因素,为临床乳腺癌化疗致肝损伤相关不良反应的预防提供参考。方法:采用病例对照回顾性分析2017年1月—2017年12月在复旦大学附属肿瘤医院接受治疗的乳腺癌患者化疗引起肝损伤的风险因素。采用二元logistic分析建立肝损伤预测模型,采用受试者工作特征(receiver operating characteristic,ROC)曲线分析检测该模型的预测能力。结果:本研究符合纳入和排除标准的乳腺癌患者724例。其中40.74%的患者化疗期间出现肝损伤。二元logistic分析结果显示,年龄分段、TNM分期、肝脏基础疾病、密集型化疗方案、紫杉醇联合铂类化疗方案、含蒽环类化疗方案、目前化疗所处周期为乳腺癌患者化疗引起肝损伤的独立危险因素。二元logistic模型:P=1/1+Exp∑(0.901-AX 1 +1.01X 2 +BX 3 -1.82X 4 +5.225X 5 +1.256X 6 +0.874X 7 -0.764X 8 ),其特异度为91.61%,灵敏度81.69%,
确度为87.60%,阴性预测值(negative predictive value,NPV)为87.92%,阳性预测值(positive predictive value,PPV)为87.00%。同时ROC曲线分析显示,ROC曲线的曲线下面积为0.923(95% CI:0.901~0.944,P<0.001)。结论:该预测模型能够满足临床乳腺癌患者化疗致肝损伤的预测要求,具有较强的临床适用性及可推广性,这为后期对乳腺癌患者肝损伤不良反应的预测及临床干预奠定了必要的基础。

关键词: 乳腺癌, 化疗, 肝损伤, 危险因素, Logistic回归分析

Abstract: Background and purpose: There are various chemotherapy regimens for breast cancer, and multiple chemotherapy drugs are often used in combination. The chemotherapy cycle is long, and chemotherapy drugs cause many adverse reactions. In clinical diagnosis and treatment of tumor patients, liver injury caused by chemotherapy is also a common adverse reaction. Of the drug-induced liver injuries, liver injury caused by antitumor drugs accounts for 15%. We aimed to provide clinical strategies for chemotherapy patients to reduce adverse reactions, increase chemotherapy compliance and improve the quality of life of patients. Methods: We retrospectively analyzed the risk factors of liver injury caused by chemotherapy in patients with breast cancer who were treated in Fudan University Shanghai Cancer Center from January 2017 to December 2017. We enrolled breast cancer patients from Fudan University Shanghai Cancer Center and collected basic information including patient’s information, medical history, chemotherapy, and laboratory indicators related to liver function. According to the presence or absence of liver injury, the patients were divided into case group and control group. Univariate analysis of each factor level and multivariate analysis were used to explore the risk factors of liver injury caused by chemotherapy in patients with breast cancer. The prediction model of liver injury was established by binary logistic analysis, and the predictive ability of the model was tested by receiver operating characteristic (ROC) curve analysis. Results: A total of 724 patients with breast cancer in Fudan University Shanghai Cancer Center were eligible for inclusion in this study. The proportion of patients with liver injury during chemotherapy was 40.74%. The results of logistics analysis based on the results of univariate analysis showed that age, TNM stage, intensive chemotherapy regimen, paclitaxel combined with platinum chemotherapy regimen, anthracycline chemotherapy regimen and chemotherapy cycle became the independent risk factors for liver injury caused by chemotherapy in patients. The accuracy of the binary logistic model: P=1/1+Exp∑(0.901- AX 1 +1.01X 2 +TX 3 -1.82X 4 +5.225X 5 +1.256X 6 + 0.874X 7 -0.764X 8 ), with a specificity of 91.61% and a sensitivity of 81.69%, was 87.60%. The negative predictive value (NPV) was 87.92%, and the positive predictive value (PPV) was 87.00%. At the same time, ROC curve analysis showed that the area under the ROC curve was 0.923 (95% CI: 0.901-0.944, P<0.001). Conclusion: It is necessary to establish an effective prediction model to take certain intervention measures for patients with high-risk liver injury. The binary logistic prediction model established in this study has high accuracy, sensitivity and specificity, and can satisfy the prediction requirements of chemotherapy-induced liver injury in breast cancer patients. It lays the theoretical foundation for the prediction and clinical intervention of adverse reactions in breast cancer patients with liver injury at later stage.

Key words:  Breast cancer, Chemotherapy, Liver injury, Risk factors, Logistic regression analysis