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预测HBV相关肝细胞癌生存的列线图模型的建立

陈松海 王春艳 郭畅 张珊 邓亚 陆荫英 纪冬

引用本文:
Citation:

预测HBV相关肝细胞癌生存的列线图模型的建立

DOI: 10.3969/j.issn.1001-5256.2022.07.020
基金项目: 

解放军总医院医疗大数据与人工智能研发项目 (2019MBD-024);

北京市自然科学基金面上项目 (7222173);

深圳市科技创新委员会可持续发展专项 (KCXFZ202002011006448)

伦理学声明: 本研究于2020年7月14日通过解放军总医院第五医学中心伦理委员会审批,批号为2020055D。
利益冲突声明: 本研究不存在研究者、伦理委员会成员、受试者监护人以及与公开研究成果有关的利益冲突。
作者贡献声明: 纪冬负责课题设计及拟定写作思路;陈松海、王春艳、郭畅进行资料分析及撰写论文;所有作者均参与收集及分析数据;陆荫英指导撰写文章并最后定稿。
详细信息
    通信作者:

    陆荫英,luyinying1973@163.com

    纪冬,jidg302@126.com

Establishment of a nomogram model for predicting the survival of hepatitis B virus-related hepatocellular carcinoma

Research funding: 

Medical Big Data and Artificial Intelligence Development Fund of Chinese PLA General Hospital (2019MBD-024);

Beijing Municipal Natural Science Foundation (7222173);

Science Technology and Innovation Committee of Shenzhen Municipality (KCXFZ202002011006448)

More Information
  • 摘要:   目的  筛选HBV相关肝细胞癌(HCC)死亡的危险因素并建立列线图预测模型。  方法  回顾性分析2010年1月—2020年1月在解放军总医院第五医学中心首次确诊为HBV相关HCC的700例患者的资料并进行随访,起点为HCC的诊断日期,终点为死亡。根据随访结果,分为死亡组(n=407)和生存组(n=293)。计量资料用组间比较采用独立样本t检验或Mann-Whitney U检验;计数资料组间比较采用χ2检验;采用Kaplan-Meier法计算生存率,log-rank检验进行单因素分析,应用多变量Cox比例风险回归筛选独立危险因素,并构建可预测1、3、5年生存率的列线图模型,采用200次10折交叉验证法对模型进行评价。  结果  两组的性别构成、Alb、TBil、ALT、AFP、CHE、LSM及BCLC分期比较差异均有统计学意义(P值均<0.05)。多因素分析显示男性[风险比(HR)=1.390,95%CI:1.077~1.794]、CHE(2500~5000 U/L)(HR=1.996,95%CI:1.470~2.710)、CHE<2500 U/L(HR=3.210,95%CI:2.188~4.709)、AFP≥400 ng/mL(HR=1.803,95%CI:1.412~2.303)、肝弹性值≥17.5 kPa(HR=1.719,95%CI: 1.349~2.190)、巴塞罗那分期B/C(HR=3.811,95%CI:2.994~4.852)、巴塞罗那分期D(HR=3.708,95%CI:2.520~5.455)为死亡的独立危险因素(P值均<0.05)。基于以上因素建立列线图模型,一致性指数为0.789(95%CI:0.769~0.809),校准曲线拟合良好,模型预测值与实际观测值之间的差异无统计学意义。  结论  本研究建立的列线图模型可以个体化预测HBV相关HCC患者的1、3、5年的生存率,有助于准确评估预后。

     

  • 图  1  HBV相关HCC死亡的危险因素

    注:ULN,正常值上限。各因素的参照(HR=1):BCLC 0/A期;AFP<20 ng/mL;CHE≥5000 U/L;女性;LSM<17.5 kPa;ALT<1×ULN;TBil<1×ULN;Alb≥1×ULN。

    Figure  1.  High-risk factors for the mortality of HBV-related HCC

    图  2  HBV相关HCC的生存曲线

    Figure  2.  Survival curve for HBV-related HCC

    图  3  预测HBV相关HCC生存率的列线图及校正曲线

    Figure  3.  Nomogram and calibration curve to predict survival rate of HBV-related HCC

    表  1  纳入患者的基线资料

    Table  1.   Baseline characteristics of enrolled patients

    项目 总体(n=700) 死亡组(n=407) 生存组(n=293) 统计值 P
    男[例(%)] 522(74.6) 332(81.6) 190(64.8) χ2=25.13 <0.001
    年龄(岁) 53.8±9.3 54.1±9.5 53.6±8.9 t=-0.67 0.501
    家族史[例(%)] 385(55.0) 213(52.3) 172(58.7) χ2=2.79 0.095
    糖尿病[例(%)] 90(12.9) 49(12.0) 41(14.0) χ2=0.58 0.446
    HBeAg阳性[例(%)] 241(34.4) 137(33.7) 104(35.5) χ2=0.25 0.614
    HBeAg血清学转换1)[例(%)] 53(13.4) 37(15.7) 16(9.9) χ2=2.37 0.124
    LSM≥17.5 kPa[例(%)] 465(66.4) 315(77.4) 150(51.2) χ2=52.40 <0.001
    Alb(g/L) 33.20±6.20 31.57±6.10 35.60±5.60 t=8.90 <0.001
    TBil(μmol/L) 19.2(13.2~32.1) 22.0(14.8~41.4) 16.2(11.4~23.4) Z=-7.18 <0.001
    PLT(×109/L) 94(59~145) 92(58~145) 95(61~146) Z=-0.43 0.670
    ALT(U/L) 34(23~52) 45(30~71) 25(20~33) Z=-13.09 <0.001
    AFP[例(%)] χ2=56.06 <0.001
      <20 ng/mL 319(45.6) 147(36.1) 172(58.7)
      20~400 ng/mL 199(28.4) 114(28) 85(29.0)
      ≥400 ng/mL 182(26.0) 146(35.9) 36(12.3)
    CHE[例(%)] χ2=121.78 <0.001
      ≥5000 U/L 241(34.4) 78(19.2) 163(55.6)
      2500~5000 U/L 295(42.2) 188(46.2) 107(36.5)
      <2500 U/L 164(23.4) 141(34.6) 23(7.8)
    BCLC分期[例(%)] χ2=174.49 <0.001
      0/A 333(47.6) 109(26.8) 224(76.5)
      B/C 305(43.6) 239(58.7) 66(22.5)
      D 62(8.8) 59(14.5) 3(1.0)
    注:1)定义为经抗病毒治疗后HBeAg阳性转为抗HBe阳性者在HBeAg阳性(397例)中的比例。
    下载: 导出CSV

    表  2  200次10折交叉验证的AUC值

    Table  2.   The area under the receiver operating characteristic curves from 200-time 10-fold cross validation

    生存率 最小值 Q25 中位值 平均值 Q75 最大值
    1年 0.704 0.834 0.864 0.862 0.893 0.968
    3年 0.679 0.831 0.863 0.860 0.894 0.981
    5年 0.609 0.801 0.844 0.841 0.887 0.994
    下载: 导出CSV
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  • 收稿日期:  2021-11-26
  • 录用日期:  2022-01-24
  • 出版日期:  2022-07-20
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