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北京大学学报（医学版） ›› 2023, Vol. 55 ›› Issue (3): 471-479. doi: 10.19723/j.issn.1671-167X.2023.03.013

• 论著 • 上一篇    下一篇

乳腺癌患者新发心血管疾病预测模型的建立与验证：基于内蒙古区域医疗数据

张云静1,2,乔丽颖3,祁萌4,严颖5,亢伟伟3,刘国臻6,王明远6,席云峰3,*(),王胜锋1,2,*()   

  1. 1. 北京大学公共卫生学院流行病与卫生统计学系，北京 100191
    2. 重大疾病流行病学教育部重点实验室(北京大学)，北京 100191
    3. 内蒙古自治区疾病预防控制中心，呼和浩特 010031
    4. 北京大学肿瘤医院暨北京市肿瘤防治研究所乳腺癌预防治疗中心，恶性肿瘤发病机制及转化研究教育部重点实验室，北京 100142
    5. 北京大学肿瘤医院暨北京市肿瘤防治研究所乳腺肿瘤内科，恶性肿瘤发病机制及转化研究教育部重点实验室，北京 100142
    6. 北京帕云医疗科技有限公司，北京 100080
  • 收稿日期:2023-02-28 出版日期:2023-06-18 发布日期:2023-06-12
  • 通讯作者: 席云峰,王胜锋 E-mail:xiyunfeng210@163.com;shengfeng1984@126.com
  • 基金资助:
    国家自然科学基金(82173616)

Development and validation of risk prediction model for new-onset cardiovascular diseases among breast cancer patients: Based on regional medical data of Inner Mongolia

Yun-jing ZHANG1,2,Li-ying QIAO3,Meng QI4,Ying YAN5,Wei-wei KANG3,Guo-zhen LIU6,Ming-yuan WANG6,Yun-feng XI3,*(),Sheng-feng WANG1,2,*()   

  1. 1. Department of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing 100191, China
    2. Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
    3. Inner Mongolia Integrative Center for Disease Control and Prevention, Hohhot 010031, China
    4. Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education; Breast Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
    5. Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education; Department of Breast Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
    6. Beijing PD Cloud Medical Technology Co., LTD, Beijing 100080, China
  • Received:2023-02-28 Online:2023-06-18 Published:2023-06-12
  • Contact: Yun-feng XI,Sheng-feng WANG E-mail:xiyunfeng210@163.com;shengfeng1984@126.com
  • Supported by:
    the National Natural Science Foundation of China(82173616)

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摘要：

目的: 开发和验证乳腺癌患者新发心血管疾病(cardiovascular disease, CVD)的3年预测模型。方法: 基于内蒙古区域医疗数据，纳入接受抗肿瘤治疗的18岁以上乳腺癌女性患者。多因素Fine & Gray模型纳入预测因子后，使用Lasso回归筛选变量，在训练集上拟合Cox比例风险、Logistic回归、Fine & Gray、随机森林和XGBoost模型，在测试集上分别用受试者工作特征(receiver operating characteristics, ROC)曲线下面积(area under the curve, AUC)和校准曲线评价模型区分度和校准度。结果: 共纳入19 325例接受抗肿瘤治疗的乳腺癌患者，平均年龄(52.76±10.44)岁，中位随访时间1.18年[四分位距(interquartile range, IQR)：2.71]。7 856例患者(40.65%)在乳腺癌诊断3年内发生CVD。Lasso回归筛选的预测因子为乳腺癌诊断年龄、居住地国内生产总值(gross domestic product，GDP)、肿瘤分期、高血压、缺血性心脏病及脑血管疾病既往史、手术类型、化疗类型、放疗类型。不考虑生存时间时，XGBoost模型的AUC显著高于随机森林模型[0.660 (95%CI：0.644~0.675) vs. 0.608 (95%CI：0.591~0.624), P < 0.001]和Logistic回归[0.609 (95%CI：0.593~0.625), P < 0.001]，Logistic回归和XGBoost模型的校准度更好。考虑生存时间时，Cox比例风险模型和Fine & Gray模型的AUC差异无统计学意义[0.600 (95%CI：0.584~0.616) vs. 0.615 (95%CI：0.599~0.631), P=0.188]，但Fine & Gray模型的校准度更好。结论: 基于区域医疗数据建立乳腺癌新发CVD的预测模型具有可行性。不考虑生存时间时，Logistic回归和XGBoost模型的预测性能更好；考虑生存时间时，Fine & Gray模型的预测性能更好。

关键词: 乳腺肿瘤, 心血管疾病, 风险预测模型, 危险性评估, 计算机化病案系统

Abstract:

Objective: To develop and validate a three-year risk prediction model for new-onset cardiovascular diseases (CVD) among female patients with breast cancer. Methods: Based on the data from Inner Mongolia Regional Healthcare Information Platform, female breast cancer patients over 18 years old who had received anti-tumor treatments were included. The candidate predictors were selected by Lasso regression after being included according to the results of the multivariate Fine & Gray model. Cox proportional hazard model, Logistic regression model, Fine & Gray model, random forest model, and XGBoost model were trained on the training set, and the model performance was evaluated on the testing set. The discrimination was evaluated by the area under the curve (AUC) of the receiver operator characteristic curve (ROC), and the calibration was evaluated by the calibration curve. Results: A total of 19 325 breast cancer patients were identified, with an average age of (52.76±10.44) years. The median follow-up was 1.18 [interquartile range (IQR): 2.71] years. In the study, 7 856 patients (40.65%) developed CVD within 3 years after the diagnosis of breast cancer. The final selected variables included age at diagnosis of breast cancer, gross domestic product (GDP) of residence, tumor stage, history of hypertension, ischemic heart disease, and cerebrovascular disease, type of surgery, type of chemotherapy and radiotherapy. In terms of model discrimination, when not considering survival time, the AUC of the XGBoost model was significantly higher than that of the random forest model [0.660 (95%CI: 0.644-0.675) vs. 0.608 (95%CI: 0.591-0.624), P < 0.001] and Logistic regression model [0.609 (95%CI: 0.593-0.625), P < 0.001]. The Logistic regression model and the XGBoost model showed better calibration. When considering survival time, Cox proportional hazard model and Fine & Gray model showed no significant difference for AUC [0.600 (95%CI: 0.584-0.616) vs. 0.615 (95%CI: 0.599-0.631), P=0.188], but Fine & Gray model showed better calibration. Conclusion: It is feasible to develop a risk prediction model for new-onset CVD of breast cancer based on regional medical data in China. When not considering survival time, the XGBoost model and the Logistic regression model both showed better performance; Fine & Gray model showed better performance in consideration of survival time.

Key words: Breast neoplasms, Cardiovascular disease, Risk prediction model, Risk assessment, Computerized medical records systems

中图分类号: 

  • R737.9

表1

2012—2021年内蒙古自治区乳腺癌新发CVD预测模型拟纳入的变量信息"

Variables Measurement Variable assignments
Age on set Age at diagnosis of breast cancer according to birthday and date of diagnosis Years
Type of medical insurance Type of medical insurance 0=employee, 1=residence
Ethnicity Ethnicity according to medical insurance data 0=Han, 1=Mongolian, 2=others
GDP of residence GDP of district where patients lived according to Inner Mongolia Bureau of Statistics 100 million yuan
Tumor stage According to clinical TNM stage and treatment patterns. Stage=0, 1, 2, 3 was defined as “early” while stage=4 was defined as “advanced” 0=early, 1=unknown, 2=advanced
Type of surgery According to records of treatments from medical insurance data 0=none, 1=breast-conserving surgery, 2=mastectomy
Type of chemotherapy According to records of treatments from medical insurance data 0=none, 1=anthracyclines, 2=non-anthracyclines
Endocrine therapy According to records of treatments from medical insurance data 0=no, 1=yes
Type of targeted therapy According to records of treatments from medical insurance data 0=none, 1=trastuzumab, 2=non-trastuzumab
Radiotherapy According to records of treatments from medical insurance data 0=no, 1=yes
History of diabetes According to records of medical insurance before breast cancer diagnosis 0=no, 1=yes
History of renal diseases According to records of medical insurance before breast cancer diagnosis 0=no, 1=yes
History of hypertension According to records of medical insurance before breast cancer diagnosis 0=no, 1=yes
History of ischemic heart diseases According to records of medical insurance before breast cancer diagnosis 0=no, 1=yes
History of cerebrovascular diseases According to records of medical insurance before breast cancer diagnosis 0=no, 1=yes
History of dyslipidemia According to records of medical insurance before breast cancer diagnosis 0=no, 1=yes
History of hypothyroidism According to records of medical insurance before breast cancer diagnosis 0=no, 1=yes
History of hyperthyroidism According to records of medical insurance before breast cancer diagnosis 0=no, 1=yes
Charlson comorbidity index According to records of medical insurance Points
Hospitalization before diagnosis within 1 year According to records of medical insurance before breast cancer diagnosis within 1 year 0=no, 1=yes
Length of stay before diagnosis within 1 year According to the duration of hospitalization records from medical insurance before breast cancer diagnosis within 1 year Days

表2

2012—2021年内蒙古自治区接受抗肿瘤治疗的乳腺癌患者的人口学特征"

Items CVD (n=7 856) No CVD (n=11 469) P value
Age onset/years, ${\bar x}$±s 53.48±10.38 52.28±10.45 < 0.001
Type of medical insurance, n (%) < 0.001
   Employee 4 291 (54.62) 6 829 (59.54)
   Resident 3 565 (45.38) 4 640 (40.46)
Ethnicity, n (%) 0.002
   Han 6 727 (85.63) 9 620 (83.88)
   Mongolian 865 (11.01) 1 372 (11.96)
   Others 263 (3.35) 473 (4.12)
GDP of residence, median (IQR) 156.21 (180.36) 174.46 (312.93) < 0.001
Charlson comorbidity index, ${\bar x}$±s 1.89±3.32 1.84±3.24 0.254
Tumor stage, n (%) < 0.001
   Early 3 619 (46.07) 4 807 (41.91)
   Advanced 3 114 (39.64) 4 680 (40.81)
   Unknown 1 123 (14.29) 1 982 (17.28)
Previous disease history, n (%)
   Dyslipidemia 943 (12.00) 2 202 (19.20) < 0.001
   Diabetes 943 (12.00) 2 202 (19.20) < 0.001
   Renal diseases 81 (1.03) 181 (1.58) 0.002
   Hypertension 1 325 (16.87) 3 535 (30.82) < 0.001
   Ischemic heart diseases 435 (5.54) 1 313 (11.45) < 0.001
   Cerebrovascular diseases 547 (6.96) 1 372 (11.96) < 0.001
   Hyperthyroidism 10 (0.13) 26 (0.23) 0.160
   Hypothyroidism 9 (0.11) 24 (0.21) 0.165
Hospitalization before diagnosis within 1 year, n (%) 1 743 (22.19) 3 309 (28.85) < 0.001
Length of stay before diagnosis within 1 year, ${\bar x}$±s 2.88±9.15 3.67±8.99 < 0.001
Type of surgery, n (%) < 0.001
   Mastectomy 3 262 (41.52) 3 910 (34.09)
   Breast-conserving 247 (3.14) 495 (4.32)
   None 4 347 (55.33) 7 064 (61.59)
Type of chemotherapy, n (%) < 0.001
   Anthracyclines 3 399 (43.27) 4 299 (37.48)
   Non-anthracyclines 2 394 (30.47) 3 593 (31.33)
   None 2 063 (26.26) 3 577 (31.19)
Endocrine therapy, n (%) 4 669 (59.43) 7 114 (62.03) < 0.001
Type of targeted therapy, n (%) 0.018
   Trastuzumab 1 023 (13.02) 1 649 (14.38)
   Non-trastuzumab 67 (0.85) 111 (0.97)
   None 6 766 (86.13) 9 709 (84.65)
Radiotherapy, n (%) 587 (7.47) 533 (4.65) < 0.001

表3

2012—2021年内蒙古自治区接受抗肿瘤治疗的乳腺癌患者的多因素竞争风险模型结果"

Factor β SE Wald χ2 HR (95%CI) P value
Age onset/years 0.02 0.00 14.17 1.02 (1.01-1.02) < 0.001
Type of medical insurance
   Employee 1.00
   Resident -0.03 0.03 -1.05 0.97 (0.92-1.03) 0.290
Ethnicity
   Han 1.00
   Mongolian 0.00 0.04 0.12 1.00 (0.94-1.08) 0.910
   Others -0.13 0.06 -2.02 0.88 (0.78-1.00) 0.043
GDP of residence 0.00 0.00 -8.36 1.00 (1.00-1.00) < 0.001
Charlson comorbidity index 0.00 0.00 -0.40 1.00 (0.99-1.01) 0.690
Tumor stage
   Early 1.00
   Advanced -0.14 0.04 -3.44 0.87 (0.80-0.94) 0.001
   Unknown -0.06 0.03 -2.15 0.94 (0.88-0.99) 0.031
Type of surgery
   None 1.00
   Breast-conserving 0.06 0.07 0.83 1.06 (0.92-1.22) 0.410
   Mastectomy 0.27 0.03 8.75 1.31 (1.23-1.39) < 0.001
Type of chemotherapy
   None 1.00
   Anthracyclines 0.19 0.03 6.22 1.21 (1.14-1.29) < 0.001
   Non-anthracyclines 0.11 0.03 3.48 1.11 (1.05-1.18) < 0.001
Endocrine therapy
   No 1.00
   Yes -0.10 0.02 -4.07 0.90 (0.86-0.95) < 0.001
Type of targeted therapy
   None 1.00
   Trastuzumab -0.01 0.03 -0.26 0.99 (0.93-1.06) 0.790
   Non-trastuzumab -0.04 0.12 -0.33 0.96 (0.76-1.21) 0.740
Radiotherapy
   No 1.00
   Yes 0.26 0.05 5.35 1.30 (1.18-1.42) < 0.001
History of hypertension
   No 1.00
   Yes -0.44 0.04 -11.52 0.64 (0.60-0.69) < 0.001
History of dyslipidemia
   No 1.00
   Yes -0.02 0.04 -0.53 0.98 (0.90-1.06) 0.590
History of diabetes
   No 1.00
   Yes 0.03 0.04 0.83 1.03 (0.96-1.12) 0.410
History of ischemic heart diseases
   No 1.00
   Yes -0.35 0.06 -6.31 0.71 (0.63-0.79) < 0.001
History of cerebrovascular diseases
   No 1.00
   Yes -0.05 0.05 -1.07 0.95 (0.86-1.05) 0.280
History of renal diseases
   No 1.00
   Yes 0.08 0.12 0.72 1.09 (0.87-1.36) 0.470
History of hyperthyroidism
   No 1.00
   Yes -0.07 0.32 -0.22 0.93 (0.50-1.75) 0.830
History of hypothyroidism
   No 1.00
   Yes 0.01 0.32 0.02 1.01 (0.53-1.90) 0.980
Hospitalization before diagnosis within 1 year
   No 1.00
   Yes -0.07 0.04 -1.75 0.93 (0.86-1.01) 0.081
Length of stay before diagnosis within 1 year 0.00 0.00 -1.20 1.00 (0.99-1.00) 0.230

图1

2012—2021年内蒙古自治区接受抗肿瘤治疗的乳腺癌患者的测试集ROC曲线"

图2

2012—2021年内蒙古自治区接受抗肿瘤治疗的乳腺癌患者的测试集校准曲线"

图3

2012—2021年内蒙古自治区接受抗肿瘤治疗的乳腺癌患者的主分析和敏感性分析生存曲线"

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