Development and validation of risk prediction model for new-onset cardiovascular diseases among breast cancer patients: Based on regional medical data of Inner Mongolia.
- Author:
Yun Jing ZHANG
1
;
Li Ying QIAO
2
;
Meng QI
3
,
4
;
Ying YAN
3
,
5
;
Wei Wei KANG
2
;
Guo Zhen LIU
6
;
Ming Yuan WANG
6
;
Yun Feng XI
2
;
Sheng Feng WANG
1
Author Information
1. Department of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing 100191, China.
2. Inner Mongolia Integrative Center for Disease Control and Prevention, Hohhot 010031, China.
3. Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education
4. Breast Center, Peking University Cancer Hospital & Institute, Beijing 100142, China.
5. Department of Breast Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
6. Beijing PD Cloud Medical Technology Co., LTD, Beijing 100080, China.
- Publication Type:Journal Article
- Keywords:
Breast neoplasms;
Cardiovascular disease;
Computerized medical records systems;
Risk assessment;
Risk prediction model
- MeSH:
Humans;
Female;
Adult;
Middle Aged;
Adolescent;
Breast Neoplasms/epidemiology*;
Cardiovascular Diseases/etiology*;
Proportional Hazards Models;
Logistic Models;
China/epidemiology*
- From:
Journal of Peking University(Health Sciences)
2023;55(3):471-479
- CountryChina
- Language:Chinese
-
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.
