A risk prediction model of cervical cancer developed based on nested case-control design.
10.3760/cma.j.cn112338-20221223-01079
- VernacularTitle:基于巢式病例对照设计开发宫颈癌发病风险预测模型
- Author:
Pei LI
1
;
Zhi Ke LIU
1
;
Hou Yu ZHAO
1
;
Xue Yang LIU
2
;
Peng SHEN
3
;
Hong Bo LIN
2
;
Si Yan ZHAN
1
;
Feng SUN
1
Author Information
1. Key Laboratory of Epidemiology of Major Diseases, Ministry of Education/Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
2. National Engineering Research Center for Software Engineering, Peking University, Beijing 100871, China.
3. Yinzhou District Center for Disease Control and Prevention of Ningbo, Ningbo 315100, China.
- Publication Type:Journal Article
- From:
Chinese Journal of Epidemiology
2023;44(7):1139-1145
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To construct a cervical cancer risk prediction model based on nested case-control study design and Yinzhou Health Information Platform in Ningbo, and provide reliable reference for self-risk assessment of cervical cancer in local women. Methods: In local women aged 25-75 years old who had no history of cervical cancer registered in Yinzhou before October 31, 2018, a follow up was conducted for at least three years, the patients who developed cervical cancer during the follow up period were selected as the case group and matched with a control group at a ratio of 1∶10. The prediction indicators before the onset was used in model construction. Variables were selected by Lasso-logistic regression, the variables with non-zero β were selected to fit the logistic regression model and Bootstrap was used for internal validation. The discrimination of the model was evaluated by area under the receiver operating characteristic curve(AUROC), and the calibration was evaluated by calibration curve and Hosmer-Lemeshow test. Results: The prediction indicators included in the final model were age, smoking status, history of cervicitis, history of adenomyosis, HPV testing, and thinprep cytologic test. The AUROC calculated in the internal validation was 0.740 (95%CI:0.739-0.740), and the calibration curve was almost identical with the ideal curve, P=0.991 in Hosmer-Lemeshow test, indicating that the model discrimination and calibration were good. Conclusions: In this study, a simple and practical cervical cancer risk prediction model was developed. The model can be used in general population with strong interpretability, good discrimination and calibration in internal validation, which can provide a reference for women to assess their risk of cervical cancer.
