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%CI0.739-0.740), 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.