Cervical cancer risk stratification based on nationwide electronic health data: a retrospective modelling and validation study from Estonia

Abstract

Transitioning to an individualized risk-based approach can significantly enhance cervical cancer screening programs. We aimed to derive and internally validate a prediction model for assessing the risk of cervical intraepithelial neoplasia grade 3 or higher (CIN3+) and cancer in women eligible for screening. This retrospective study utilized data from the Estonian electronic health records, including 517,884 women from the health insurance database and linked health registries. We employed Cox proportional hazard regression, incorporating reproductive and medical history variables (14 covariates), and utilized the least absolute shrinkage and selection operator (LASSO) for variable selection. A 10-fold cross-validation for internal validation of the model was used. The main outcomes were the performance of discrimination and calibration. Over the 8-year follow-up, we identified 1,326 women with cervical cancer and 5,929 with CIN3+, with absolute risks of 0.3% and 1.1%, respectively. The prediction model for CIN3+ and cervical cancer had good discriminative power and was well calibrated Harrell's C of 0.74 (0.73 - 0.74) (calibration slope 0.99 (0.94 - 1.03) and 0.67 (0.66 - 0.69) (calibration slope 0.96 (0.88 - 1.04) respectively. A developed model based on nationwide electronic health data showed potential utility for risk stratification to supplement screening efforts.