Predicting risk of endometrial cancer in asymptomatic women (PRECISION): Model development and external validation

Abstract

AbstractObjectivesDevelop an endometrial cancer risk prediction model and externally validate it for UK primary care use.DesignCohort study.SettingThe UK Biobank was used for model development and a linked primary (Clinical Practice Research Datalink, CPRD) and secondary care (HES), mortality (ONS) and cancer register (NRCAS) dataset was used for external validation.PopulationWomen aged 45–60 years with no history of endometrial cancer or hysterectomy.MethodsModel development was performed using a flexible parametric survival model and stepwise backward selection aiming to minimise the Akaike information criterion. Model performance on external validation was assessed through flexible calibration plots, calculation of the expected to observed ratio and C‐statistic and decision curve analysis.Main outcome measuresEndometrial cancer diagnosis within 1–10 years of the index date.ResultsModel development included 222 031 women (902 incident endometrial cancer cases) and external validation 3 094 371 women (8585 endometrial cancer cases). The final model (with equation provided) incorporated age, body mass index, waist circumference, age at menarche, menopause and last birth, hormone replacement, tamoxifen and oral contraceptive pill use, type 2 diabetes, smoking and family history of bowel cancer. It was well calibrated on external validation (calibration slope 1.14, 95% confidence interval [CI] 1.11–1.17, E/O 1.03, 95% CI 1.01–1.05), with moderate/good discrimination (C‐statistic 0.70, 95% CI 0.69–0.70) and had improved net benefit compared with previously developed models.ConclusionsThe Predicting risk of endometrial cancer in asymptomatic women model (PRECISION), using easily measurable anthropometric, reproductive, personal and family history, accurately quantifies a woman's 10‐year risk of endometrial cancer. Its use could determine eligibility for primary endometrial cancer prevention trials and for targeted resource allocation in UK general practices.