Development and validation of a multivariable risk prediction model for head and neck cancer using the UK Biobank.

Abstract

Head and neck cancer (HNC) is the eighth most common cancer in the UK, with over 12,000 new cases every year. The incidence of HNC is predicted to increase by 33% by2035. Risk modelling produces personalised risk estimates for specific diseases, which can be used to inform education, screening programmes and recruitment to clinical trials. The present study describes the development and validation of the first risk prediction model for absolute risk of HNC, using a nested case‑control study within the UK Biobank dataset. The UK Biobank recruited 502,647 individuals aged 40‑69years from around the UK. In total, 859cases of HNC were identified, with 253 incident cases (individuals who developed HNC in the 7years following recruitment to the UK Biobank study). Logistic regression was used to develop the model, then the model performance was validated using a cohort from the North West of England. Overall, increasing age, male sex, positive history of smoking and alcohol consumption and higher levels of material deprivation were significantly associated with a higher risk of HNC. Consuming at least five portions of fruit and vegetables per day, exercising at least once per week and higher BMI offered a protective effect against HNC. The C‑statistic was 0.69 [95% confidence interval (CI), 0.66‑0.71] and the model displayed good calibration. Upon external validation, the C‑statistic was 0.64 (95% CI, 0.60‑0.68) with reasonable calibration. The model developed and validated in the present study allows calculation of a personalised risk estimate for HNC. This could be used to guide clinicians when counselling individuals on risk behaviour, and there is potential for such models to inform recruitment to screening trials.