Predicting keratinocyte carcinoma in patients with actinic keratosis: development and internal validation of a multivariable risk-prediction model
Background: Patients with actinic keratosis (AK) are at increased risk for developing keratinocyte carcinoma (KC) but predictive factors and their risk rates are unknown. Objectives: To develop and internally validate a prediction model to calculate the absolute risk of a first KC in patients with AK. Methods: The risk-prediction model was based on the prospective population-based Rotterdam Study cohort. We hereto analysed the data of participants with at least one AK lesion at cohort baseline using a multivariable Cox proportional hazards model and included 13 a priori defined candidate predictor variables considering phenotypic, genetic and lifestyle risk factors. KCs were identified by linkage of the data with the Dutch Pathology Registry. Results: Of the 1169 AK participants at baseline, 176 (15·1%) developed a KC after a median follow-up of 1·8 years. The final model with significant predictors was obtained after backward stepwise selection and comprised the presence of four to nine AKs [hazard ratio (HR) 1·68, 95% confidence interval (CI) 1·17–2·42], 10 or more AKs (HR 2·44, 95% CI 1·65–3·61), AK localization on the upper extremities (HR 0·75, 95% CI 0·52–1·08) or elsewhere except the head (HR 1·40, 95% CI 0·98–2·01) and coffee consumption (HR 0·92, 95% CI 0·84–1·01). Evaluation of the discriminative ability of the model showed a bootstrap validated concordance index (c-index) of 0·60. Conclusions: We showed that the risk of KC in patients with AK can be calculated with the use of four easily assessable predictor variables. Given the c-index, extension of the model with additional, currently unknown predictor variables is desirable.
|British Journal of Dermatology|
Tokez, S. (S.), Alblas, M, Nijsten, T.E.C, Pardo Cortes, L.M, & Wakkee, M. (2019). Predicting keratinocyte carcinoma in patients with actinic keratosis: development and internal validation of a multivariable risk-prediction model. British Journal of Dermatology. doi:10.1111/bjd.18810