MP45-01 VALIDATION AND PUBLIC HEALTH MODELLING OF RISK PREDICTION MODELS FOR KIDNEY CANCER IN UK BIOBANK

Abstract

You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance I (MP45)1 Sep 2021MP45-01 VALIDATION AND PUBLIC HEALTH MODELLING OF RISK PREDICTION MODELS FOR KIDNEY CANCER IN UK BIOBANK Hannah Harrison, Lisa Pennells, Angela Wood, Sabrina Rossi, Grant Stewart, Simon Griffin, and Juliet Usher-Smith Hannah HarrisonHannah Harrison More articles by this author , Lisa PennellsLisa Pennells More articles by this author , Angela WoodAngela Wood More articles by this author , Sabrina RossiSabrina Rossi More articles by this author , Grant StewartGrant Stewart More articles by this author , Simon GriffinSimon Griffin More articles by this author , and Juliet Usher-SmithJuliet Usher-Smith More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002066.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Kidney cancer is the 14th most common cancer worldwide. Although early detection is associated with improved survival rates, many newly diagnosed kidney cancers are metastatic. A barrier to the introduction of a screening programme is low population prevalence. Population risk stratification could minimise harms to individuals and improve the efficiency of a screening programme. Stratification requires a model that accurately identifies individuals at high risk of kidney cancer. Although several models have been developed most have not been externally validated, and the benefits of incorporating them in a screening programme have not been assessed. METHODS: We identified phenotypic risk models in a recent systematic review and validated them in a large population cohort (UK Biobank). We assessed discrimination and calibration of the models for men, women and the whole cohort. We undertook a public health modelling analysis using the best performing models to estimate accuracy in the UK population (individuals aged 40-70). We accounted for differences in demographics (age and sex) and kidney cancer incidence between the UK Biobank cohort and the general population. We compared the ability of the models to identify high-risk individuals for screening with age- and sex-based screening strategies. RESULTS: We included 30 models in the review. Eight had reasonable discrimination (AUROC>0.62) in men, women and the mixed-sex cohort. Many of the models had poor calibration in the UK Biobank. Modelling demonstrated the accuracy of the best models over a range of thresholds (6-year risk: 0.1%-1.0%). At any threshold, the models performed similarly. Overall, they showed a small improvement in ability to identify high-risk individuals compared to age- and sex- based screening. At a cut-off threshold of 0.4%, the best performing model screens 12.3% of the population and detects one case for every 180 individuals screened. Screening all men over the age of 60 (14.1%) would detect one case for every 206 individuals screened. All of the models performed less well in women than men. CONCLUSIONS: This is the first comprehensive external validation of risk prediction models for kidney cancer. Five models showed both reasonable discrimination and good calibration in a UK population. The best-performing models could improve the efficiency of screening by similar amounts, with the choice of model depending on the availability of data. Future research may consider adding biomarkers or genetic risk factors to models. Source of Funding: HH is supported by a NIHR DSE Award (NIHR301182) © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e802-e802 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Hannah Harrison More articles by this author Lisa Pennells More articles by this author Angela Wood More articles by this author Sabrina Rossi More articles by this author Grant Stewart More articles by this author Simon Griffin More articles by this author Juliet Usher-Smith More articles by this author Expand All Advertisement Loading ...