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Accurate breast cancer risk prediction could improve risk-reduction paradigms if thoughtfully used in clinical practice. Identification of at-risk women is the first step in tailoring risk screening and risk-reduction protocols to women's needs. Using the UK Biobank, we validated a simple risk model to predict breast cancer risk in the general population. Our simple breast cancer risk (BRISK) model integrates a combination of impactful breast cancer-associated risk factors including extended family history and polygenic risk allowing for the removal of moderate factors currently found in comprehensive traditional models. Using two versions of BRISK, differing by 77-single-nucleotide polymorphisms (SNP) versus 313-SNP polygenic risk score integration, we found improved discrimination and risk categorization of both BRISK models compared with one of the most well-known models, the Breast Cancer Risk Assessment Tool (BRCAT). Over a 5-year period, at-risk women classified ≥3% 5-year risk by BRISK had a 1.829 (95% CI = 1.710–1.956) times increased incidence of breast cancer compared with the population, which was higher than the 1.413 (95% CI = 1.217–1.640) times increased incidence for women classified ≥3% by BCRAT. Prevention Relevance: In this prospective population-based cohort study, we show the improved performance of a new risk assessment model compared with a gold-standard model (BCRAT). The classification of at-risk women using this new model highlights the opportunity to improve risk stratification and implement existing clinical risk-reduction interventions.

The ability to efficiently and accurately risk stratify women can lead to improved risk-reduction measures. Actionable risk thresholds for both screening and risk-reduction guidance exist; however, despite several clinical risk models currently in existence, the utilization of such models is underwhelming. We have developed and validated an abridged clinical risk model, COSMO (Clinical pOlygenic denSity faMily mOdel), incorporating three of the most relevant epidemiological risk factors associated with sporadic and familial breast cancer in a format that is designed for “ease of use”. Polygenic risk is recognized as a promising epidemiological risk factor however, its value is greater when integrated with other phenotypic risk factors including 1st and 2nd family history, breast density menopausal status, BMI and age. We used 200,009 active UK Biobank (UKB) members who were female, genetically Caucasian, had SNP data for 313 breast cancer associated variants and aged 40 to 69 years at baseline assessment date. We used age and date of baseline assessment to calculate censoring age for those who had died before completing 5 years of follow-up and to determine the date at which 5 years of follow-up was completed for the others. We used both self-reported and linked cancer registry data (ICD9 code 174 or ICD10 code C50) to determine the earliest diagnosis of invasive breast cancer for each affected woman. In terms of discrimination, COSMO (AUC=0.651; 95% CI 0.642, 0.661) performed better than the Gail model (AUC=0.567; 95% CI=0.557, 0.567); P value for difference Citation Format: Erika Spaeth, Gillian Dite, Richard Allman. Validation of abridged breast cancer risk assessment model for the general population [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-10-06.

Using nested case–control data from the Lifelines COVID-19 cohort, we undertook a validation study of a clinical and genetic model to predict the risk of severe COVID-19 in people with confirmed COVID-19 and in people with confirmed or self-reported COVID-19. The model performed well in terms of discrimination of cases and controls for all ages (area under the receiver operating characteristic curve (AUC) = 0.680 for confirmed COVID-19 and AUC = 0.689 for confirmed and self-reported COVID-19) and in the age group in which the model was developed (50 years and older; AUC = 0.658 for confirmed COVID-19 and AUC = 0.651 for confirmed and self-reported COVID-19). There was no evidence of over- or under-dispersion of risk scores but there was evidence of overall over-estimation of risk in all analyses (all P < 0.0001). In the light of large numbers of people worldwide remaining unvaccinated and continuing uncertainty regarding vaccine efficacy over time and against variants of concern, identification of people at high risk of severe COVID-19 may encourage the uptake of vaccinations (including boosters) and the use of non-pharmaceutical inventions.