Decision support tools for the assessment and management of breast cancer risk may improve uptake of prevention strategies. End-user input in the design of such tools is critical to increase clinical use. Before developing such a computerized tool, we examined clinicians' practice and future needs. Twelve breast surgeons, 12 primary care physicians and 5 practice nurses participated in 4 focus groups. These were recorded, coded, and analyzed to identify key themes. Participants identified difficulties assessing risk, including a lack of available tools to standardize practice. Most expressed confidence identifying women at potentially high risk, but not moderate risk. Participants felt a tool could especially reassure young women at average risk. Desirable features included:evidence-based, accessible (e.g. web-based), and displaying absolute (not relative) risks in multiple formats. The potential to create anxiety was a concern. Development of future tools should address these issues to optimize translation of knowledge into clinical practice.
We aimed to develop a user-centered, web-based, decision support tool for breast cancer risk assessment and personalized risk management. Using a novel model choice algorithm, iPrevent® selects one of two validated breast cancer risk estimation models (IBIS or BOADICEA), based on risk factor data entered by the user. Resulting risk estimates are presented in simple language and graphic formats for easy comprehension. iPrevent® then presents risk-adapted, evidence-based, guideline-endorsed management options. Development was an iterative process with regular feedback from multidisciplinary experts and consumers. To verify iPrevent®, risk factor data for 127 cases derived from the Australian Breast Cancer Family Study were entered into iPrevent®, IBIS (v7.02), and BOADICEA (v3.0). Consistency of the model chosen by iPrevent® (i.e., IBIS or BOADICEA) with the programmed iPrevent® model choice algorithm was assessed. Estimated breast cancer risks from iPrevent® were compared with those attained directly from the chosen risk assessment model (IBIS or BOADICEA). Risk management interventions displayed by iPrevent® were assessed for appropriateness. Risk estimation model choice was 100 % consistent with the programmed iPrevent® logic. Discrepant 10-year and residual lifetime risk estimates of >1 % were found for 1 and 4 cases, respectively, none was clinically significant (maximal variation 1.4 %). Risk management interventions suggested by iPrevent® were 100 % appropriate. iPrevent® successfully integrates the IBIS and BOADICEA risk assessment models into a decision support tool that provides evidence-based, risk-adapted risk management advice. This may help to facilitate precision breast cancer prevention discussions between women and their healthcare providers.Electronic supplementary materialThe online version of this article (doi:10.1007/s10549-016-3726-y) contains supplementary material, which is available to authorized users.
participants/materials, setting, methods: Eligible women were from families segregating BRCA1 or BRCA2 mutations and had known mutation status. Participants were aged 25 -45 years, had no personal history of cancer, retained both ovaries and were not pregnant or breastfeeding at the time of plasma storage. Circulating AMH was measured for 172 carriers and 216 non-carriers from families carrying BRCA1 mutations, and 147 carriers and 158 non-carriers from families carrying BRCA2 mutations. Associations between plasma AMH concentration and carrier status were tested by linear regression, adjusted for age at plasma storage, oral contraceptive use, body mass index and cigarette smoking.main results and the role of chance: Mean AMH concentration was negatively associated with age (P , 0.001). Mutation carriers were younger at blood draw than non-carriers (P ≤ 0.031). BRCA1 mutation carriers had, on average, 25% (95% CI: 5%-41%, P ¼ 0.02) lower AMH concentrations than non-carriers and were more likely to have AMH concentrations in the lowest quartile for age (OR 1.84, 95% CI: 1.11 -303, P ¼ 0.02). There was no evidence of an association between AMH concentration and BRCA2 mutation status (P ¼ 0.94).
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