RESUMEN
With advances in genetic epidemiology, increasingly large amounts of pedigree-related information are being collected by family studies, including twin studies. To date, biomedical data management systems that cater for family data have usually done so as part of their standard (non-family-centric) data model. Consequently, data managers with computing expertise are needed to extract family datasets and perform family-centric operations. We present a robust approach to handling large family datasets. Our approach is implemented as a new module which extends the capabilities of The Ark, an open-source web-based biomedical data management tool. Using an algorithm designed by the authors, the pedigree module dynamically infers family relationships for any selected subject (not necessarily the proband). A web interface allows researchers to create, update, delete and navigate parental and twin relationships between subjects, and bulk import/export pedigrees. Consanguineous relationships can be captured, and configurable pedigree visualizations generated. A web services interface provides interoperability.
Asunto(s)
Visualización de Datos , Informática Médica/métodos , Epidemiología Molecular/métodos , Linaje , Programas Informáticos , Algoritmos , Australia , Demografía , Humanos , InternetRESUMEN
Summary: The Ark is an open-source web-based tool that allows researchers to manage health and medical research data for humans and animals without specialized database skills or programming expertise. The system provides data management for core research information including demographic, phenotype, biospecimen and pedigree data, in addition to supporting typical investigator requirements such as tracking participant consent and correspondence, whilst also being able to generate custom data exports and reports. The Ark is 'study generic' by design and highly configurable via its web interface, allowing researchers to tailor the system to the specific data management requirements of their study. Availability and Implementation: Source code for The Ark can be obtained freely from the website https://github.com/The-Ark-Informatics/ark/ . The source code can be modified and redistributed under the terms of the GNU GPL v3 license. Documentation and a pre-configured virtual appliance can be found at the website http://sphinx.org.au/the-ark/ . Contact: adrianb@unimelb.edu.au. Supplementary information: Supplementary data are available at Bioinformatics online.
Asunto(s)
Investigación Biomédica/métodos , Bases de Datos Factuales , Programas Informáticos , Animales , Femenino , Humanos , Internet , Masculino , LinajeRESUMEN
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.