Clinical software development for the Web: lessons learned from the BOADICEA project

Genetically guided precision medicine clinical decision support tools: a systematic review

OBJECTIVES

Patient care using genetics presents complex challenges. Clinical decision support (CDS) tools are a potential solution because they provide patient-specific risk assessments and/or recommendations at the point of care. This systematic review evaluated the literature on CDS systems which have been implemented to support genetically guided precision medicine (GPM).

MATERIALS AND METHODS

A comprehensive search was conducted in MEDLINE and Embase, encompassing January 1, 2011-March 14, 2023. The review included primary English peer-reviewed research articles studying humans, focused on the use of computers to guide clinical decision-making and delivering genetically guided, patient-specific assessments, and/or recommendations to healthcare providers and/or patients.

RESULTS

The search yielded 3832 unique articles. After screening, 41 articles were identified that met the inclusion criteria. Alerts and reminders were the most common form of CDS used. About 27 systems were integrated with the electronic health record; 2 of those used standards-based approaches for genomic data transfer. Three studies used a framework to analyze the implementation strategy.

DISCUSSION

Findings include limited use of standards-based approaches for genomic data transfer, system evaluations that do not employ formal frameworks, and inconsistencies in the methodologies used to assess genetic CDS systems and their impact on patient outcomes.

CONCLUSION

We recommend that future research on CDS system implementation for genetically GPM should focus on implementing more CDS systems, utilization of standards-based approaches, user-centered design, exploration of alternative forms of CDS interventions, and use of formal frameworks to systematically evaluate genetic CDS systems and their effects on patient care.

CanRisk Tool-A Web Interface for the Prediction of Breast and Ovarian Cancer Risk and the Likelihood of Carrying Genetic Pathogenic Variants

BACKGROUND

The CanRisk Tool (https://canrisk.org) is the next-generation web interface for the latest version of the BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) state-of-the-art risk model and a forthcoming ovarian cancer risk model.

METHODS

The tool captures information on family history, rare pathogenic variants in cancer susceptibility genes, polygenic risk scores, lifestyle/hormonal/clinical features, and imaging risk factors to predict breast and ovarian cancer risks and estimate the probabilities of carrying pathogenic variants in certain genes. It was implemented using modern web frameworks, technologies, and web services to make it extensible and increase accessibility to researchers and third-party applications. The design of the graphical user interface was informed by feedback from health care professionals and a formal evaluation.

RESULTS

This freely accessible tool was designed to be user friendly for clinicians and to boost acceptability in clinical settings. The tool incorporates a novel graphical pedigree builder to facilitate collection of the family history data required by risk calculations.

CONCLUSIONS

The CanRisk Tool provides health care professionals and researchers with a user-friendly interface to carry out multifactorial breast and ovarian cancer risk predictions. It is the first freely accessible cancer risk prediction program to carry the CE marking.

IMPACT

There have been over 3,100 account registrations, and 98,000 breast and ovarian cancer risk calculations have been run within the first 9 months of the CanRisk Tool launch.

Clinicians' use of breast cancer risk assessment tools according to their perceived importance of breast cancer risk factors: an international survey

The BOADICEA breast cancer (BC) risk assessment model and its associated Web Application v3 (BWA) tool are being extended to incorporate additional genetic and non-genetic BC risk factors. From an online survey through the BOADICEA website and UK, Dutch, French and Swedish national genetic societies, we explored the relationships between the usage frequencies of the BWA and six other common BC risk assessment tools and respondents' perceived importance of BC risk factors. Respondents (N = 443) varied in age, country and clinical seniority but comprised mainly genetics health professionals (82%) and BWA users (93%). Oncology professionals perceived reproductive, hormonal (exogenous) and lifestyle BC risk factors as more important in BC risk assessment compared to genetics professionals (p values < 0.05 to 0.0001). BWA was used more frequently by respondents who gave high weight to breast tumour pathology and low weight to personal BC history as BC risk factors. BWA use was positively related to the weight given to hormonal BC risk factors. The importance attributed to lifestyle and BMI BC risk factors was not associated with the use of BWA or any of the other tools. Next version of the BWA encompassing additional BC risk factors will facilitate more comprehensive BC risk assessment in genetics and oncology practice.

Use of the BOADICEA Web Application in clinical practice: appraisals by clinicians from various countries

The 'BOADICEA' Web Application (BWA) used to assess breast cancer risk, is currently being further developed, to integrate additional genetic and non-genetic factors. We surveyed clinicians' perceived acceptability of the existing BWA v3. An online survey was conducted through the BOADICEA website, and the British, Dutch, French and Swedish genetics societies. Cross-sectional data from 443 participants who provided at least 50% responses were analysed. Respondents varied in age and, clinical seniority, but mainly comprised women (77%) and genetics professionals (82%). Some expressed negative opinions about the scientific validity of BOADICEA (9%) and BWA v3 risk presentations (7-9%). Data entry time (62%), clinical utility (22%) and ease of communicating BWA v3 risks (13-17%) received additional negative appraisals. In multivariate analyses, controlling for gender and country, data entry time was perceived as longer by genetic counsellors than clinical geneticists (p < 0.05). Respondents who (1) considered hormonal BC risk factors as more important (p < 0.01), and (2) communicated numerical risk estimates more frequently (p < 0.001), judged BWA v3 of lower clinical utility. Respondents who carried out less frequent clinical activity (p < 0.01) and respondents with '11 to 15 years' seniority (p < 0.01) had less favourable opinions of BWA v3 risk presentations. Seniority of '6 to 10 years' (p < 0.05) and more frequent numerical risk communication (p < 0.05) were associated with higher fear of communicating the BWA v3 risks to patients. The level of genetics training did not affect opinions. Further development of BWA should consider technological, genetics service delivery and training initiatives.

Validating the IBIS and BOADICEA Models for Predicting Breast Cancer Risk in the Iranian Population

BACKGROUND

Several approaches have been suggested for incorporating risk factors to predict the future risk of breast cancer. The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and International Breast Cancer Intervention Study (IBIS) are among these approaches. We compared the performance of these models in discriminating between cases and noncases in the Iranian population.

PATIENTS AND METHODS

We performed a case-control study in Tehran, from November 2015 to April 2016, and enrolled 1633 women aged 24 to 75 years, including 506 cases of breast cancer, 916 population-based controls, and 211 clinic-based controls. We calculated and compared the risk of breast cancer predicted by the IBIS and BOADICEA models and the logistic regression model. For model discrimination, we computed the area under the receiver operating characteristic (ROC) curve.

RESULTS

The risk of breast cancer predicted by the IBIS model was higher than the BOADICEA model, but lower than the logistic model. The area under the ROC plots indicated that the logistic regression model showed better discrimination between cases and noncases (71.53%) compared with the IBIS model (49.36%) and BOADICEA model (35.84%). Based on the Pierson correlation coefficient, the correlation between IBIS and BOADICEA models was much stronger than the correlation between IBIS and logistic models (0.3884 and 0.1639, respectively).

CONCLUSION

The IBIS model discriminated cases and noncases better than the BOADICEA model in the Iranian population. However, the discrimination of the logistic regression model, which included a larger array of familial, genetic, and personal risk factors, was better than the 2 models.

Recommendations on breast cancer screening and prevention in the context of implementing risk stratification: impending changes to current policies

In recent years, risk stratification has sparked interest as an innovative approach to disease screening and prevention. The approach effectively personalizes individual risk, opening the way to screening and prevention interventions that are adapted to subpopulations. The international perspective project, which is developing risk stratification for breast cancer, aims to support the integration of its screening approach into clinical practice through comprehensive tool-building. Policies and guidelines for risk stratification-unlike those for population screening programs, which are currently well regulated-are still under development. Indeed, the development of guidelines for risk stratification reflects the translational aspects of perspective. Here, we describe the risk stratification process that was devised in the context of perspective, and we then explain the consensus-based method used to develop recommendations for breast cancer screening and prevention in a risk-stratification approach. Lastly, we discuss how the recommendations might affect current screening policies.

BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface

BACKGROUND

The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is a risk prediction model that is used to compute probabilities of carrying mutations in the high-risk breast and ovarian cancer susceptibility genes BRCA1 and BRCA2, and to estimate the future risks of developing breast or ovarian cancer. In this paper, we describe updates to the BOADICEA model that extend its capabilities, make it easier to use in a clinical setting and yield more accurate predictions.

METHODS

We describe: (1) updates to the statistical model to include cancer incidences from multiple populations; (2) updates to the distributions of tumour pathology characteristics using new data on BRCA1 and BRCA2 mutation carriers and women with breast cancer from the general population; (3) improvements to the computational efficiency of the algorithm so that risk calculations now run substantially faster; and (4) updates to the model's web interface to accommodate these new features and to make it easier to use in a clinical setting.

RESULTS

We present results derived using the updated model, and demonstrate that the changes have a significant impact on risk predictions.

CONCLUSION

All updates have been implemented in a new version of the BOADICEA web interface that is now available for general use: http://ccge.medschl.cam.ac.uk/boadicea/.