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Enhancing Familial Breast Cancer Risk Stratification in Primary Care through Argumentation

Explore the value of decision support tools for familial breast cancer in primary care, analyzing complex case management & risk stratification using argumentation processes and a novel family history risk assessment software.

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Enhancing Familial Breast Cancer Risk Stratification in Primary Care through Argumentation

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  1. Improving risk stratification of familial breast cancer in primary care using argumentationDr Stephen WengNIHR Research Fellow Division of Primary CareUniversity of Nottingham

  2. Value of decision support tools in primary care decision making Decision Support Cost-effectiveness

  3. ‘Guideline-informed Tools’ Development of Family History Risk Assessment Software for primary care Limited by its ability to handle complex cases

  4. Dealing with complex cases in the NIHR funded familial breast cancer study Total Recruited (Intervention Arm) N = 1147 Family History Risk Assessment (FaHRAS) Population Risk & Manage in Primary Care 79.6% N = 913 High Risk & Straight Referral to Secondary Care 5.6% N = 64 Risk Uncertain 14.8% N = 170 Case-by-Case Discussion with Secondary Care Manage in Primary Care 61.8% N = 105 Referral to Secondary Care 38.2% N = 65

  5. Causes of uncertainty: To refer or not to refer? Ovarian cancer diagnosis occurred age 60 or above Breast cancer diagnosis occurred age 40 or above HNPCC Gene & family histories of ovarian, colorectal, stomach cancers in relatives under age of 50 Average age of diagnosis in first & second degree relative with breast cancer under 50 Score of 15 or above on the Manchester Scale for ovarian cancer & living relative with ovarian cancer results in referral for direct gene testing Polyps may be linked to ovarian cancer Score of 20 or above on the Manchester Scale for breast cancer Early onset cancers in distant relatives Score of 17 or above on the Manchester Scale for ovarian cancer & two relatives with ovarian cancer who have died results inreferral for indirect gene testing Melanoma genetically related to breast cancer Living relative with ovarian cancer

  6. Argumentation Explained • Quantitative [0,1] degree of belief (e.g. probability, possibility) [-1,+1] bipolar measures (e.g. belief functions) {1,2,3,…n} ad hoc weighting of arguments • Qualitative + “supporting” arguments {+,-} “supporting” and “opposing” arguments {++,--, +, -} … plus “confirming” and “excluding” • Naturalistic Linguistic (perhaps, possible, probable, plausible …) Advantages: • Visualisation of arguments in an “argument map” • Probabilistic evidence synthesis from disparate sources • Inclusion of conflicting “arguments” • Memory: reasoning/justification of reaching certain conclusions • Mathematical quantification of uncertainty • Formal evaluation and validation using data

  7. Argumentation Process (qualitative example) Argument 1: France is country  Justification (evidence: United Nations)  Strong Argument 2: France is in Europe  Justification (evidence:European Map)  Strong Conclusion: France is a country in Europe

  8. Simple Argument Refer No Referral Defeated First cousin with stomach cancer under age 50 Defeated Arguments For Arguments Against Justification Justification

  9. Multiple Arguments Refer Refer No Referral No Referral Defeated Defeated First cousin with stomach cancer under age 50 HNPCC Gene Defeated Defeated Defeated Arguments For Arguments For Arguments Against Arguments Against Justification Justification Justification Justification

  10. Argumentation: UK Royal Free Breast Cancer Management Tool

  11. Familial Breast Cancer Argumentation Prototype

  12. Strengths: • Efficiency in handling of complex family histories – information capture resembles full family history pedigrees • Reduction of uncertainty in referral decisions - reduce workload on secondary care • Front end pro-forma simplicity – step-by-step primary care workflow • System can integrate new information and evidence (new arguments) – Bayesian learning approach Limitations: • Challenges for integration into existing primary care computer systems • How should we visualise family history information for GPs? pedigrees, relationship diagrams, tabular format?? • Balancing act between capturing all intricacies of secondary care decision making but simple enough for GPs to use and understand Going forward: • Validation project ongoing using patient data from primary care and secondary care outcomes of referrals • Developing more appealing and simple visualisations for the beta version • Approaching GP computer systems suppliers for developing integrated software • Needs implementation trial – cluster randomised controlled trial • Cost-effectiveness analysis along side trial

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