iprevent Breast Cancer Risk Assessment and Risk Management Decision Support Tool Kelly Anne Phillips MBBS MD FRACP

Transcription

1 iprevent Breast Cancer Risk Assessment and Risk Management Decision Support Tool Click to edit Master subtitle style Kelly Anne Phillips MBBS MD FRACP Medical Oncologist, Head Peter Mac Breast and Ovarian Cancer Risk Management Clinic

2 Relative Frequency Breast Cancer Facts 14,000 BC diagnosed per year* 13% of women get BC in their lifetime, BUT risk not normally distributed MOST women are at low risk (<4%)** Those at increased risk can be identified and targeted for prevention Lifetime Risk % *Australian Institute for Health and Welfare 2012 **Hopper J. Epidemiol Perspec Innov 2011

3 Cancer Australia Defined Risk Categories High risk Moderate risk Average >3 times population risk times population risk <1.5 times population risk

4 Strategies For Managing Breast Cancer Risk Strategy Relative Risk Reduction Target Group Bilateral Mastectomy >90% High Risk Bilateral Salpingooophorectomy (premenopausal ) Medication - SERMS or AIs Up to 50% in mutation carriers BRCA Mutation Carriers FHx of ovarian cancer 33-65% Moderate or High Risk Screening - Mammography / MRI 0% All women

5 Background All Australian women should have their personal BC risk formally assessed and managed Most do not Inadequate clinical tools are available to help primary care and specialist clinicians assess and manage BC risk

6 What is iprevent? User friendly, personalised, web-based risk assessment and risk management decision support tool Developed with early user input and designed to meet clinicians needs For use by GPs, breast surgeons, and FCC clinicians to help: o estimate a woman s personal BC risk (using well-validated IBIS and BOADICEA risk assessment algorithms) o give tailored estimates of the absolute (not relative!) risks and benefits of each risk management option for that woman o registered trademark and Australian innovation patent

7 iprevent Development ANZBCTG Discretionary Funding Needs Assessment GPs Breast Surgeons FCC Clinicians BreastScreen Consumers Programming iprevent Software Integrate existing IBIS and BOADICEA risk assessment models NHMRC Project Grant Verification ABCFS Data Settings General Practice FCC Clinicians Breast Surgeons rooms/outpatients Piloting Type Paper scenarios Actors Real patients iprevent Revision Randomised trials

8 Needs Assessment Focus Groups Engaging the End User Led by Dr Louise Keogh Senior lecturer/sociologist The University of Melbourne Collins IM et al The Breast 2014; 23: Phillips KA et al Aust J Primary Healthcare 2015;

9 Focus Groups Clinician type Focus groups (n=7) Participants (n=55) Breast Surgeons 2 12 G.P s and Practice Nurses 2 12/5 Breastscreen staff 1 5 FCC staff 2 21 Focus groups with approx 40 consumers still to be completed.

10 Relevant Findings Overall Clinicians currently have difficulty accurately and easily assessing and communicating breast cancer risk Also difficult to assess and communicate the absolute benefits and disadvantages of risk management interventions There is no optimal tool that combines accurate, easy, risk assessment with risk management, most don t use a tool

11 Relevant Findings Overall Clinicians want a tool that is: evidence-based accessible woman can enter her risk factor data before the consultation to save time provides 10 year and residual lifetime risks displays personalised absolute (not relative) risks and risk reductions in multiple formats (e.g. graph and pictogram) to account for different patient information needs printable output

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13 How does iprevent work? A woman enters her own data including: personal risk factors e.g. age, HRT use, previous LCIS family history mutation status, if tested

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15 How does iprevent work? iprevent takes the inputted information Chooses the most appropriate algorithm to estimate BC risk (IBIS or BOADICEA) Interfaces with the relevant algorithm via the internet

16 iprevent Chooses the Most Appropriate Risk Assessment Algorithm Adapted from Amir E et al JNCI 2010; 102:

17 How does iprevent work? Estimates 10 year and residual lifetime BC risk and compares to population risk Calculates the Cancer Australia risk category using residual lifetime risk, initially provides a qualitative assessment of risk High risk: substantially increased risk Moderate risk: somewhat increased risk Population risk: average risk

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19 How does iprevent work? If desired gives more detail on BC risk Presents the absolute BC risks in an understandable way: words, pictograms, graphs Women can choose how much or how little detail they want and in which format

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21 Lifetime Risk - Pictogram

22 Lifetime Risk - Graph

23 How does iprevent work? Presents risk adapted intervention options based on Cancer Australia recommendations Presents the absolute risk reductions for each intervention in the same formats as BC risk Women can choose how much or how little detail they want and in which format

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27 Risk Reducing Medication - Pictogram

28 Risk Reducing Medication - Graph

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30 Risk of Serious Side Effects

31 iprevent Verification Aim to ensure the processes and pathways (logic) within iprevent are correctly implemented Used comprehensive risk factor data from 127 unaffected women in the population-based BC family case - control study, the Australian Breast Cancer Family Study Data were manually entered for each woman into IBIS, BOADICEA and iprevent iprevent outcomes in terms of algorithm choice, BC risk presented and interventions offered were recorded

32 Model choice by iprevent BOADICEA (59%) High risk = 36 Moderate risk =31 Population risk = 8 IBIS (41%) High risk = 21 Moderate risk =12 Average risk =19

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34 Verification of iprevent risk estimates 10 year and residual lifetime risk estimates from iprevent were compared to results direct from the algorithm that iprevent selected 21/127 (17%) showed discrepant risk estimates

35 Discrepant Risk Estimates iprevent Flowchart 10 years Lifetime Model selected arm risk% risk% Model 10 years Lifetime risk% risk% Difference (iprevent - Model) 10 years Lifetime risk risk C IBIS C IBIS C IBIS C IBIS C IBIS C IBIS C IBIS C IBIS E IBIS E IBIS B BOADICEA B BOADICEA B BOADICEA B BOADICEA B BOADICEA E IBIS E IBIS D2 BOADICEA C IBIS C IBIS C IBIS Mean variation was +0.2% (10yr) and +0.5% (lifetime)

36 Why are there some variations in the results? In 10 cases the variation was minimal and due to rounding 10 cases are due to the fact that iprevent does not collect family structure information on women with no family history of cancer. In 1 case the cause is still being investigated (0.3% / 0.5% discrepancy) In none of these 21 discrepant cases was the category of risk estimate affected

37 Information pages shown Risk Category n BPM RRSO Meds MRI Lifestyle Screening Population Moderate # High * # Risk reducing medication recommendations not shown for moderate risk women under age 35 * RRSO recommendation only given for high risk BRCA1/2 mutation carriers and high risk women with a family history of ovarian cancer

38 Accuracy of risk reduction estimates n BPM RRSO Meds MRI Lifestyle Screening Output page /57 36/36 94/94 57/57 127/ /127 Risk reduction 100% 100% 100% - - -

39 iprevent Development ANZBCTG Discretionary Funding Needs Assessment GPs Breast Surgeons FCC Clinicians BreastScreen Consumers Programming iprevent Software Integrate existing IBIS and BOADICEA risk assessment models NHMRC Project Grant Verification ABCFS Data Settings General Practice FCC Clinicians Breast Surgeons rooms/outpatients Piloting Type Paper scenarios Actors Real patients iprevent Revision Randomised trials

40 Acknowledgements iprevent Study Investigators Bruce Mann, Phyllis Butow, John Hopper, Jon Emery, Rod Jackson, Alison Trainer, Louise Keogh, Adrian Bickerstaffe, Ian Collins, Jack Cuzick, Antonis Antoniou. iprevent Study Staff Prue Weideman & Emma Steel Consumer Representatives Leslie Gilham & Debbie Sandler Funding NHMRC Project grant # and NBCF Practitioner Fellowship awarded to KAP