Finding High-Risk HF Citizens in BC to support Primary Care - A Work In Progress Update

1. Finding High-Risk HF Citizens in BC to support Primary Care - A Work In Progress Update February 28, 2013 Ella Young, Director of Care Continuum and Actuarial Analytics, VCH ella.young@vch.ca

2. Objective Event 1 e.g. Diabetes diagnosis Event 2 e.g. Heart drug Prediction Event 3 e.g. Knee xray Prevented or Mitigated Event

3. Heart Failure • The inability of the heart to pump blood to meet the oxygenation and nutritional needs of the tissues, with multiple systems affected and participating in the dysfunction - more than just a weak pump • Affects men and women equally. Women tend to be older with a history of hypertension (HTN) when first diagnosed; men tend to be younger at onset and have a history of coronary artery disease (CAD) • More common as a person ages, and as more of the population ages the incidence and the prevelance of HF in the population is expected to increase • Has an annual mortality anywhere from 5% to 50%, depending on the severity of the dysfunction and associated symptoms (ie. pulmonary edema) along with other factors (ie.co-morbidities) • HF is associated with numerous symptoms and causes and subsequently there are many ways a person may present. It continues to be a syndrome that is difficult to diagnosis and to treat effectively.

4. Analysis / Modeling

5. Exploratory Analysis • Consider relative risk factors to predict future expected events • Avoid “non-response bias” • Identify people with lower, emerging, risk • Step 1 • Who? What common characteristics? • What are the implications of those characteristics? • Step 2 • scarce resource allocation for maximum ROI • Who is intervenable or impactable?

6. N = 20,148 Pop = 4.5 m. New CHF/Pop = 0.5%

7. Pre-HF Chronic Conditions of the 09/10 HF Incident Cohort

8. How good are the various models at figuring out if your patient is at high risk for heart failure?

9. “ Predictions are hard, especially about the future.” Niels Bohr Nobel Laureate in Physics

10. Preliminary Findings – No Surprises • Membership in chronic disease registries and lab tests are important • Of approx. 800 initial variables, about 15 remain (depending on strata) • Decision trees and path analysis found 100 significant variables from the 800 • Of the General Linear Models, discriminant analysis did the best - it achieved a classification rate of about 70% for age groups of interest • Neural network modeling was next at about 65-71% classification rate • Logistic regression resulted in a 60-65% classification rate

11. Preliminary Results - Survival Analysis • Examines the time it takes for events to occur, allows for time-varying predictors, and late entry into the risk set • Among the best results at 85%+ classification rate on sample data sets and stratum of interest • Indicates that MSP and drug amounts are important • Drug costs related to lipids showed significant protective effects

12. Conclusion • To date, survival analysis achieves the best result overall • Over 9 years, it finds about 85% of 45+ year-old who became HF incident • Over a shorter timeframe, predictive power increases, so within two years it yields over 90% classification rate

13. Predictive modeling: focus your most powerful interventions on the right people! Most companies realize the advantage of linking data and using advanced analytics Powerful incentives: Enough of the right people have to engage in the process to produce enough benefit to SEE a positive ROI The entire front-to-back PM process has to incorporate the best modeling, and be implemented properly Summary of“Old Way” vs. “New Way” • Not focusing your interventions on this year’s future high-cost group • Relying on claims & pharmacy-based approaches • Expecting that people will participate because “it’s the right thing to do”; weak, nonexistent, or inconsistent incentives • Poor timing, data, and PM logistics

14. Next Steps • Identify VCH citizens at high risk of HF incidence and communicate them with providers • Work with Primary Care/GPSC/BC HF SC to provide evidence-based care for those identified • More value to model ??

15. Possible Clinical Next Steps • Physician Profile Report • Actionable conversations with impactable patients • Risk-reduction and education e.g. if AMI, then cardiac rehab, condition management, etc. • Ask about swollen ankle, take BP each visit, etc. • Care plan • iCDM • Blood test -> biomarkers ? • HF clinic consult • Develop validation study with GPs ?? • Other ??

16. Physician Profile Report

17. Q & A Thanks for your feedback!

18. PROOF Centre Blood Tests for Care of Heart Failure and COPD Patients ■ Bruce McManus ■ Practice Support Program ■ Thursday, February 28, 2013

19. “The only way to keep your health is to eat what you don’t want, drink what you don’t like, and do what you’d rather not.” ~ Mark Twain

20. PROOF Centre’s Solution Blood Molecules Tissue mRNA or miRNA White blood cell mRNA or miRNA Plasma proteins and metabolites Signatures from ~38,000 blood and/or tissue components computationally analyzed

21. Test Development Process Clinical Need Biomarker (Blood Test Content) Identification / Replication Develop Clinical Laboratory Blood Test Clinical Use Clinician Driven Health Economics Evaluation, Commercialization and Implementation Strategies

22. 50% up to of heart failure patients die within 5 years of diagnosis $4B more than is spent on heart failure care each year in Canada

23. Improving care of heart failure patients Develop blood tests that will allow physicians anywhereto diagnose chronic heart failure and to monitor therapeutic responses and outcomes

24. How will our Blood Tests be used to manage Heart Failure? Blood tests to monitor response to therapy and hospitalization / deterioration outcomes Family practice physician Person with heart failure symptoms DHF Diagnostic blood test SHF Person with risk for heart failure Admin Data Predictor (Ella Young) DHF = diastolic heart failure SHF = systolic heart failure

25. COPD Exacerbations [lung attacks] are theleading causeof hospitalization in Canada $1.3B/year is spent on hospitalization Affects >200M people globally

26. Improving care of patients with COPD • Develop blood tests that will allow doctors to identify who • will have frequent COPD exacerbations [lung attacks] • is having a COPD exacerbation

27. How Will Our Prognostic Test Change COPD Patient Care?   High AECOPD Risk     Prognostic Blood Test Evidence-Based Treatment     Low AECOPD Risk  

28. Benefitsfor patients with COPD • Improved evidence-based management of COPD • More family medicine-based care, fewer COPD hospitalizations • By 2020, help >450,000 patients/year, save >50,000 QALY’s/year, and >$600M/year

29. How do we translate our results to physicians and care teams?Cell phone, iPad, computer, etc Score: 1… 10 Short Explanation Mouse-over: Full Recommendation

30. Our Passion • Focused on each patient • Grounded in clinical reality • State-of-the-art analytic algorithms and access to data • Quality-assured technologies • Health economics modeling • Ability to engage key partners across all sectors

31. THANK YOU www.proofcentre.ca bruce.mcmanus@hli.ubc.ca