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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 [email protected] Objective Event 1 e.g. Diabetes diagnosis Event 2 e.g. Heart drug T-(24+y) mos. Prediction T-(24+x) mos. T-24 mos. Event 3 e.g. Knee xray HF Prevented or Mitigated Event 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. 3 Analysis / Modeling 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? N = 20,148 Pop = 4.5 m. New CHF/Pop = 0.5% Pre-HF Chronic Conditions of the 09/10 HF Incident Cohort % of HF patients with another select Chronic Disease 90% HTN 80% 70% 60% 50% IHD OA, DEPR, DM ANGIO 40% COPD 30% CKD OSTEOPOROSIS 20% DEMENTIA PTCA, CABG 10% STROKE RA 0% -8 -7 -6 -5 -4 -3 -2 Average incidence time versus HF (years) -1 0 How good are the various models at figuring out if your patient is at high risk for heart failure? “ Predictions are hard, especially about the future.” Niels Bohr Nobel Laureate in Physics 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 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 Conclusion • To date, survival analysis achieves the best result overall – Over 9 years, it finds about 85% of 45+ yearold who became HF incident – Over a shorter timeframe, predictive power increases, so within two years it yields over 90% classification rate Summary of “Old Way” vs. “New Way” Not focusing your interventions on this year’s future high-cost group Relying on claims & pharmacybased 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 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 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 ?? 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 ?? Physician Profile Report Q&A Thanks for your feedback! PROOF Centre Blood Tests for Care of Heart Failure and COPD Patients ■ Bruce McManus ■ Practice Support Program ■ Thursday, February 28, 2013 “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 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% of heart failure patients die within up to 5 years of diagnosis more than $4B is spent on heart failure care each year in Canada 23 Improving care of heart failure patients Develop blood tests that will allow physicians anywhere to diagnose chronic heart failure and to monitor therapeutic responses and outcomes 24 How will our Blood Tests be used to manage Heart Failure? Family practice Person with physician heart failure symptoms Diagnostic blood test Person with risk for heart failure DHF SHF Blood tests to monitor response to therapy and hospitalization / deterioration outcomes Admin Data Predictor (Ella Young) DHF = diastolic heart failure SHF = systolic heart failure 25 COPD Exacerbations [lung attacks] are the leading cause of 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 Low AECOPD Risk Evidence-Based Treatment 28 Benefits for 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 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 [email protected]