Applying Analytical Approaches to Characterize the Impact of Clinical Gaps and Profiles on the Management of RA. Karyn Ruiz-Cordell, MA, PhD

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2 Applying Analytical Approaches to Characterize the Impact of Clinical Gaps and Profiles on the Management of RA Karyn Ruiz-Cordell, MA, PhD

3 DISCLOSURE Chief Data Scientist, VP of Analytics RealCME, NYC

4 Introduction RA is a chronic autoimmune disorder that requires highly individualized treatment approaches that integrate consideration of multiple complex patient characteristics. Meta-analytic study Goal: Employ predictive analytics to identify defined areas that if targeted will lead to the greatest success in achieving treatment goals for patients with RA

5 Methods The dataset 7 CURRICULA 25 ACTIVITIES ~ 2,400 PARTICIPANTS

6 Topics Covered in Included Curricula Treatment goals (safety and efficacy) Early diagnosis Management of established RA Managing a non-responder Treat-to-Target Incorporating (ACR) treatment guidelines Biologic therapies Cardiovascular issues in RA patients

7 Methods Parameters ACTIVITY OUTCOMES PROTOCOL Measure Moore s Levels 1 4 Paired Pre- and Post-Test questions Employs Knowledge, Competence, Confidence, and practice strategy questions CURRICULUM OUTCOMES PROTOCOL Measure Moore s Levels 1 5 RealIndex question: Prior to the first activity After completion of each activity Post-curriculum assessment survey 6+ weeks after last activity PRE- CURRICULUM REALINDEX ASSESSMENT MATCHED PRE/POST QUESTIONS POST- CURRICULUM REALINDEX ASSESSMENT ACTIVITY 1 ACTIVITY 2 ACTIVITY 3

8 Meta-Analysis

9 Results Significant gains and high post-test scores across all domains High standard deviations

10 Results Knowledge Gaps Reasons for withholding or delaying tx with a TNF inhibitor Target-based pt. management T2T guidelines regarding composite measures Prognostic indicators Safety of biologic therapies ACR guideline targets Clinical trial data

11 Results Competence Gaps RA & cardiovascular risk Assessing disease activity Management of moderate RA Early management of RA & combination therapy Tx. intensification

12 Results Confidence & Practice Strategy How often do you use the following composite measures of disease activity to monitor disease progression and treatment response in patients with RA (CDAI, RAPID3)

13 Results - Performance Gaps If full workup is consistent with rheumatoid arthritis and disease activity is moderate without poor prognostic features, consider nonbiologic or biologic DMARD Order anti-cyclic citrullinated peptide Add a non-tnf inhibitor agent, such as abatacept, to the current regimen If workup is consistent with rheumatoid arthritis, initiate DMARD therapy within 6 months of diagnosis Add a nonbiologic DMARD Start methotrexate before progressing to biological DMARDs unless there is a clear contraindication to methotrexate. Continue methotrexate for another 6 months and re-evaluate Add a TNF inhibitor to current methotrexate treatment

14 Gaps From Meta-Analysis Implement and/or optimize the utilization of target-based patient management within the next three months to standard patient care and optimize overall outcomes (T2T) Diagnostic indicators (assessing severity, disease activity, prognosis) Early dx and tx Biologic Therapies Decide when patients will likely benefit from the use of biological therapies Define the role of newer biologic therapies in the management of early RA Identify efficacy and safety profiles of biologic agents and characteristics of patients with RA that guide the selection of biologic DMARD therapy.

15 Predictive Modeling

16 Identifying the Target (Gap) Based on the common gaps identified across the meta-analysis, confirmed in all learning domains, the related statements from the Performance metric (the RealIndex) were extracted to create the Target for Prediction: RA Treatment Optimization Composite Score

17 Identifying the Predictors Finding the Model

18 The Model Predictive Profiling Results of Linear Modeling: : All significant drivers were entered, in addition to learner specific variables (e.g., demographics, # of patients tx ed, barriers to tx, etc.).

19 The Model Predictive Profiling Results of K-Means Modeling: In order to more fully understand the importance of Region as a driver, a cluster analysis was performed. Results identified a specific regional grouping (cluster 1) as most representative of our target.

20 Results of PM: RA Clinician Profile Low knowledge concerning ACR target guidelines Unsure of what factors impact delaying tx with a TNF-inhibitor Low confidence related to applying ACR recommendations for tx with DMARDs (including biologics) Primarily practicing in the South or West/East coasts Low knowledge concerning the diagnostic tests High confidence related to employing T2T approach Low use of the global assessment of pain scale

21 How do we apply ALL this information? Create future education that: 1. Addresses specific knowledge gaps T2T Diagnostic indicators Treatment timing 2. Addresses Over-confidence through peer-benchmarking and possibly sharing study results T2T 3. Addresses Under-confidence through reinforcement Use of biologics 4. Reinforces the importance of specific recommended practice strategy behaviors Disease activity In the area of treatment optimization for patients with RA.

22 Discussion