Treat-to-target in RA: Collaboration To Improve adOption and adhereNce (TRACTION)

The purpose of this study is to determine if a Learning Collaborative is an effective tool to improve adoption and adherence to a Treat-to-Target (TTT) treatment strategy in U.S. rheumatology practices for the treatment of Rheumatoid Arthritis (RA). The TTT strategy has been embraced by the American College of Rheumatology through the RA Treatment Guidelines and by EULAR,however several lines of evidence suggest that TTT is not practiced consistently across rheumatology settings.Participating practices will be randomized to receive the Learning Collaborative intervention in one of two phases. Coaching consistent with Learning Collaborative practices will be used to promote adherence to TTT. Chart abstraction will be used to determine if the Learning Collaborative was an effective tool to increase TTT adherence.

Twelve rheumatology practices around the U.S. will be recruited to participate in a Learning Collaborative (LC). Eligible practices will have at least 50 rheumatoid arthritis patients, at least 2 rheumatologists, and an electronic medical record or typed notes. Each practice will be randomized to receive the intervention in either phase 1 (first 9 months) or phase 2 (second 9 months). The collaborative will consist of a series of 9 Learning Sessions (1 conducted face-to-face, the remaining conducted via webinar) as well as regular coaching sessions and collaborative sessions. The structure of the collaborative will be such that each learning session will focus on resources, expertise, and best practices that address common barriers to Treat-to-Target (TTT) adoption. The follow-up coaching and collaborative sessions will give providers the opportunity to reflect on what they have learned from their own practices and from each other. The first Learning Session was a one-day face-to-face meeting that consisted of orienting the teams to the Model for Improvement, describing the Change Package and its content, conducting team building activities focused on developing ideas for plan-do-study-act (PDSA) cycles, and cross-team learning activities. (The PDSA cycles refer to tests of change, using four stages, that are performed as part of a quality improvement process.) The day primarily consisted of discussion sessions, and several lectures on TTT, disease activity measures, and shared decision making helped orient teams to the Change Package contents. There was ample time for teams to get feedback from expert faculty on their proposed tests of change and results to date. Subsequent Learning Sessions were conducted via webinar. We developed a web-based collaborative tool for the Learning Collaborative. It helped manage contents being shared across teams (i.e., key resources, PDSAs), displayed monthly improvement metrics, and provided a discussion board with conversation "threads." The tool was used in all Learning Sessions. Specific patient data will never be identified in the sessions of the Collaborative, and no specific patient data will be shared between the participating providers. Providers will be able to interact with each other to share general practice patterns and behaviors, but sensitive patient information will not be shared. The intervention will be conducted in 2 phases. In phase 1, a group of 5practices [Cohort 1] will initiate the collaborative, which will last 9 months. After 9 months, phase 2 will begin with the remaining practices [Cohort 2] initiating the second collaborative of the same content as cohort 1, and the second collaborative will also take 9 months to complete. The cohort 1 and cohort 2 collaborative groups will not have any interaction with each other, but cohort 2 will serve as a concurrent control to cohort 1 in the first 9 months of the intervention. De-identified data will be collected from participating sites to compare TTT practices prior to and following the Learning Collaborative intervention using a chart review abstraction tool. The tool has four items: 1) documentation of a treatment target; 2) documentation of shared decision making; 3) documentation of a disease activity measure; and 4) evidence that this information guided treatment decisions. This tool will be used to grade the visit in the two months immediately prior to the start of Phase 1 and the visit in the two months immediately prior to the end of Phase 1; a change score between baseline to follow-up will be calculated. The range of change in implementation of TTT can vary from -4 (worsening from 4 at baseline to 0 at follow-up) to +4 (improvement from 0 at baseline to 4 at follow-up). Thus, the range of change scores will be from -4 to +4, a 9-point ordinal scale. The baseline visit will be considered the patient visit within two months before the start of Phase 1 (January 2015). If there are multiple visits in this time-frame, then the note for the visit most proximal to January 2015 (start of the study) will be assessed. The end of Phase 1 visit will be considered the visit within two months before November 1, 2015. Again, if there are multiple visits in this time-frame, then the note for the visit most proximal to October 1, 2015 will be assessed. When assessing the performance at each site, we will randomly sample the medical records of patients with RA who have visits documented within these two time-frames. Surveys will also be collected from patients and providers to assess satisfaction with the patient-provider interaction and shared decision-making. RA patients were randomly selected at each site to complete a questionnaire rating their satisfaction with the shared decision-making process using the three item collaboRATE scale. This was carried out at the start of Phase I and will be re-assessed at the end of Phase 1. Similarly, we asked providers involved in the Learning Collaborative from all sites in both groups to complete a modified version of this questionnaire; this will also be re-assessed at the end of Phase 1. During Phase 2, the only outcomes we plan to assess are the primary outcome of TTT implementation as well as the patient collaboRATE scale. The primary analysis will compare the primary outcome among the Learning Collaborative sites with the control sites. The mean change in implementation of TTT for the Learning Collaborative arm will be compared with implementation of TTT for the control arm after accounting for intra-cluster correlation using linear mixed models. Although the normality assumption may be violated when the outcome variable is ordinal, linear mixed models should still be valid for the proposed sample size. Treatment arm will be the exposure of interest. Covariates included in the model will include provider-level characteristics (such as age, gender, training), patient-level characteristics (age, gender, baseline disease activity, baseline RA drugs), and other covariates found to be unbalanced at baseline. While these characteristics should be balanced given the random assignment to treatment arm, the small number of centers in each arm opens the possibility of baseline differences and thus the rationale for adjustment. Similarly, for the secondary outcomes (dichotomous variables), we will use generalized linear mixed models for binary outcomes. The trial has been powered based on the primary outcome - the estimated difference in change in TTT implementation between the Learning Collaborative intervention and the control sites. Several other assumptions underpin the sample size estimation. First, the control group would have no or only small change (0-5%) change in implementation of TTT compared with a change in the intervention group of 20-40%, an improvement level observed in a similar prior trial using a Learning Collaborative. Second, we will include 5 sites in the intervention group, and 6 in the control group. We assume that average number of providers in each practice is 5, and expect there would be substantial intra-cluster correlation (ICC) among patients within a given provider. We conservatively assume a range of ICC is 0.1-0.3 based on prior work. Third, the significant level (alpha) would be two-sided 0.05, and the goal power would be 80%. Based on these assumptions, we estimated sample sizes for the proposed trial. The required number of patients per provider needed to detected meaningful differences was calculated for each set of assumptions. Based on these estimates, we will review a random 6 patients per provider with eligible visits to ensure an adequate sample size to achieve 80% power.

The learning collaborative as designed will be delivered to this arm during phase I, which will last a period of approximately 9 months. After the 9 months, there will be passive follow-up of this arm to see if outcomes following the first 9 months are sustained.

This arm will serve as a control for the Phase I intervention arm during the first 9 months of the study for primary analysis. After the first 9 months, the Phase II intervention arm will receive the learning collaborative during the following 9 months.

A learning collaborative is an established technique that uses the model for improvement to bridge the gap between what we know and what we do. Structured learning sessions and collaborative coaching are used to guide small cycles of change called Plan-Do-Study-Act (PDSA) cycles. This quality improvement intervention will help providers adopt treat-to-target practices through the structured learning collaborative.

Chart abstraction will determine how strongly providers adhered to treat-to-target principles before and following the Learning Collaborative.

Chart abstraction will determine how TTT adherence correlates with patient disease activity and adverse events before and following the Learning Collaborative.

Surveys will determine how the TTT principle of shared decision making affects satisfaction with the patient and provider experiences before and following the Learning Collaborative.

Chart abstraction will determine if a directional relationship exists between adherence to TTT and resource utilization (frequency of medications, labs, clinical exams, etc.) before and following the Learning Collaborative.

Inclusion Criteria: rheumatology practices with at least 50 RA patients for the practice rheumatology practices with at least 2 rheumatologists rheumatology practices utilizing an electronic medical record or typed notes Exclusion Criteria: rheumatology practices already explicitly employing Treat-to-target principles

Doran MF, Pond GR, Crowson CS, O'Fallon WM, Gabriel SE. Trends in incidence and mortality in rheumatoid arthritis in Rochester, Minnesota, over a forty-year period. Arthritis Rheum. 2002 Mar;46(3):625-31. doi: 10.1002/art.509.

Gabriel SE. The epidemiology of rheumatoid arthritis. Rheum Dis Clin North Am. 2001 May;27(2):269-81. doi: 10.1016/s0889-857x(05)70201-5.

Scott DL, Wolfe F, Huizinga TW. Rheumatoid arthritis. Lancet. 2010 Sep 25;376(9746):1094-108. doi: 10.1016/S0140-6736(10)60826-4.

Singh JA, Furst DE, Bharat A, Curtis JR, Kavanaugh AF, Kremer JM, Moreland LW, O'Dell J, Winthrop KL, Beukelman T, Bridges SL Jr, Chatham WW, Paulus HE, Suarez-Almazor M, Bombardier C, Dougados M, Khanna D, King CM, Leong AL, Matteson EL, Schousboe JT, Moynihan E, Kolba KS, Jain A, Volkmann ER, Agrawal H, Bae S, Mudano AS, Patkar NM, Saag KG. 2012 update of the 2008 American College of Rheumatology recommendations for the use of disease-modifying antirheumatic drugs and biologic agents in the treatment of rheumatoid arthritis. Arthritis Care Res (Hoboken). 2012 May;64(5):625-39. doi: 10.1002/acr.21641. No abstract available.

Prince FH, Bykerk VP, Shadick NA, Lu B, Cui J, Frits M, Iannaccone CK, Weinblatt ME, Solomon DH. Sustained rheumatoid arthritis remission is uncommon in clinical practice. Arthritis Res Ther. 2012 Mar 19;14(2):R68. doi: 10.1186/ar3785.

Smolen JS, Aletaha D, Bijlsma JW, Breedveld FC, Boumpas D, Burmester G, Combe B, Cutolo M, de Wit M, Dougados M, Emery P, Gibofsky A, Gomez-Reino JJ, Haraoui B, Kalden J, Keystone EC, Kvien TK, McInnes I, Martin-Mola E, Montecucco C, Schoels M, van der Heijde D; T2T Expert Committee. Treating rheumatoid arthritis to target: recommendations of an international task force. Ann Rheum Dis. 2010 Apr;69(4):631-7. doi: 10.1136/ard.2009.123919. Epub 2010 Mar 9. Erratum In: Ann Rheum Dis. 2011 Aug;70(8):1519. Ann Rheum Dis. @011 Jul;70(7):1349. van der Heijde, Desiree [corrected to van der Heijde, Desiree].

Smolen JS. Treat-to-target: rationale and strategies. Clin Exp Rheumatol. 2012 Jul-Aug;30(4 Suppl 73):S2-6. Epub 2012 Oct 18.

Goekoop-Ruiterman YP, de Vries-Bouwstra JK, Kerstens PJ, Nielen MM, Vos K, van Schaardenburg D, Speyer I, Seys PE, Breedveld FC, Allaart CF, Dijkmans BA. DAS-driven therapy versus routine care in patients with recent-onset active rheumatoid arthritis. Ann Rheum Dis. 2010 Jan;69(1):65-9. doi: 10.1136/ard.2008.097683.

Schipper LG, van Hulst LT, Grol R, van Riel PL, Hulscher ME, Fransen J. Meta-analysis of tight control strategies in rheumatoid arthritis: protocolized treatment has additional value with respect to the clinical outcome. Rheumatology (Oxford). 2010 Nov;49(11):2154-64. doi: 10.1093/rheumatology/keq195. Epub 2010 Jul 29.

Schoels M, Knevel R, Aletaha D, Bijlsma JWJ, Breedveld FC, Boumpas DT, Burmester G, Combe B, Cutolo M, Dougados M, Emery P, van der Heijde D, Huizinga TWJ, Kalden J, Keystone EC, Kvien TK, Martin-Mola E, Montecucco C, de Wit M, Smolen JS. Evidence for treating rheumatoid arthritis to target: results of a systematic literature search. Ann Rheum Dis. 2010 Apr;69(4):638-643. doi: 10.1136/ard.2009.123976. Erratum In: Ann Rheum Dis. 2011 Aug;70(8):1519.

Fransen J, Moens HB, Speyer I, van Riel PL. Effectiveness of systematic monitoring of rheumatoid arthritis disease activity in daily practice: a multicentre, cluster randomised controlled trial. Ann Rheum Dis. 2005 Sep;64(9):1294-8. doi: 10.1136/ard.2004.030924. Epub 2005 Apr 13.

Goekoop-Ruiterman YP, de Vries-Bouwstra JK, Allaart CF, van Zeben D, Kerstens PJ, Hazes JM, Zwinderman AH, Peeters AJ, de Jonge-Bok JM, Mallee C, de Beus WM, de Sonnaville PB, Ewals JA, Breedveld FC, Dijkmans BA. Comparison of treatment strategies in early rheumatoid arthritis: a randomized trial. Ann Intern Med. 2007 Mar 20;146(6):406-15. doi: 10.7326/0003-4819-146-6-200703200-00005.

Grigor C, Capell H, Stirling A, McMahon AD, Lock P, Vallance R, Kincaid W, Porter D. Effect of a treatment strategy of tight control for rheumatoid arthritis (the TICORA study): a single-blind randomised controlled trial. Lancet. 2004 Jul 17-23;364(9430):263-9. doi: 10.1016/S0140-6736(04)16676-2.

Mottonen T, Hannonen P, Leirisalo-Repo M, Nissila M, Kautiainen H, Korpela M, Laasonen L, Julkunen H, Luukkainen R, Vuori K, Paimela L, Blafield H, Hakala M, Ilva K, Yli-Kerttula U, Puolakka K, Jarvinen P, Hakola M, Piirainen H, Ahonen J, Palvimaki I, Forsberg S, Koota K, Friman C. Comparison of combination therapy with single-drug therapy in early rheumatoid arthritis: a randomised trial. FIN-RACo trial group. Lancet. 1999 May 8;353(9164):1568-73. doi: 10.1016/s0140-6736(98)08513-4.

Saunders SA, Capell HA, Stirling A, Vallance R, Kincaid W, McMahon AD, Porter DR. Triple therapy in early active rheumatoid arthritis: a randomized, single-blind, controlled trial comparing step-up and parallel treatment strategies. Arthritis Rheum. 2008 May;58(5):1310-7. doi: 10.1002/art.23449.

Verstappen SM, Jacobs JW, van der Veen MJ, Heurkens AH, Schenk Y, ter Borg EJ, Blaauw AA, Bijlsma JW; Utrecht Rheumatoid Arthritis Cohort study group. Intensive treatment with methotrexate in early rheumatoid arthritis: aiming for remission. Computer Assisted Management in Early Rheumatoid Arthritis (CAMERA, an open-label strategy trial). Ann Rheum Dis. 2007 Nov;66(11):1443-9. doi: 10.1136/ard.2007.071092. Epub 2007 May 22.

Lumley T, Diehr P, Emerson S, Chen L. The importance of the normality assumption in large public health data sets. Annu Rev Public Health. 2002;23:151-69. doi: 10.1146/annurev.publhealth.23.100901.140546. Epub 2001 Oct 25.

Mangione-Smith R, Schonlau M, Chan KS, Keesey J, Rosen M, Louis TA, Keeler E. Measuring the effectiveness of a collaborative for quality improvement in pediatric asthma care: does implementing the chronic care model improve processes and outcomes of care? Ambul Pediatr. 2005 Mar-Apr;5(2):75-82. doi: 10.1367/A04-106R.1.

Knox SA, Chondros P. Observed intra-cluster correlation coefficients in a cluster survey sample of patient encounters in general practice in Australia. BMC Med Res Methodol. 2004 Dec 22;4(1):30. doi: 10.1186/1471-2288-4-30.

Donner A, Klar N. Pitfalls of and controversies in cluster randomization trials. Am J Public Health. 2004 Mar;94(3):416-22. doi: 10.2105/ajph.94.3.416.

Solomon DH, Yu Z, Katz JN, Bitton A, Corrigan C, Fraenkel L, Harrold LR, Smolen JS, Losina E, Lu B. Adverse Events and Resource Use Before and After Treat-to-Target in Rheumatoid Arthritis: A Post Hoc Analysis of a Randomized Controlled Trial. Arthritis Care Res (Hoboken). 2019 Sep;71(9):1243-1248. doi: 10.1002/acr.23755. Epub 2019 Jul 19.

Solomon DH, Lu B, Yu Z, Corrigan C, Harrold LR, Smolen JS, Fraenkel L, Katz JN, Losina E. Benefits and Sustainability of a Learning Collaborative for Implementation of Treat-to-Target in Rheumatoid Arthritis: Results of a Cluster-Randomized Controlled Phase II Clinical Trial. Arthritis Care Res (Hoboken). 2018 Oct;70(10):1551-1556. doi: 10.1002/acr.23508.

Solomon DH, Losina E, Lu B, Zak A, Corrigan C, Lee SB, Agosti J, Bitton A, Harrold LR, Pincus T, Radner H, Yu Z, Smolen JS, Fraenkel L, Katz JN. Implementation of Treat-to-Target in Rheumatoid Arthritis Through a Learning Collaborative: Results of a Randomized Controlled Trial. Arthritis Rheumatol. 2017 Jul;69(7):1374-1380. doi: 10.1002/art.40111. Epub 2017 May 31.