Examining the prevalence of rheumatoid arthritis in data from the Clinical Practice Research Datalink

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Lee Hawkins
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Soljak, Department of Primary Care & Public Health Benjamin

1 Examining the prevalence of rheumatoid arthritis in data from the Clinical Practice Research Datalink Julian Gardiner, Michael Soljak, Department of Primary Care & Public Health Benjamin Ellis, Arthritis Research UK Funded by Arthritis Research UK Strategic Grant

Background ARUK MSK Calculator project Phase 2A: refinement of Phase 1 data Phase 2B: data discovery (including rheumatoid arthritis (RA) prevalence model) Phase 2C:

2 Background ARUK MSK Calculator project Phase 2A: refinement of Phase 1 data Phase 2B: data discovery (including rheumatoid arthritis (RA) prevalence model) Phase 2C: external validation Phase 2D: linked wider analyses Phase 2E: apply prevalence models to other UK countries data Phase 2F: value estimation Phase 2G: Support for web developers

3 Aims of RA prevalence model development 1 to identify cases of RA in the CPRD in three different ways: 1. doctor diagnosis NB QOF register 2. develop an algorithm for the EULAR / ACR diagnostic criteria [1] 3. Identify patients taking disease-modifying anti-rheumatic drugs (DMARDs) without another disease indication to estimate the prevalence of RA identified in these three ways to model risk factors for RA in a case- control /non-case analysis using logistic regression [1] Aletaha D, Neogi T, Silman AJ Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Annals of the Rheumatic Diseases. 2010;69(10).

4 Aims of RA prevalence model development 2 to apply the derived odds ratios to risk factor subcategories in general practice and Middle Layer Super Output Area (MLSOA) populations to estimate time from first RA clinical manifestation and from diagnostic algorithm being met to time of diagnosis entry [1] to perform a geospatial comparison of observed/expected prevalence of RA [1] Raza, K. and A. Filer (2014). "The therapeutic window of opportunity in rheumatoid arthritis: does it ever close?" Annals of the Rheumatic Diseases 74(5):

5 Methods: data extraction We extracted data from the CPRD for all patients with one or more Read/medcodes from a list of RA related items. Medcodes were in four categories: doctor diagnoses of RA joint inflammation acute phase reactant (APR) tests C-reactive protein (CRP), Erythrocyte sedimentation rate (ESR) Serology tests Rheumatoid Factor (RF), Anti citrullinated protein antibody (ACPA) For example Effusion of PIP joint of finger Effusion of DIP joint - finger Effusion of hip Effusion of knee Effusion of tibio-fibular joint Effusion of ankle Effusion of subtalar joint Effusion of talonavicular joint Effusion of lesser MTP joint Effusion of IP joint of toe 2695 Synovitis of hip Synovitis of knee etc (130 medcodes in all)

6 Removal of conflicting diagnoses RA cases with a more recent alternative diagnosis will be excluded: Psoriatic arthropathy Ankylosing spondylitis Sacroiliitis NEC Spondylitis NOS Psoriatic arthritis Psoriatic arthropathy NOS FH: Ankylosing spondylitis Inflammatory spondylopathies Psoriasis spondylitica BASDAI - Bath ankylosing spondylitis disease activity index Juvenile ankylosing spondylitis Spinal enthesopathy Arthritis mutilans Other inflammatory spondylopathies Other inflammatory spondylopathies NOS Marie - Strumpell spondylitis Distal interphalangeal psoriatic arthropathy Inflammatory spondylopathies in diseases EC

7 EULAR/ACR RA diagnostic algorithm

8 Algorithm steps (Score 6, diagnose as case of RA) A. Joint involvement 1 large joint Score large joints Score small joints Score small joints Score 3 >10 joints (at least one small joint) Score 5 B. Serology Negative RF and negative ACPA Score 0 Low-positive RF or low-positive ACPA Score 2 High-positive RF or high-positive ACPA Score 3 C. Acute-phase reactants Normal CRP and normal ESR Score 0 Abnormal CRP or abnormal ESR Score 1 D. Duration of symptoms <6 weeks Score 0 6 weeks Score 1 (must occur within 3 months of joint involvement to contribute to the score)

9 Candidate patients The CPRD extract contained over 3,000,000 patients. In order to make the algorithm more tractable we filtered the patients to produce a list of candidate patients. The candidate patients for the RA diagnostic algorithm were patients with 1. Record of joint involvement 2. Test result for either APR of serology test (or both) There were 136,036 such patients.

10 Results: records of large joint involvement Number of records Frequency % 0 99, , , , or more Total 136,

11 Results: records of small joint involvement Number of records Frequency % 0 135, or more Total 136,

12 Results: records of site not specified Number of records Frequency % 0 33, , , , , or more Total 136,

13 Revising the algorithm Problems with joint counting The laterality of the joints is not specified (except possibly in hard to access free text) so for example two records of knee involvement could be one joint or two. The number of joints involved is not specified, so for example a record of finger joint involvement could mean anything from 1 to 8 joints. In a large majority of cases the joint location is not specified. Revised algorithm Algorithm A (strict) Algorithm B (lax) If there is any record of joint involvement, score 1 point If there is any record of joint involvement, score 2 points 1 large joint Score large joints Score small joints Score small joints Score 3 >10 joints (at least one small) Score 5

14 So two versions of algorithm used Algorithm Score from A (strict): Algorithm score minimum A (strict) of 1 points for Algorithm joint involvement B (lax) Algorithm joint section B (lax): score minimum of 2 points for joint involvement of algorithm Frequency % Frequency % 0 1, , , ,

15 Total algorithm scores for strict and lax joint involvement Total score from algorithm Algorithm A (strict) Algorithm B (lax) Frequency % Frequency % , , , , , , , , ,

16 Total possible? RA cases after conflicting disease exclusions Cases of RA Before exclusion of patients with alternative diagnosis After exclusions of patients with alternative diagnosis Doctor diagnosed cases 89,675 88,299 Additional algorithm diagnosed cases Algorithm A (strict) Algorithm B (lax) ,321 12,928

17 Prevalence of RA in CPRD data: Year(s) Prevalence of doctor diagnosed RA (per million) Additional prevalence of algorithm diagnosed RA (per million) Total prevalence of RA (per million) Additional algorithm cases as % of doctor diagnosed cases

18 Comparison with previous RA prevalence estimates Men Women CPRD1 doctor diagnosed cases % % CPRD2 doctor diagnosed + lax algorithm % % Norfolk study [1] 0.44 % 1.16 % [1] Symmons, D., et al. (2002). "The prevalence of rheumatoid arthritis in the United Kingdom: new estimates for a new century." Rheumatology (Oxford) 41(7):

19 Prevalence patterns by age group and time (males) 1 Year Prevalence of doctor diagnosed RA (cases per million people)

20 Prevalence patterns by age group and time (males) 2 Year Prevalence of additional algorithm diagnosed RA (cases per million people)

21 Prevalence patterns by age group and time (males) 3 Year Percentage change in the doctor diagnosed prevalence from the addition of algorithm diagnosed cases

22 Discussion Are the algorithm cases really cases of RA? Scoring 2 points for any joint involvement, our algorithm B (lax) cases have: (i) Some joint involvement + low positive RF test + positive APR test within 3 months of joint involvement + symptom duration > 6 weeks OR (ii) Some joint involvment + high positive RF test + EITHER pos. APR test within 3 months of joint involvement OR symptom duration > 6 weeks We may do better to think of these as high risk individuals or pre-cases. The decline in the rate of additional algorithm cases with increasing age (PREVIOUS SLIDE) is consistent with algorithm cases being high risk individuals (or pre-cases ) many of whom then go on to develop doctor diagnosed RA.

23 Next steps: DMARDs and regression modelling Identifying a third group of potential cases those taking DMARD drugs Identification of a further group of (potential) RA cases in addition to doctor diagnosed and algorithm group those taking DMARD drugs who have no other diagnosis to explain the prescription. Regression modelling We will explore the risk factors for RA in case / control logistic regression models, considering the three groups of cases / potential cases identified: Doctor diagnosed RA cases Algorithm diagnosed RA cases Patients taking DMARDs with no other known cause

24 Next steps: regression modelling / known risk factors Known Risk Factors for RA Sex Age group Smoking Alcohol intake BMI Ethnicity Occupation Parity Deprivation Education

25 Conclusion Working with CPRD data has the advantage of an extremely large sample size. The potential lack of uniformity in the data presents some challenges when interpreting results. It is important to make use of validation and sensitivity analysis where possible. Abbreviations ACPA Anti Citrullinated Protein Antibody (test) ACR American College of Rheumatology APR Acute Phase Reactants ARUK Arthritis Research UK CPRD Clinical Practice Research Datalink CRP C-Reactive Protein (test) ESR Erythrocyte Sedimentation Rate (test) EULAR European League Against Rheumatism MSK Musculoskeletal QOF Quality and Outcomes Framework RA RF Rheumatoid Arthritis Rheumatoid Factor (test) References Aletaha D, Neogi T, Silman AJ Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Annals of the Rheumatic Diseases. 2010;69(10). Raza, K. and A. Filer (2014). "The therapeutic window of opportunity in rheumatoid arthritis: does it ever close?" Annals of the Rheumatic Diseases 74(5): Symmons, D., et al. (2002). "The prevalence of rheumatoid arthritis in the United Kingdom: new estimates for a new century." Rheumatology (Oxford) 41(7):

Clinical Policy: Etanercept (Enbrel) Reference Number: PA.CP.PHAR.250 Effective Date: 01/18 Last Review Date: 08/17 Line of Business: Medicaid

Clinical Policy: Etanercept (Enbrel) Reference Number: PA.CP.PHAR.250 Effective Date: 01/18 Last Review Date: 08/17 Line of Business: Medicaid Clinical Policy: (Enbrel) Reference Number: PA.CP.PHAR.250 Effective Date: 01/18 Last Review Date: 08/17 Line of Business: Medicaid Coding Implications Revision Log Description (Enbrel ) is tumor necrosis