Faculty Bibliography

One of the major developments in the treatment of rheumatoid arthritis over the last decade and a half has been the realization that early and aggressive treatment leads to better outcomes for most patients. Early use of methotrexate and switching to a combination treatment regimen within the first 3-6 months if there is inadequate response to methotrexate is the currently accepted paradigm for rheumatoid arthritis treatment. To achieve better outcomes it is not enough to just use combination treatments; disease activity also needs to be measured and monitored with a 'treat-to-target' approach, where remission or low disease activity is the target and available medications are used either alone or in combination to get there. 2013 Future Medicine Ltd

Background Randomized controlled trials (RCT) are designed to answer specific questions using a certain number of patients, determined by sample size calculation. If the calculation is not done properly, more than the needed number of patients may be enrolled, leading to unnecessary exposure for those patients to potentially harmful drugs. In an ideal world, assumptions that go into the calculation of the sample size would be more certain. However in the real world it is not always possible to have ideal calculations and it would be expected that under and over enrolling would be seen in roguishly similar number of RCTs. Objectives To determine the actual numbers of patients needed to be enrolled in RA biologic RCT. Methods A Pubmed search was conducted, for RCT of abatacept, adalimumab, etanercept, infliximab, rituximab and tocilizumab, in rheumatoid arthritis (RA) patients (n=291). Only original initial studies where the primary outcome was efficacy were analyzed (n=42). Using the final results of the primary outcome, back calculation of the actually needed patients for the trials were calculated and compared to the actual enrollment numbers. Results 42 studies were analyzed (infliximab 10, etanercept 7, adalimumab 8, abatacept 7, tocilizumab 5, rituximab 5). Primer efficacy outcome was ACR 20 in 29 studies. ACR 50 in 3 studies, ACR N in 2 studies, DAS-28 in 5 studies, Paulus 20 in 2 studies and reduction of number of swollen/tender joints in 1 study. The mean number of patients enrolled in the treatment arms were 153 and the control arms were 114. After back calculation, the actual needed numbers for the treatment arm was 79 and control arm was 79. According to the recalculated sample size results, there were more patient than required to show differences between groups in 35 studies (83%) and less patients than required in 7 studies. In the 35 studies were more than necessary patients were enrolled had a median of 103 extra patients. Conclusions Over 80% of RCT of biologic agents in the tre!

Background CDAI (clinical disease activity index) and RAPID3 (routine assessment of patient index data 3) cut points defining responses that best match ACR20/50/70 response in rheumatoid arthritis (RA) patients (pts), are unknown. Objectives To evaluate cut points in a study population treated with certolizumab pegol (CZP) plus methotrexate (MTX).1 Methods ACR responders through Week (Wk) 12 from 393 CZP treated pts (400 mg at Wks 0, 2 and 4 then 200 mg every 2 Wks) plus MTX in RAPID 1 (NCT00152386) were categorized by proposed response (R) cut points in CDAI (change from baseline [CFB] >6.7, >10.0, >13.9) and RAPID3 (CFB >1.8, >3.6). ACR20/50/70 responses were compared with proposed categorizations using cross-tabulations, AUC under the ROC curve (AUC-ROC), % correctly classified (%CC) pts and kappa statistics. CART2 (classification and regression trees) modeling identified CDAI and RAPID3 cut points defining responses most closely associated with ACR20/50/70 responses. Results Sensitivities were very high; at Wk 12, 93-100% ACR20/50/70 responders achieved CFB in CDAI (>6.7,>10.0,>13.9). Positive predictive value [PPV] was lower indicating CDAI-R is not as strongly associated with ACR-R as ACR-R is with CDAI-R; lower specificities indicate that ACR nonresponse (NR) does not correspond well to CDAI-NR (particularly for ACR50/70). However, CDAI-NR predicts well ACR-NR (very high negative predictive value [NPV]) (Table). CFB in CDAI>13.9 was most closely associated with ACR20-R (=0.57). Association between proposed CDAI categorizations and ACR50/70 was weak (=0.05-0.29). A similar trend was observed for RAPID3. CART modeling identified CFB in CDAI >13.80/>20.15/>20.15 and CFB in RAPID3 >5.46/>7.28/>5.53 as categorizations most closely associated with ACR20/50/70 responses. Conclusions CDAI and RAPID3 CFB thresholds were identified that defined the closest associations with ACR responses in pts with inadequate response to MTX and high disease activity at baseline (mean DAS28 6.9). Sensitivities/NP!

Background A patient self-report joint count, rheumatoid arthritis disease activity index (RADAI),1 is correlated significantly with tender and swollen joint counts performed by a health professional in rheumatoid arthritis (RA) patients2. The RADAI is included on the multidimensional health assessment questionnaire (MDHAQ), which is completed in many rheumatology settings by all patients with all diagnoses, as the same questionnaire is (logistically) most feasible. Objectives To analyze RADAI painful joint count scores in patients with diagnoses other than RA in usual care setting. Methods Each patient seen at an academic rheumatology site completes an MDHAQ at each visit, while waiting to see the doctor in the infrastructure of clinical care. The RADAI on the MDHAQ includes 8 bilateral specific joint groups: fingers, wrist, elbow, shoulder, hip, knee, ankle and toes, each scored from 0 ("no pain") to 3 ("severe pain") (total 0-48). A random visit of 465 patients was analyzed including 75 with SLE, 50 with gout, 53 with PsA, 113 with OA, as well as 174 with RA. Mean RADAI and % of patients scoring each specific joint group as affected were computed for each diagnosis, and compared to the MDHAQ patient global estimate (PATGL) and physician global estimate (DOCGL) using Spearman correlations. Results Patients were primarily women (68%), age 51+16 years;SLE patients were youngest (39.5+13.8) and OA patients oldest (62.8+12.5). An abnormal RADAI score >0 was reported by 99% of patients with OA, 87% of patients with RA, 83% with PsA, 60% with gout, and 59% with SLE. The joints reported as most affected were knees (58%) and fingers (52%) in all patients; in RA, fingers (52%), wrists (58%), and knees (58%); in SLE, fingers (37%) and shoulders (32%); in gout, toes (24%) and knees (22%); in PsA, knees (43%) and fingers (40%), and in OA, knees (68%) and fingers (42%) (Table). RADAI scores were correlated significantly with PATGL (rho =0.50-0.75, p<0.001), and moderately, though significantly, with DOCGL!