Measurement Considerations In Rheumatology: Integrating Biomarkers, Technology, Safety, and Comorbidities to Assess Risks and Benefits of Treatment Jeffrey.

Measurement Considerations In Rheumatology: Integrating Biomarkers, Technology, Safety, and Comorbidities to Assess Risks and Benefits of Treatment Jeffrey Curtis, MD MS MPH University of Alabama at Birmingham Director, Arthritis Clinical Intervention Program (ACIP) Co-Director, UAB Center for Education and Research on Therapeutics (CERTS) of Musculoskeletal Diseases

Acknowledgements & Disclosures
Funding AHRQ R01-HS018517 AHRQ U18-HS NIH AR053351 Doris Duke Charitable Foundation Research / Consulting Centocor, Amgen, Abbott, UCB, CORRONA, Crescendo, BMS, Roche/Genentech, Pfizer

Overview More on Measurement
Biomarker-Based Assessment of RA Disease Activity Technology-based approaches Safety & Relationship with Comorbidities Infections GI Perforations CV Events Putting It All Together

Which Biomarkers Might be Important in RA?
Interleukins Receptors Hormones Skeletal Others IL1A AGER Follicle stimulating hormone Aggrecan Adiponectin IL1B* EGFR Gastric inhibitory polypeptide C2C Adrenomedullin IL1RA * IL2RA ghrelin CS846-epitope Amyloid P component, serum IL2 IL4R GLP-1 COMP Bone morphogenetic protein 6 IL3 IL6R* Growth hormone 1 ICTP* c5a IL4 IL-1 receptor, type I insulin Keratan sulphate c5b-9 IL5 IL-1 receptor, type II Leptin* Osteocalcin CALCB IL6* KIT NT-proBNP Osteonectin Calprotectin* IL7 sFLT4 Pancreatic polypeptide Osteopontin CD40 ligand IL8* sKDR POMC PIIANP CRP* IL9 TNFRI* Prolactin PYD* Cystatin C IL10 PTHrP DKK IL12 PYY Fibrinogen IL12B Resistin * FLT3 ligand IL13 Growth Factors TNF Superfamily TNFR Superfamily Other Cytokines Glial cell derived neurotrophic factor IL15 FGF2 APRIL CD30 EPO gp130 IL17 EGF* BAFF* FAS GCSF Haptoglobin IL18* HGF LIGHT Osteoprotegerin GMCSF HSP90AA1 IL23 NGF LTA TNFRSF1A IFNA1 IGFBP1 PDGF-AA RANKL TNFRSF1B IFNA2 Neurotrophin 4 PDGF-AB TNF-alpha TNFRSF9 IFNG Pentraxin 3 PlGF TNFSF18 LIF S100A12 TGFA TWEAK MCSF SAA1* VEGFA* CCL22* sclerostin Selectins Adhesion Molecules Enzymes Apolipoproteins Matrix Metalloproteinases SERPINE1 Selectin E ICAM1* Alkaline phosphatase APOA1* MMP1* sFLT1 Selectin L ICAM3 Lysozyme APOA2 MMP10 SLPI Selectin P VCAM1* Myeloperoxidase APOB MMP2 Thrombomodulin Thyroid peroxidase APOC2* MMP3* YKL40* APOC3 MMP9 APOE This list represents the 137 biomarkers which entered feasibility; the final 25 markers selected for development are indicated with a star. The 137 biomarkers and the subsequent 25 selected represent a diversity of marker classes that contribute to RA pathophysiology. The 25 prioritized biomarkers represented cytokines/receptors, adhesion molecules, growth factors (GFs), matrix metalloproteinases (MMPs), hormones, acute phase proteins, and apoliproteins. *Indicates biomarkers selected for development; 25 total were selected Bakker et al. Presented at ACR 2010; Poster #1753. Curtis et. al. Manuscript under review.

Vectra™ DA: Development Studies
FEASIBILITY DEVELOPMENT VALIDATION >300 patients >300 samples Select biomarkers Build prototypes > 500 patients > 700 samples Finalize algorithm ~800 patients > 800 samples SCREENING Biomarker Screening Feasibility I Feasibility II Feasibility III Feasibility IV Assay Optimization Training Verification Validation Identify candidate biomarkers Qualify assays Select top candidates Build prototypes Prepare for development Optimize analytical performance of individual assays Develop algorithm Refine algorithm and validate analytically Evaluate in independent cohort 2 final stages of Development, algorithm training (development) using large multi-center studies, and then algorithm verification to test the algorithm in an independent patient set prior to validation. 396 Candidate Biomarkers 137 Candidate Biomarkers 25 Candidate Biomarkers 12 Final Biomarkers Validated Vectra DA Adapted from: Bakker et al. Presented at: ACR 2010; Poster #1753. Curtis et. al. Manuscript under review.

Cohorts Used in Vectra™ DA Development
BRASS (n=637) Oklahoma (n=288) InFoRM (n=685) Leiden EAC (n=77) CAMERA (n=74) Description Brigham and Women’s RA Sequential Study (Massachusetts) Oklahoma City Community Cohort (Oklahoma) Index For RA Measurement - Crescendo Bioscience study (N Amer) Leiden Early Arthritis Cohort (Netherlands) Computer Assisted Management in Early RA (Netherlands) Type Observational Inception Cohort Randomized Open Label (Tight control) Inclusion criteria Patients with RA > 18 yrs Patients age with RA Patients with early arthritis (all arthritis; <2yrs) Patients age >16 with early RA (<1 yr) Patients >1100 >800 >1300 >1800 all arthritis 299 Sample and clinical exam schedule Annual clinical exam and samples One clinical exam and sample per patient 3 visits/patient, ~3 months apart, with clinical exam and samples Baseline and 3 months then yearly sample and clinical exam Clinical exam and sample at every visit: Conventional group every 3 months, intensive group every 4 wks Therapies DMARDs, biologics DMARDS, analgesics MTX +/- cyclosporine Timeline ongoing 2007-ongoing 1993-ongoing This table includes details about the cohorts used in Vectra DA development. These are details of the original cohorts; the patients and samples used in the Vectra DA studies may have different characteristics due to selection details and exclusion criteria. InFoRM Fleischmann et al. Presented at EULAR Poster #SAT0518. BRASS Iannaccone et al. Rheumatology (Oxford) Sep 16. [Epub ahead of print] Leiden van Aken et al. Clin Exp Rheumatol. 2003;21(5 suppl 31):S100-S105. van der Linden et al. Arthritis Rheum. 2010;62:3537–46. CAMERA Verstappen et al. Ann Rheum Dis. 2007:

RA: A Disease with a Diverse Biology
The 12 biomarkers represent multiple biological pathways known to be important in RA. This model describes the interaction of the biomarkers with different cell types, in different tissue compartments.

Vectra™ DA Algorithm Includes 12 biomarkers and uses a formula similar to DAS28CRP Different subsets and/or weightings of biomarkers are used to estimate SJC28, TJC28, and PG DAS28CRP=0.56√TJC √SJC PG log(CRP+1) TJC=tender joint count; SJC=swollen joint count; PG =patient global health Vectra DA Score =(0.56√PTJC √PSJC PPG log(CRP+1) ) * PT JC=predicted TJC, PSJC=predicted SJC, PPG =predicted PG CRP IL-6 SAA YKL-40 EGF TNF-RI Leptin VEGF-A VCAM-1 MMP-1 MMP-3 Resistin TJC28 SJC28 Patient Global Biomarkers Used To Estimate Each DAS Component The algorithm with the best performance used 12 biomarkers in a formula similar to DAS28CRP, resulting in a score of between 1 and 100. Different subsets and/or weights are used to estimate the tender joint count, swollen joint count, and patient global. The biomarkers used for each component are noted in the Venn Diagram. In the case of the tender and swollen joint counts the biomarkers used to estimate them are the same, but the weights used to estimate them are different. The final 12 markers selected retained the biological breadth of the original biomarker set. Bakker et al. Presented at: ACR 2010; Poster #1753. Curtis et. al. Manuscript under review.

Vectra™ DA Validation and Performance
The Vectra DA score was significantly associated with disease activity categories compared to the gold standard of the DAS28CRP* (p<0.001) RF+ and/or Anti-CCP+ AUROC = 0.77* RF- and Anti-CCP- AUROC = 0.70* True Positives False Positives True Positives False Positives The primary analysis validated that the Vectra DA score is significantly associated with DAS28CRP in 230 RF+ and/or anti-CCP+ patients and 141 RF- and anti-CCP- patients. Both cohorts were selected to have a wide and relatively uniform distribution of disease activity. For RF+ and/or anti-CCP+, the AUROC for the Vectra DA score relative to the DAS28CRP threshold of 2.67 was 0.77 with a Pearson correlation of The 230 patients were selected to have a wide and relatively uniform distribution of disease activity. For RF- and anti-CCP-, the AUROC for the Vectra DA score relative to the DAS28CRP threshold of 2.67 was 0.70 with a Pearson correlation of The 141 patients were selected to have disease activity distribution similar to the cohorts from which they were selected. Please note: we cannot directly compare performance of Vectra DA in patients positive for RF and/or anti-CCP vs patients negative for both since the study populations were not matched. *low versus moderate/high disease activity using DAS28CRP = 2.67 as the threshold Curtis et al. Presented at ACR 2010; Poster #1782

Vectra™ DA algorithm score tracks disease activity over time
Studies demonstrate that change in Vectra DA algorithm score is significantly correlated with change in DAS28 (p<0.001) In the BeSt Study: Vectra DA algorithm score significantly correlated with change in DAS28 (0.54, p < ) An independent study of patients in the InFoRM study analyzed the ability of the score generated from the Vectra DA algorithm to track disease activity A total of 85 patients with 3 visits approximately 3 months apart were evaluated. Due to the variability in the DAS28CRP, only visits with a change greater than the smallest detectable difference in the DAS28CRP, defined as >1.3 change (Uhlig) were evaluated. There were 68 significant changes in DAS28CRP between visits (>1.3 DAS units): The Vectra DA Score tracked changes in DAS28CRP with 74% agreement in the direction of the change, p<0.001 The change was concordant in 50 patients and discordant in 18 patients Reference: Uhlig et al. Test-retest reliability of disease activity core set measures and indices in rheumatoid arthritis measures. Ann Rheum Dis Jun;68(6):972-5. BeSt Study 54 RA patients and 108 visits from the BeSt clinical trial were studied to evaluate whether the Vectra DA algorithm score could track change in disease activity over 1 year. association between the change in Vectra DA algorithm score and change in DAS28 over 1 year was evaluated by Pearson’s correlation Reference: Hirata S, Dirven L, Shen Y, Centola M, Cavet G, Lems WF, Tanaka Y, Huizinga TW, Allaart CF. A Multi-Biomarker Based Disease Activity (MBDA) Score System Compared to a Conventional Disease Activity Score (DAS) System in the BeSt Rheumatoid Arthritis (RA) Study. Ann Rheum Dis 2011;70(Suppl3):593. . Hirata S,et al. Ann Rheum Dis 2011;70(Suppl3):593;

Vectra™ DA algorithm score discriminates low disease activity from remission
Vectra DA algorithm score was significantly associated with remission by ACR/EULAR Boolean criteria (by AUROC, p<0.001) Similar AUROCs were seen for CDAI, SDAI, DAS28CRP and DAS28ESR remission (p≤0.001) ROC curve for Vectra DA algorithm score classification of Boolean-defined remission vs. non-remission. Poster does not show p values for AUROCs for other criteria for remission, but Slide text is consistent with Poster text. Data on file, Crescendo Bioscience. 70 RA patients with disease duration <10 years and DAS28ESR≤3.2 A Vectra DA algorithm score of 25 was set as the exploratory threshold for remission Using Vectra DA algorithm score ≤25 to define remission, 38/70 (54%) patients were in remission, comparable to the remission rates obtained with SDAI (56%) and CDAI (53%) criteria. Ma MH, et al. EULAR Annual Meeting 2011; Presentation SAT0047;

Vectra DA Algorithm Score
Vectra™ DA algorithm score was not affected by common comorbidities in a study of 512 patients Ratio of Disease Activity Measure’s Median Value Between RA Patients With and Without Common† Comorbidities Subgroup n (%) CRP CDAI DAS28CRP Vectra DA Algorithm Score Hypertension 223 (44) 0.98 1.32* 1.14* 1.05 Osteoarthritis 172 (34) 0.88 1.17 1.13 Osteoporotic bone fractures 131 (26) 0.91 1.02 Degenerative joint disease 113 (22) 1.20 1.18 1.11* 1.07 Diabetes 73 (14) 1.01 1.09 1.04 1.07* Current smoker 67 (13) 1.46 1.45* 1.17* Asthma 50 (10) 1.28 1.11 512 RA subjects from the InFoRM Study, a geographically diverse observational North American cohort Concentrations of 12 biomarkers were used to generate a multi-biomarker disease activity (Vectra™ DA algorithm) score Smoking status and comorbidities present in ≥10% of patients were evaluated Ratios of median values of CRP, CDAI, DAS28CRP, and the Vectra DA algorithm score were calculated in subjects who did and did not have the condition Multiple regression was used to assess the strength of age- and gender-adjusted associations between comorbidities and measures of disease activity. The effect of multiple testing was controlled using the false discovery rate (FDR) method of Benjamini and Hochberg (An FDR<0.05 was considered significant) An exploratory analysis was conducted for fibromyalgia even though it was present in only 6% of patients † Present in ≥10% of the study population * Nominal p < 0.05; adjusted for age and gender. When adjusted for multiple comparisons, none were statistically significant Shadick NA, et al. EULAR Annual Meeting 2011; Presentation FRI0305

Exploratory Analysis: Fibromyalgia had smaller observed effects on the Vectra™ DA algorithm score than on other disease activity measures Measures of Disease Activity in RA Patients With and Without Fibromyalgia FM (n=33) Non-FM (n=475) Ratio INDICES Median Vectra DA algorithm Score 47 42 1.1 Median DAS28CRP 4.3 3.3 1.3 Median CDAI 18 11 1.6 COMPONENTS Mean swollen joint count 4.7 Mean tender joint count 9.1 5.2 1.8 Mean patient global 50 33 1.5 Median CRP (mg/L) 7.0 4.2 1.7 512 RA subjects from the InFoRM Study, a geographically diverse observational North American cohort Concentrations of 12 biomarkers were used to generate a multi-biomarker disease activity (Vectra™ DA algorithm) score Smoking status and comorbidities present in ≥10% of patients were evaluated Ratios of median values of CRP, CDAI, DAS28CRP, and the Vectra DA algorithm score were calculated in subjects who did and did not have the condition Multiple regression was used to assess the strength of age- and gender-adjusted associations between comorbidities and measures of disease activity. The effect of multiple testing was controlled using the false discovery rate (FDR) method of Benjamini and Hochberg (An FDR<0.05 was considered significant) An exploratory analysis was conducted for fibromyalgia even though it was present in only 6% of patients The slight elevation of the Vectra DA algorithm score was of similar magnitude to the elevation in the swollen joint count Shadick NA, et al. EULAR Annual Meeting 2011; Presentation FRI0305

Vectra™ DA significantly associated with radiographic progression in the BeSt study
In the BeSt study, the Vectra DA algorithm score had greater observed correlations with 12 month change in total Sharp-van der Heijde score (DTSS) than measures available in routine clinical practice* (n=89) Relative performance of variables measured at Year 1 that predict TSS change from Year 1 to Year 2 Spearman Correlation Note: Vectra DA was not developed or optimized to predict radiographic progression and is not intended for use in the assessment of structural joint damage. * SHS is not available in routine clinical practice The Vectra DA algorithm predicted radiographic outcomes better at year 1 than at BL, perhaps because BL samples did not reflect the effects of antirheumatic therapy. Change in SHS was defined as any progression of change > 0 Allaart CF, et al. EULAR Annual Meeting 2011; Presentation THU0319

Vectra™ DA algorithm score associated with radiographic progression in Leiden EAC
Correlation to Radiographic Progression Over 12 months Spearman correlation n = 271 visits EAC = Early Arthritis Cohort van der Helm-van-Mil AH et al. As presented at EULAR FRI0013

High Vectra™ DA algorithm score in DAS28CRP remission indicates increased joint damage risk
RR=1.5* *p<0.05 RR=2.3* RR=3.1* Patients in DAS28CRP remission had a significantly higher risk of progression if they also had a high Vectra DA algorithm score EAC = Early Arthritis Cohort; TSS = total van der Heijde sharp score; DAS CRP remission=(< 2,32); High Vectra DA algorithm score= (> 44) Van der Helm-van Mil, ACR Annual Meeting 2011 Presentation SUN323

Significant change in the mean Vectra™ DA algorithm score occurred as early as 2 weeks after initiation of therapy Change in Vectra DA algorithm score (in both responders and non responders) Bold Line indicates Median and Boxes Indicate the IQR Δ BL to: n Mean Δ (95% CI) p value Wk 2 43 -8.0 (-12 to -4.1) <0.001 Wk 6 -7.9 (-11 to -4.6) Wk 12 29 -8.4 (-13 to -3.7) 0.001 Adult RA patients at Brigham Arthritis Center ≥6 tender joints and ≥6 swollen joints Stable treatment regimen during the previous month Lack of prior exposure to anti-TNF agents Received MTX or anti-TNF (± MTX) therapy in the course of their routine care Clinical assessments conducted at baseline (BL) and week 6, with a follow-up assessment at week 12 for subjects who did not attain an ACR20 response at week 6 The concentrations of 12 biomarkers were used to generate a multi-biomarker disease activity (Vectra™ DA algorithm) score The majority of the decrease in the Vectra DA Algorithm Score occurred during the first 2 weeks Weinblatt M, et al. EULAR Annual Meeting 2011; Presentation THU BL, baseline

Change in Vectra™ DA Score significantly discriminates between ACR50 responders vs. non-responders; Change in CRP does not The change in Vectra DA algorithm score at the last study visit was significantly associated with ACR50 (AUROC=0.69, p=0.03) The %change in CRP was not significantly associated with ACR50 (AUROC=0.60, p=0.30) Weinblatt M, et al. EULAR Annual Meeting 2011; Presentation THU0339

Potential Uses of Measuring Biomarkers in RA
Assist in clinical management when more information is needed Allow for more rapid switching of therapies in Phase 2/3 studies & clinical practice Impact patient-physician communication Predict Successful therapy withdrawal Flare Radiographic progression Proxy for synovitis on MSK US & MRI

Biomarker-Based Assessment of RA Disease Activity Technology-based approaches Safety Infections GI Perforations CV Events Putting It All Together

Electronically Collected PROs: One Example at UAB
Also and optionally collects MDHAQ, RAPID3, Patient Acceptable Symptom State (PASS), EQ5D, SF-12, SF-6D, RADAI, patient preferences…

Physician Collected Data

Final Scoring Page

Longitudinal Trends In Disease Activity
RAPID3 Score

Predicting Response with Clinical Data Collected Early
Curtis JR. Ann Rheum Disease 2011; epub ahead of print

ACR Rationale for Evidence-Based Clinical Practice Recommendations
Summarize efficacy and safety considerations for busy clinicians Are advisory, NOT proscriptive May help to improve health care Quality Appropriateness Cost effectiveness Should be based on evidence and created using a formal process (e.g. RAND method) As Dr shekelle has so clearly discussed, there are many rationale for Evidence based clinical practice recommendations. They serve as valuable education tools by summarizing efficacious interventions for busy clinicians. They are advisory, but NOT prescriptive. Ultimately, it is hoped that clinical practice recs improve health care quality, appropriateness, & cost-effectiveness. Ideally, they should be based on evidence and be created using a formal process (e.g. RAND method).

ACR RA Recommendations 2011 Update
Revision of the ACR RA Recommendations published in 2008* using the same methodology Added new drugs: TCZ, GOL, CZP Advisory, not proscriptive “Positive” recommendations only No statements on when not to start a therapy, based on efficacy considerations No statements on when it is “okay” to use an agent based on safety considerations Adding and switching therapies now included Focus on common patients, NOT exceptional cases ; cost not considered but alternate choices are * Saag K et al. Arthritis Rheum. 2008;59:762

Task Force Panel (TFP) C Bombardier MD MSc Maxime Dougados MD
AF Kavanaugh MD D Khanna MD MSc JM Kremer MD Charles King MD Karen Kolba MD Amye Leong MBA Eric Matteson MD MPH Eileen Moynihan MD JT Schousboe MD MS The TF panel, represented by well known rheumatologists, pharmacists, epidemiologist, economist and patient advocates and from the US and Canada, was selected based on their acknowledged leadership in thedr disciplines.Despite some intended variation in their background, all have a passion for improving care for RA patients. Their task was to create the recommendations thorugh the formal group process that you have just heard about.

Core Expert Panel (CEP)
K Saag MD MSc J Singh MD MPH D Furst MD J Curtis MD MPH H Paulus MD Tim Beukelman MD Maria Almazor MD MPH S Louis Bridges MD PhD W Winn Chatham MD L Moreland MD Ted Mikuls MD MSPH James O’dell MD There were 6 core experts from UAB and 2 from RAND/UCLA including Dr Shekelle, 2 were from U Nebraska and so on....these were the people whom we often called upon to provide expert advice.

ACR RA RECOMMENDATION DEVELOPMENT PROCESS Working group and CEP
Systematic Literature Review TFP Develop Evidence Report Create Clinical Scenarios Review report Rate clinical scenarios Analyze 1st ratings RAND-UCLA Method Iterative Group Meetings and Internet Teleconferences Review rating, discuss evidence, second rating Next is the RAND-UCLA appropriateness method, which is a iterative process of group meetings, ratings and discussion of evidence Analyze 2nd ratings Draft recommendations Grade recommendations Saag K et al. Arthritis Rheum. 2008;59:762 Finalize recommendations Submit to ACR

Systematic Literature Review- Article Selection: Update 2010
BIOLOGICS Newer-BIOLOGICS NON-BIOLOGICS 1765 Started 69 Started 663 Started 253 articles after titles and abstracts review 27 articles after titles and abstracts review 160 articles after titles and abstracts review Data from 149 articles included and abstracted after full text review

Sample Evidence Table: Update 2011

With features of poor prognosis Moderate Disease Activity
Clinical Scenario Example- When To Use a Biologic DMARD failure definition With features of poor prognosis Low Disease Activity Moderate Disease Activity High Disease Activity 1 Optimal MTX/SSZ/HCQ a months 2 3 4 5 6 7 8 9 b months Optimal MTX alone Optimal MTX and another DMARD in combination This is a truncated worksheet of scenarios regarding indication for use of a biologic in a patient with establish RA . In this case, we varied the features of poor prognosis with low, mod and high disease activity. As the definition of DMARD failure, we varied the prior thearpy as MTX/SSZ/HCQ vs MTX alone, vs MTX plus another DMARD, we also varied the duration of these priore therapyies as either 3 or 6 months. The likert scale shwon in gray respresents appropriateness, with 1 being fully inappropriate and 9 being most appropriate. The TFP reviewed the evidence report and then rated these scenarios using the RAND Appropriateness methods. After collecting individual ratings, the dispersion and median of theses ratings can be visualised. The green highlights represents disagreement. While the Red highlight represents the median score if there is no disagreement. Disagreements or median ratings in the range of 4-6 were discussed in even more detail than other scenarios in an effort to resolve discordant ratings. Median rates in the range of 7-9 were translated into recommendation statements RED = Median score with TFP agreement; GREEN = NO median score due to disagreement

Interpretation of Group Median Values
Median score 7 or greater: “Appropriate” Median score 3 or less: “Inappropriate” Median score 3.5 to 6.5: “Uncertain” 1 2 3 4 5 6 7 8 9 “Inappropriate” “Uncertain” “Appropriate”

Key Assumptions for 2011 ACR RA Clinical Scenarios
Focus on common patients, NOT exceptional cases Cost not considered Alternate therapeutic choices taken into account When a particular drug not recommended; does NOT mean it’s contraindication Scenarios for which agreement not achieved noted explicitly Optimal dose of medication given for 3 mos before therapy escalation or switching Disease activity and prognosis assessments mostly between 3-6 mos, although can be reassessed as early as 3 mos Assume a clinical indication (based upon disease activity) for use of each therapeutic choice Contraindications related to safety present at the time of making treatment decision and NOT based solely on history of condition

Examples of Features of Poor RA Prognosis
Functional limitation Defined using standard scales (e.g., health assessment questionnaire-DI) Extra-articular disease Includes presence of rheumatoid nodules, Felty’s syndrome, RA lung disease, etc. Positive Rheumatoid Factor (RF) Positive anti-cyclic citrullinated peptide (CCP) Bony erosions by radiograph

Overview Methods for ACR 2011 Guideline Development
Highlights from 2011 ACR Guidelines Update Safety Infections GI Perforations CV Events Biomarker-Based Assessment of RA Disease Activity

Methods: Defining Disease Duration and Markers or Poor RA Prognosis
Disease Duration Intervals <6 months (early disease) 6–24 months (intermediate disease) >24 months (longer disease duration) Clinical Markers of Poor RA Prognosis Extra-articular disease (eg, vasculitis, Sjögren’s syndrome, RA lung disease, etc) Bony erosions by radiography Functional limitation (eg, HAQ Disability Index) RF positivity and/or positive anti-CCP antibodies

Measuring and Goals of RA Therapy
The ACR 2011 Task Force panel recommends Measuring RA disease activity in a quantitative fashion at least every 3-6 mos Setting the target for disease activity as either remission, or low disease activity

Thresholds of Disease Activity
Example Methods For Assessing RA Disease Activity Instrument Score Range Thresholds of Disease Activity Low Moderate High Disease Activity Score (DAS) in 28 joints 0 – 9.4 ≤ 3.2 > 3.2 – ≤ 5.1 > 5.1 Simplified Disease Activity Index (SDAI) 0.1– 86.0 ≤ 11 > 11 – ≤ 26 > 26 Clinical Disease Activity 0 – 76.0 ≤ 10 > 10 – ≤ 22 > 22 Rheumatoid Arthritis Disease Activity Index (RADAI) 0 – 10 < 2.2 ≥ 2.2 – ≤ 4.9 > 4.9† Routine Assessment Patient Index Data (RAPID) 0 – 30 < 6 ≥ 6 – ≤ 12 > 12 †Median

2011 ACR Recommendations for Early RA (< 6 months)

Established RA* Indications for DMARD therapy and Switching between DMARDs
If a patient has low disease activity after 3-6 months of DMARD monotherapy (hydroxychloroquine, methotrexate, minocycline or sulfasalazine), add methotrexate or leflunomide . If a patient has at least moderate disease activity after 3 months of methotrexate- Add another non-methotrexate DMARD to methotrexate OR Switch to a different non-methotrexate DMARD If a patient has at least moderate disease activity after 3 months of DMARD monotherapy, methotrexate or leflunomide should be added. *disease duration ≥ 6 months

2011 ACR Recommendations for Established RA

Established RA on DMARDs, When to Add a Biologic
Low disease activity and poor prognosis after 3-6 months of methotrexate monotherapy or methotrexate-based DMARD combination: Add or switch to a anti-TNF biologic At least moderate disease activity after 3 months of methotrexate monotherapy or methotrexate-based DMARD combination-therapy: Add or switch to an anti-TNF biologic OR Add or switch to abatacept or rituximab in anti-TNF-naïve patients OR Add or switch DMARDs

Established RA, When to Switch between Biologics
At least moderate disease activity 3 months after anti-TNF biologic has failed: Switch to another anti-TNF biologic or a non-TNF biologic OR Add or switch DMARDs At least moderate disease activity 6 months after failing a non-TNF biologic (abatacept, rituximab or tocilizumab): Switch biologics (no specific recommendation on which one)

Established RA Switching between Biologics Due to Harms/Adverse Event
At least moderate disease activity after failing anti-TNF biologic because of non-serious adverse events* - Switch to another anti-TNF biologic or non-TNF biologic. At least moderate disease activity after failing a non-TNF biologic because of an adverse event (serious or non-serious)- Switch to anti-TNF biologic. As defined by FDA:

Established RA Switching to Non TNF biologics
DISEASE ACTIVITY Failed anti-TNF trial Failed MTX or MTX+DMARD trial Lack/Loss of benefit after Adverse Event Duration 3 months 6 months Non serious Serious Low Disease Activity  Moderate Disease Activity  in anti-TNF-naïve patients* High Disease Activity Established RA Switching to Non TNF biologics *Abatacept and Rituximab are recommended

Established RA Switching to anti-TNF Biologics
DISEASE ACTIVITY Failed anti-TNF trial Failed non-TNF trial Failed MTX or MTX+DMARD Lack/Loss of benefit after Adverse Event Duration 3 months 6 months Non serious Serious Low Disease Activity  With poor prognosis Moderate Disease Activity  High Disease Activity Established RA Switching to anti-TNF Biologics

Use of biologics in RA patients with past treated malignancy
Recommended Level of Evidence Any treated solid malignancy or treated non-melanoma skin cancer > 5 years ago Any biologic C Treated solid malignancy or treated non-melanoma skin cancer < 5 years ago or treated melanoma skin cancer > 5 years ago Rituximab Treated lymphoproliferative malignancy (ever) Recommendations supported by evidence level C are largely based upon expert opinion

Use of biologics in RA patients with hepatitis
Recommended Not Recommended Level of Evidence Past history of acute hepatitis B with positive hepatitis B core antibody Abatacept C Hepatitis C (treated or not) Etanercept Untreated chronic Hepatitis B, or treated chronic Hepatitis B, with Child-Pugh Class B and higher Any biologic Recommendations supported by evidence level C are largely based upon expert opinion

2011 ACR RA Recommendations for TB screening with biologics
For RA patients being considered for treatment with biologics AND have previously received a Bacillus-Calmette-Guérin (BCG) vaccination- Use IGRA over TST. 2011 ACR RA Recommendations for TB screening with biologics

Tuberculosis (TB) Screening for Biologics
Other recommendations Annual testing in patients who live, travel, or work in situations where TB exposure is likely while they continue treatment with biologics. Patients who test positive for TST or IGRA at baseline are expected to remain positive for these tests even after successful treatment of TB. These patients need monitoring for clinical signs and symptoms of recurrent TB, since repeating tests will not help in diagnosis of recurrent TB

Before starting a DMARD or a biologic:
Recommended vaccination in patients* starting or currently receiving DMARDs or biologics Before starting a DMARD or a biologic: All killed vaccines (Pneumococcal, Influenza and Hepatitis B) Recombinant vaccine (Human Papilloma Virus vaccine for cervical cancer) Live attenuated vaccine (Herpes Zoster) Patients already taking a DMARD or a biologic All above vaccinations, EXCEPT Herpes Zoster Vaccine is recommended ONLY during DMARD therapy, contraindicated in biologic users (± 30d exposure) * assuming age appropriate

Biomarker-Based Assessment of RA Disease Activity Technology-based approaches Safety Infections GI Perforations CV Events

Biologics Revolutionary for Many Diseases
Ankylosing spondylitis Inflammatory bowel disease Juvenile Idiopathic arthritis (JIA) Psoriasis Psoriatic arthritis Rheumatoid arthritis (RA) Multiple sclerosis Systemic lupus Gout In large randomized controlled trials, shown to yield Improved disease related outcomes Improved function Improved quality of life Improved work and home productivity Typically very expensive (e.g. ~$30,000/year)

Biologics: Lots of Choices, Lots of Risks?
Anti-TNF Adalimumab (Humira) Certolizumab (Cimzia) Etanercept (Enbrel) Golimumab (Simponi) Infliximab (Remicaide) B-cell depleting therapies Rituximab (Rituxan) IL-1Ra Anakinra (Kineret) IL-6Ra Tocilizumab (Actemra) IL-12/23 Ustekinemab (Stelara) T-cell costimulatory inhibitors Abatacept (Orencia) Alefacept (Amevive) Autoimmune syndromes (e.g. SLE) Cancer Heart Failure Demyelinating Events (e.g. MS) Gastrointestinal Perforation Ischemic events (e.g. MI) Hypersensitivity Leukopenia Liver Failure Psoriasis (new onset) Serious Infections TB, Histo, PML, Cocci, … Death

Structure of the Anti-TNFs
p75 soluble TNF receptor Fab′ Fab PEG IgG1Fc PEGylated Fab′ Anti-TNF Recombinant receptor/Fc fusion protein Monoclonal antibody Infliximab Adalimumab Golimumab Certolizumab pegol Etanercept All three reagents are bivalent CZP is structurally different and have an active isotype Fc as it is PEGylated, univalent and does not have an Fc 58

Case Study Ms. Jones, 38 year old AA woman
Seropositive rheumatoid arthritis, diagnosed 1 year ago Treated with methotrexate 20mg/week, prednisone 10mg/day, celecoxib 200mg/day Somewhat improved, still with active disease Tender joint count = 12 Swollen joint count = 8 Rheumatologist is recommending addition of a biologic (anti-TNF therapy) In preparation for asking your opinion as her PCP, she’s done some research…

Safety Profile of 2 Biologics for RA

Case Study, continued After further searching the internet, she learns that anti-TNF therapy Doubles the risk of serious infections* Increases risk for TB 12-fold** Is associated with PML***, a severe, sometimes fatal disease for which few if any effective therapies exist Is associated with death She declines biologic therapy * Curtis JR, et al., Arthritis Rheum 2007; 56(4): ; Listing et al., Arthritis Rheum 2005;52: ** Tubach F et. al., Arthritis Rheum 2009 Jul;60(7): *** Kumar et. al. Arthritis Rheum Nov;62(11):3191-5

Challenges in Studying Biologic Safety
Biologic use associated with disease severity Biologics frequently used with prednisone or other immunosupressive agents Clinical trials not powered to detect adverse events High rate of co-morbidities in real-world patients Methodologic issues in analysis Duration of exposure of some biologics unclear Drug switching common *UK Biologics Register, 7,818 patients, October 2004 At time of biologic start 49% prednisone, 55% MTX 25% with 2 or more co-morbidities (cardiovascular or pulmonary disease most common) Hyrich et al. Ann Rheum Dis 2006

Increased Infection Due To RA Itself and Active Disease
609 RA patients and 609 controls matched on age, residence, sex* residing around Rochester, Minnesota Greater than 12 years of follow-up, Pre-biologic era Risk for hospitalized infection associated with RA: hazard ratio = ( ) CORRONA registry** More than 25,000 RA patients More active RA  higher rate of infection * adjusted for smoking, diabetes, chronic lung disease, steroid use, and leukopenia * Doran et al. Arthritis Rheum 2002; 46(9): ** Au et. al. Ann Rheum Disease May 2011;70(5):785-91

Potentially Confounding Factors: Concomitant Glucocorticoid Use
Mean daily dose of glucocorticoids (no. of treatment episodes), outcome Propensity score adjusted rate ratio (95% CI) ≤5 mg (n = 1,781) Pneumonia 0.88 ( ) Any bacterial infection 1.34 ( ) 6-9 mg (n = 1.510) 2.01 ( ) 1.53 ( ) 10-19 mg (n = 4,435) 2.97 ( ) 2.86 ( ) ≥20 mg (n = 2,891) 6.69 ( ) 5.48 ( ) Schneeweiss S. Arthritis Rheum Jun;56(6): Schneeweiss, S. et al., Arthritis Rheum 2007;56:

Dixon et. al. Arthitis Rheum 2007; 56(9): 2896-2904.
New Users, early period of treatment Ongoing Users Relative Rate of Infection: Overall: 1.0 (0.7–1.6) First 90 days: 4.6 (1.8–11.9) Dixon et. al. Arthitis Rheum 2007; 56(9):

Effect of Anti-TNF Therapy on the Incidence of Serious Infections in RA Patients: Results from Clinical Trials Summary Relative Risk of Infection = 2.0 (1.3 – 3.1) Bongartz T et al, JAMA, May , Vol 295: No. 19,

Results from Observational Studies: Serious infections under anti-TNF treatment
Incidence of serious infections in anti-TNF treated patients (per 100 patient years) RABBIT: Listing et al., Arthritis Rheum 2005;52: 6.3 BSRBR: Dixon et al., Arthritis Rheum 2006;54(8): 5.3 ARTIS: Askling et al., Ann Rheum Dis 2007;66: 5.4* Curtis JR, et al., Arthritis Rheum 2007; 56(4): 2.9** Schneeweiss S, et al., Arthritis Rheum 2007; 56(6): 2.2 Adj. relative rates of serious infections on anti-TNF therapy compared to RA patients on non-biologic DMARDS RABBIT: Listing et al., Arthritis Rheum 2005;52: 2.2 BSRBR: Dixon et al., Arthritis Rheum 2006;54(8): 1.0 (4.6 early) ARTIS: Askling et al., Ann Rheum Dis 2007;66: 1.4* Curtis JR, et al., Arthritis Rheum 2007; 56(4): 1.9 Schneeweiss S, et al., Arthritis Rheum 2007; 56(6): 1.0 *only prior hospitalized patient, first year ** in the first six months after biologic use

Results from Observational Studies: Serious infections under anti-TNF treatment
Incidence of serious infections in anti-TNF treated patients (per 100 patient years) RABBIT: Listing et al., Arthritis Rheum 2005;52: 6.3 BSRBR: Dixon et al., Arthritis Rheum 2006;54(8): 5.3 ARTIS: Askling et al., Ann Rheum Dis 2007;66: 5.4* Curtis JR, et al., Arthritis Rheum 2007; 56(4): 2.9** Schneeweiss S, et al., Arthritis Rheum 2007; 56(6): 2.2 *only prior hospitalized patient, first year ** in the first six months after biologic use

Lower resp. tract infections per 100 patient years
Resolving Discordant Results across Biologic Safety Analyses: Who is in the Control Group? Example: Comorbidities BSRBR: significant higher frequency of COPD and current smokers in the control group Lower resp. tract infections per 100 patient years Etanercept Infliximab Anti-TNF RABBIT 1.86 ( ) 3.38 ( ) 2.5 ( ) BSRBR 2.1 ( )

Rate of lower resp. tract infections per 100 patient years
Resolving Discordant Results across Biologic Safety Analyses: Who is in the Control Group? Example: Comorbidities BSRBR: significant higher frequency of COPD and current smokers in the control group Rate of lower resp. tract infections per 100 patient years Etanercept Infliximab Anti-TNF Controls RABBIT 1.86 ( ) 3.38 ( ) 2.5 ( ) 0.7 ( ) BSRBR 2.1 ( ) 2.7 ( ) Crude Incidence Rate Ratio in RABBIT registry: 2.5 / 0.7 = 3.6 Crude Incidence Rate Ratio in BSRBR registry: 2.1 / 2.7 = 0.8

Rates of Serious Infections Largely Driven by Disease, Comorbidities and Patient Factors, Not Biologics Rate of Serious Infections per 100 person-years PBO + DMARD 3.8 Combination MTX + TCZ, Overall 5.2 RR= 5.2 / 3.8 = 1.4 8% of TCZ treated patients had diabetes PBO = placebo; TCZ = tocilizumab Kremer et. al. ACR 2008, abstract 1668; Smolen et. al., ACR 2008, abstract 1669; Genovese 2008 (TOWARD); Emery 2008 (RADIATE) OPTION study MTX + TCZ 8mg/kg n = 205 RADIATE study MTX + TCZ 8mg/kg n = xx Age RA disease duration 7 +- 7 Number of prior DMARDs HAQ Steroid users, % 55% 52% Diabetes 8%

Rates of Serious Infections Largely Driven by Disease, Comorbidities and Patient Factors, Not Biologics Rate of Serious Infections per 100 person-years PBO + DMARD 3.8 Combination MTX + TCZ, Overall 5.2 TOWARD (DMARD failure, biologic naive) (MTX +TCZ 8mg/kg) vs. (MTX + PBO) 5.9 vs. 4.7 8% of TCZ treated patients had diabetes PBO = placebo; TCZ = tocilizumab Kremer et. al. ACR 2008, abstract 1668; Smolen et. al., ACR 2008, abstract 1669; Genovese 2008 (TOWARD); Emery 2008 (RADIATE) OPTION study MTX + TCZ 8mg/kg n = 205 RADIATE study MTX + TCZ 8mg/kg n = xx Age RA disease duration 7 +- 7 Number of prior DMARDs HAQ Steroid users, % 55% 52% Diabetes 8%

Rates of Serious Infections Largely Driven by Disease, Comorbidities and Patient Factors, Not Biologics Rate of Serious Infections per 100 person-years PBO + DMARD 3.8 Combination MTX + TCZ, Overall 5.2 TOWARD (DMARD failure, biologic naive) (MTX +TCZ 8mg/kg) vs. (MTX + PBO) 5.9 vs. 4.7 RADIATE (TNF Failures, refractory RA) (MTX +TCZ 8mg/kg) vs. (MTX + PBO) 9.9 vs. 9.6 8% of TCZ treated patients had diabetes Risk difference for patients on MTZ + TCZ who have diabetes compared to those who don’t is ~ 4 / 100py PBO = placebo; TCZ = tocilizumab Kremer et. al. ACR 2008, abstract 1668; Smolen et. al., ACR 2008, abstract 1669; Genovese 2008 (TOWARD); Emery 2008 (RADIATE) OPTION study MTX + TCZ 8mg/kg n = 205 RADIATE study MTX + TCZ 8mg/kg n = xx Age RA disease duration 7 +- 7 Number of prior DMARDs HAQ Steroid users, % 55% 52% Diabetes 8%

Applying Research Results to Clinical Care
How much should a ~1.5 to 2-fold increased risk of infection matter to my patients?

Putting Relative Risks into Context: Two Examples
Example Patient Hypothetical Baseline Serious Infection Rate Hypothetical RR of Infection Associated with Biologic Use Resulting Infection Rate #1: 42 yo, severe RA MTX, HCQ no other medical problems 1% / yr 2.0 2% / yr

Example Patient Hypothetical Baseline Serious Infection Rate Hypothetical RR of Infection Associated with Biologic Use Resulting Infection Rate #1: 42 yo, severe RA MTX, HCQ no other medical problems 1% / yr 2.0 2% / yr #2: 65 yo, moderate RA MTX, prednisone 7.5 mg/day Diabetes, COPD, hosp. for pneumonia last year 10% / yr 20% / yr

Safety Assessment of Anti-TNF Agents Used in Autoimmune Disease (SABER)
Sponsored by FDA / AHRQ THE UNIVERSITY OF ALABAMA AT BIRMINGHAM CCEB

Specific Aims Aim #1: To estimate incidence rate ratio (RR) of SAEs associated with each biologic agents among users and comparable nonusers To estimate the RR of SAEs after considering time since first use, duration of use, concomitant drug use and relevant comorbidities Aim #2: To estimate the RR of SAEs in vulnerable populations including (1) low income groups; (2) minority groups; (3) women (especially pregnant women); (4) children; (5) the elderly; (6) individuals classified as disabled; (7) patients with co-morbidities; (8) patients living in rural or inner city areas who may have reduced access to health care. SAE = serious adverse events

Research Plan: The Overall Structure
Executive Committee Jeffrey R. Curtis, MD, MPH Marie R. Griffin, MD, MPH Lisa J. Herrinton, PhD James D. Lewis, MD, MSCE Liyan Liu, MD Parivash Nourjah, PhD Rita Ouellet-Hellstrom, PhD, MPH Kenneth G. Saag, MD, MSc Daniel H. Solomon, MD, MPH Data Coordinating Center at KPNC Seven Working Groups (defined by outcomes)

Centers, Working Groups, & Datasets
Working Group (Outcomes Lead) Datasets used for Each Outcome UAB Infections (including Opportunistic, TB) Medicare Standard Analytic Files & MAX, HMORN Death, Pulmonary Fibrosis KPNC, Univ Penn Malignancies - Vanderbilt Congenital anomalies & pregnancy outcomes Fractures TennCare, Brigham and Women’s DEcIDE Center Cardiovascular PACE, PAAD ,’ 98- ’06 BCLHD, ’96-’06 Horizon BCBSNJ, ’96-’07

New Paradigms to Pool Data to Study Rare Adverse Events
Rassen J. Med Care Jun;48(6 Suppl):S83-9.

SABER Results for Serious Bacterial Infections

Figure 3. Incidence Rates and hazard Ratios for Specific TNF-a Antagonists and Serious Infections Among Patients with Rheumatoid Arthritis

Example Patient Hypothetical Baseline Serious Infection Rate Hypothetical RR of Infection Associated with Biologic Use Resulting Infection Rate #1: 42 yo, severe RA MTX, HCQ no other medical problems 1% / yr 2.0 2% / yr #2: 65 yo, moderate RA MTX, prednisone 7.5 mg/day Diabetes, COPD, hosp. for pneumonia last year 10% / yr 20% / yr Number needed to treat (NNT) with a biologic: To achieve a good response (ACR50): ~ 3 to 8 To result in 1 “extra” serious infection: Example patient #1: Example patient #2: 10

Is Serious Infection Risk Additive, or Multiplicative, for anti-TNF Users?
Assumptions for this hypothetical scenario DMARD rate of infection is 3 per 100 patient years; TNF user rate is 6 per 100 patient years. Rate ratio = 6 / 3 = 2.0; Rate difference is = 3.0 per 100 py

Is Serious Infection Risk Additive, or Multiplicative, for anti-TNF Users?
Assumptions for this hypothetical scenario: multiplicative risk doubles the rate of infection, additive risk increases it by 3 per 100 patient years

Infection Risk Constant for High Risk and Low Risk Patients
Curtis JR, ACR 2011 annual meeting, manuscript under review

TB Risk for those on Anti-TNF Therapy
UK Biologic Registry Background UK rate 12-14/100,000 Cochrane: TB rate 200/100,000 persons receiving drug Dixon WG et al. Ann Rheum Dis 2010:69: 90

Adjusted Odds Ratio (95% CI)
Drug-Specific Risks of Other Opportunistic Infections from French RATIO registry 45 cases of opportunistic infections Most common infections were zoster, PCP, listeria, nocardia, non-tuberculosis mycobacteria Overall absolute event rates 1.5 / 1000 py Adjusted Odds Ratio (95% CI) Most recent TNF Etanercept Adalimumab Infliximab 1.0 (referent) 10.0 (2.3 – 44.4) 17.6 (4.3 – 72.9) Prednisone > 10mg/day or bursts No Yes 6.3 (2.0 – 20.0) Salmon-Ceron et. al. Ann Rheum Dis 2011; 70:616–623

TNF Inhibitors and the Risk of Mycobacterial Infections
29,500 new users of TNFi therapy for RA (Kaiser No. Cal, U.S. Medicare + Medicaid, Tenncare) 24 NTM and 11 TB cases, IR 66.7/100,000 (95% CI 40-93) and 45.9/100,000 (14-78) patient years, respectively Rate of TB in the general population was 5.1/100,000py In the US, rate of TB among RA patients who start TNFi therapy is 9-fold higher than the general population 2-3 fold higher than among RA patients starting non-biologic RA medications Winthrop KL, et al. Rates of Tuberculosis and Nontuberculous Mycobacterial Disease among Rheumatoid Arthritis Patients Who Use Anti-Tumor Necrosis Factor-alpha Therapy; from the Safety of Biologic ThERapy (SABER) Study, ACR scientific meeting 2010, abstract #404.

Do I Need to Worry About “Endemic” Fungal Infections Outside of Endemic Regions?
3.3/100,000 p-yrs Approximately 2-fold increase in risk of Histo Rates shown per 100,000 patient years IR = Incidence Rates of Histoplasmosis in patients ≥65 from U.S. Medicare data (5% sample) J Baddley EID, in press

Progressive Multifocal Leukoencephalopathy (PML)
Demyelinating disease of CNS. Caused by the polyoma “JC” virus Pathology first described in 1958; virus identified by microscopy in 1965, by culture in 1971 Initials “JC” are from patient whose brain was used to first culture the organism in 1971 Occurs in those with depressed cell-mediated immunity 80% of population is seropositive by adulthood Disease occurs from reactivation of latent virus Clinical: mental slowness, disorientation, behavioral changes, motor dysfunction: lack of coordination, gait disturbances, ataxia, hemiparesis Imaging: multifocal areas of white matter demyelination that do not conform to vascular territories and do not exhibit mass effect or enhancement Diagnosis: imaging, biopsy, CSF PCR for JCV Death often occurs from weeks to months after diagnosis (respiratory source?) CSF PCR >90% sensitivity?

PML Clinical: mental slowness, disorientation, behavioral changes
Motor dysfunction: lack of coordination, gait disturbances, ataxia, hemiparesis Headaches are uncommon Imaging: multifocal areas of white matter demyelination that do not conform to vascular territories and do not exhibit mass effect or enhancement Diagnosis: -imaging, biopsy -CSF PCR for JCV (>90% sensitivity?) Death occurs from weeks to months after diagnosis

PML and Biologics PML increasingly associated with treatment of rheumatologic diseases (>45 cases); but rarely in patients using TNF-α antagonists Rates per 100,000 hospital discharges: SLE: 4; RA: 0.4; Other CTDs 2 Appeared in 0.1% of patients treated with natalizumab for MS or Crohn’s (31 cases ). Estimated incidence of 1 per 1000 patients. Four cases (1 in 500) with efalizumab use for plaque psoriasis Fifty-seven patients who developed PML after rituximab (2 SLE, 1 RA) Molloy and Calabrese, Arthritis Rheumatism 2009;60:

Incidence of PML in SABER
Among 712,708 unique individuals with RA, PsA, PsO, JIA, IBD, or AS, a total of 55 hospitalizations with PML diagnoses identified 55 suspected cases 29 had insurance coverage for > 6 months prior to the PML case date and > 1 physician diagnoses of a rheumatic disease that occurred before PML case date 82% with HIV; 10% with malignancy Overall case rate = 7.7 per 100,000 individuals Among biologic users, 1 cases among inflixumab users, 2 among rituximab users Case rate among patients with autoimmune diseases on biologics w/o HIV or cancer ~0.2 per 100,000 Bharat A, Curtis JR. Arthritis Care & Research, in press

What About Infections for Which We Can Vaccinate?
Patients with rheumatic and autoimmune diseases are at increased risk of herpes zoster (HZ), also known as shingles A live zoster vaccine reduces risk by 51% Treatment-related contraindication Safety concern: vaccine might trigger HZ in these patients within 4-6 weeks Safety and efficacy not clear ACIP: advisory committee on immunization practices E.g., biologics, methotrexate ≥ 0.4 mg/Kg/week, glucocorticoids > prednisone-equivalent 20mg/d Theoretical concern: No data to support or dispute this concern Varicella vaccine is safe in children infected with HIV Strangfeld et al., JAMA. 2009;301(7): Oxman et al., N Engl J Med. 2005;352(22): Harpaz et al., MMWR Recomm Rep. 2008;57(RR-5):1-30; quiz CE32-34.

Hypotheses Zoster vaccine is safe and effective in patients with selected rheumatic and autoimmune diseases Zoster vaccine not associated with increased HZ risk within 42 days after vaccination for patients currently using anti-TNF or other biologics Zoster vaccine reduces long-term HZ risk

Eligible Participants
100% of Medicare beneficiaries, Age ≥ 60 years With fee for service coverage, enrolled in a Part D drug plan ≥ 2 physician diagnoses of rheumatoid arthritis, psoriatic arthritis, psoriasis, ankylosing spondylitis, or inflammatory bowel diseases occurring between 7 and 365 days apart A baseline period of at least 6 months of continuous coverage No evidence for HZ infection during baseline period

Study Design Retrospective cohort study using 100% sample of Medicare data age >= 60 RA, psoriasis, PsA, AS, or IBD based upon >= 2 MD diagnoses Effectiveness analysis: > 42 days after vaccination Unvaccinated Person-time Start of Follow-up Vaccination End of Follow-up Safety analysis: ≤ 42 days after vaccination

Exposure and Outcome Zoster vaccination
Current Procedural Terminology (CPT) code, National Drug Code (NDC), and G code Medication exposure NDC code, CPT code Biologics, disease modifying anti-rheumatic drugs (DMARDs), and oral glucocorticoids Incident HZ infection ICD-9 diagnosis code + antiviral drug use (acyclovir, famcyclovir, valacyclovir) within 7 days ICD-9 diagnosis code only Current Procedural Terminology (CPT) code 90736 National Drug Code for zoster vaccine followed by either G code G0377 or CPT code within 7 days Patients censored if no procedure code for administration of vaccine could be identified Biologics and disease modifying anti-rheumatic drugs (DMARDs) Prescription date and days of supply Oral glucocorticoids Cumulative average in the previous 90 days

Statistical analysis Safety endpoint
HZ incidence rate ≤ 42 days after vaccination Effectiveness endpoints HZ incidence rate > 42 days after vaccination Hazard ratio Vaccinated vs. unvaccinated regardless of timing Proportional hazard regression, controlling for age, gender, race, comorbidities, concurrent medications, and health care utilization

Results 463,104 eligible patients with at least one of the 5 autoimmune diseases of interest Mean age 74 years 72% women 86% Caucasian 20,570 (4.4%) received zoster vaccine 10,032 developed HZ during follow-up Patients with RA contributed over half (65.3%) of the total person-years during follow-up

Herpes Zoster Incidence Rates, Unvaccinated
Not Exposed to Oral Glucocorticoids Medications (exclusive groups) # HZ cases* HZ IR‡ 95% CI Any anti-TNF (regardless of non-biologic DMARDs use) 738 12.6 Adalimumab 98 11.8 Etanercept 150 11.5 Infliximab 490 13.2 Other anti-TNFs <11 15.6 Any non-TNF biologics (regardless of non-biologic DMARDs use) 109 14.3 Abatacept 53 12.1 Rituximab 48 17.5 Non-biologic DMARDs without biologics 1,074 11.0 Methotrexate (regardless of other non-biologic DMARDs use) 625 10.4 All other non-Methotrexate DMARDs alone or in combination 449 11.9 *HZ, Herpes Zoster; IR, Incidence Rate per 1,000 Person-Years; 95% CI, Confidence Interval

Herpes Zoster Incidence Rates, Unvaccinated
Not Exposed to Oral Glucocorticoids Medications (exclusive groups) # HZ cases* HZ IR‡ 95% CI Any anti-TNF (regardless of non-biologic DMARDs use) 738 12.6 Adalimumab 98 11.8 Etanercept 150 11.5 Infliximab 490 13.2 Other anti-TNFs <11 15.6 Any non-TNF biologics (regardless of non-biologic DMARDs use) 109 14.3 Abatacept 53 12.1 Rituximab 48 17.5 Non-biologic DMARDs without biologics 1,074 11.0 Methotrexate (regardless of other non-biologic DMARDs use) 625 10.4 All other non-Methotrexate DMARDs alone or in combination 449 11.9 *HZ, Herpes Zoster; IR, Incidence Rate per 1,000 Person-Years; 95% CI, Confidence Interval Data Use Agreement restrictions from CMS prohibited showing non-zero cell sizes < 11

Herpes Zoster Incidence Rates, Unvaccinated, by Steroid Exposure
Exposure to Glucocorticoids No Yes Medications (exclusive groups) HZ IR‡ IR Ratio 95% CI Any anti-TNF (regardless of non-biologic DMARDs use) 12.6 22.4 1.8 Adalimumab 11.8 21.7 Etanercept 11.5 20.7 Infliximab 13.2 23.2 Other anti-TNFs 15.6 26.2 Any non-TNF biologics (regardless of non-biologic DMARDs use) 14.3 18.6 1.3 Abatacept 12.1 17.1 Rituximab 17.5 20.4 Non-biologic DMARDs without biologics 11.0 1.7 Methotrexate (regardless of other non-biologic DMARDs use) 10.4 18.2 All other non-Methotrexate DMARDs alone or in combination 11.9 19.3 *HZ, Herpes Zoster; IR, Incidence Rate per 1,000 Person-Years; 95% CI, Confidence Interval Data Use Agreement restrictions from CMS prohibited showing non-zero cell sizes < 11

Herpes Zoster Incidence Rates, Unvaccinated, by Steroid Exposure
Exposure to Glucocorticoids No Yes Medications (exclusive groups) HZ IR‡ IR Ratio 95% CI Any anti-TNF (regardless of non-biologic DMARDs use) 12.6 22.4 1.8 Adalimumab 11.8 21.7 Etanercept 11.5 20.7 Infliximab 13.2 23.2 Other anti-TNFs 15.6 26.2 Any non-TNF biologics (regardless of non-biologic DMARDs use) 14.3 18.6 1.3 Abatacept 12.1 17.1 Rituximab 17.5 20.4 Non-biologic DMARDs without biologics 11.0 1.7 Methotrexate (regardless of other non-biologic DMARDs use) 10.4 18.2 All other non-Methotrexate DMARDs alone or in combination 11.9 19.3 *HZ, Herpes Zoster; IR, Incidence Rate per 1,000 Person-Years; 95% CI, Confidence Interval

≤ 42 Days Following Vaccination
Herpes Zoster Incidence Rates by Vaccination Status and Medication Exposure Safety Endpoint: ≤ 42 Days Following Vaccination Unvaccinated # HZ # Participants IR* Overall <11 7,781 7.8 11.6 Drug Exposure Biologics (regardless of concomitant DMARDs or oral glucocorticoids) 636 - 15.8 Anti-TNF therapies 556 15.7 DMARDs (without biologics but regardless of oral glucocorticoids) 1,817 14.6 13.8 Oral glucocorticoids alone 1,215 21.2 17.1 *HZ, Herpes zoster; IR, HZ incidence rate per 1,000 person-Years Data Use Agreement restrictions from CMS prohibited showing non-zero cell sizes < 11

≤ 42 Days Following Vaccination
Herpes Zoster Incidence Rates by Vaccination Status and Medication Exposure Safety Endpoint: ≤ 42 Days Following Vaccination Unvaccinated Infections, n Vaccinated, n IR* Overall <11 7,781 7.8 11.6 Drug Exposure Biologics (regardless of concomitant DMARDs or oral glucocorticoids) 636 - 15.8 Anti-TNF therapies 556 15.7 DMARDs (without biologics but regardless of oral glucocorticoids) 1,817 14.6 13.8 Oral glucocorticoids alone 1,215 21.2 17.1 *HZ, Herpes zoster; IR, incidence rate per 1,000 person-Years Data Use Agreement restrictions from CMS prohibited showing non-zero cell sizes < 11

Effectiveness Endpoint: > 42 Days Following Vaccination
Herpes Zoster Incidence Rates by Vaccination Status and Medication Exposure Effectiveness Endpoint: > 42 Days Following Vaccination Unvaccinated # HZ # Participants IR* Overall 78 9964 7.8 11.6 Drug Exposure Biologics (regardless of concomitant DMARDs or oral glucocorticoids) <11 838 9.5 15.8 Anti-TNF therapies 725 11.0 15.7 DMARDs (without biologics but regardless of oral glucocorticoids) 1419 4.9 13.8 Oral glucocorticoids alone 15 1059 14.2 17.1 *HZ, Herpes zoster; IR, incidence rate per 1,000 person-years Data Use Agreement restrictions from CMS prohibited showing non-zero cell sizes < 11

Effectiveness Endpoint: > 42 Days Following Vaccination
Herpes Zoster Incidence Rates by Vaccination Status and Medication Exposure Effectiveness Endpoint: > 42 Days Following Vaccination Unvaccinated # HZ # Participants HZ IR* Overall 78 9964 7.8 11.6 Drug Exposure Biologics (regardless of concomitant DMARDs or oral glucocorticoids) <11 838 9.5 15.8 Ant-TNF therapies 725 11.0 15.7 DMARDs (without biologics but regardless of oral glucocorticoids) 1419 4.9 13.8 Oral glucocorticoids alone 15 1059 14.2 17.1 *HZ, Herpes Zoster; IR, Incidence Rate per 1,000 Person-Years Data Use Agreement restrictions from CMS prohibited showing non-zero cell sizes < 11

Reduced Risk of Zoster Associated With Vaccination, Varying Case Definitions
Outcome Definition Hazard Ratio* 95% CI Diagnosis code + anti-viral medications 0.69 Diagnosis code only 0.72 *Controlling for age, gender, race, concurrent medications (anti-TNF, non-TNF biologics, non-biologic DMARDs, oral glucocorticoids), and health care utilization (hospitalization and physician visits)

TNF Inhibitors and Risk of Post-Op Infections: Impact of Stop Time
On/Off at Time of Surgery On/Off 28 Days Before Surgery On Off On 28 Days Off 28 Days Infections, N (%) 49 (3.0) 15 (3.5) 59 (3.4) 5 (1.4) Adjusted OR (95% CI) Ref. ( ) ( ) SPOI and Influence of Stop Time On/Off at Surgery On/Off 28 Days Before Surgery 2 "On" 1.15 "On 28" 1.0 Adjusted OR (95% Cl) 0.6 "Off" 0.4 0.38 [2007] [OP0215] ANTI-TNF THERAPY AND THE RISK OF SERIOUS POST-OPERATIVE INFECTION: RESULTS FROM THE BSR BIOLOGICS REGISTER (BSRBR) W.G. Dixon, M. Lunt, K.D. Watson, K.L. Hyrich, BSR Control Centre Consortium, D.P.M. Symmons ARC Epidemiology Unit, University of Manchester, Manchester, United Kingdom Background: Surgery is a risk factor for infection in patients with rheumatoid arthritis (RA). It is unclear whether this risk is enhanced by anti-TNF therapy and whether anti-TNF therapy should be stopped prior to elective surgery. UK guidelines suggest that anti-TNF therapy should be stopped 2-4 weeks prior to surgery. Objectives: 1) To examine the risk of serious post-operative infection (SPOI) in patients with RA exposed to anti-TNF therapy at the time of surgery compared to anti-TNF naïve RA patients; and 2) to compare the rates of SPOI in patients exposed to anti-TNF therapy in the month preceding surgery with the rates of SPOI in patients previously exposed to anti-TNF therapy but not exposed in the month preceding surgery. Methods: All operations prior to 31st July 2006 were identified in patients with RA recruited by the British Society for Rheumatology Biologics Register (BSRBR). Patients who had received anti-TNF treatment at any time prior to surgery were categorised in the following two ways: Firstly, Group 1) exposed to drug during the 28 days preceding surgery and Group 2) not exposed to drug during the 28 days preceding surgery; Secondly, Group A) exposed to drug at the time of surgery and Group B) not exposed to drug at the time of surgery. Anti-TNF stop date was defined as the first missed dose. A cohort of biologic-naïve patients with active RA treated with traditional DMARDs was also identified. SPOIs, i.e. infections occurring within 30 days of surgery leading to intravenous antibiotics, hospitalisation or death, were identified from consultant questionnaires, patient diaries and the UK Office for National Statistics. Odds ratios (OR) were calculated after adjustment for age, gender, disease severity, diabetes and baseline steroid use, using logistic regression. Comparisons were made between Group A and the DMARD cohort, between Groups A and B and between Groups 1 and 2. Results: 1694 operations were performed on 1348 patients ever exposed to anti-TNF therapy; 179 operations were performed on 155 DMARD patients (75%) were orthopaedic procedures. 96 (7.1%) of the 1357 operations undertaken whilst exposed to an anti-TNF drug (Group A) were followed by a SPOI. 13 (7.3%) of operations in the DMARD cohort had a SPOI. The age and sex-adjusted OR was 1.07 (0.58, 1.96) and the fully adjusted OR (aOR) 1.71 (0.60, 4.89). SPOIs occurred in 103 patients (7.3%) in Group 1 and 13 (4.8%) in Group 2 (aOR 0.56 (0.30, 1.04)). The aOR comparing Group A to B was 0.75 (0.44, 1.28). Conclusion: Exposure to anti-TNF therapy at the time of surgery appears to increase the risk of SPOI compared to the DMARD cohort after allowing for other risk factors. However, the number of SPOIs in the DMARD group was small and there may be unmeasured residual confounders. In patients ever exposed to anti-TNF therapy, the risk of infection was nearly two-fold lower in those who were not exposed to the drug in the 28 days prior to surgery than in those who were exposed. Until more data accumulate, these results support the advice to stop anti-TNF therapy 4 weeks prior to elective surgery. "Off 28" 0.2 Conclusions Patients off TNF inhibitor >28 days before surgery had ~60% reduction in infections Data support discontinuing TNF inhibitor at least 4 weeks prior to surgery Dixon W, et al. Presented at: 2007 EULAR Annual Meeting. Barcelona, Spain. Abstract OP0215. 115

Treatment of PML Immune reconstitution:
-ART for HIV (prolongs survival) -discontinuation of biologics -decreasing steroids, tacrolimus, etc. Cidofovir- No benefit over ART. Toxic. Mirtazipine- improvement in case reports Cytarabine- has helped 2 patients with dermatomyositis Mefloquine- has in vitro anti-JCV activity

Cancers Relative risks Anti-TNF vs general population
Relative risks Anti-TNF vs general population Anti-TNF vs anti-TNF-naive RA Lymphomas 2.72 1.35 Standardized Incidence Ratio vs. general population All RA Anti-TNF users All solid carcinomas 1.05 0.9 Risk of Skin Cancer in TNF Users vs. Comparator RA Non-melanoma Melanoma Odds ratio (95% CI) for skin cancer 1.5 ( ) 2.3 ( ) Askling J et al. Ann Rheum Dis 2009;68:648–653. Askling J et al. Ann Rheum Dis October; 64(10): 1421–1426. Wolfe F et al. Arthritis Rheum Sep;56(9):

Are Anti-TNF Users at Higher Risk for Recurrent Malignancies?
DMARD (n =117) Anti-TNF (n =177) Person-years of followup 235 515 Median (IQR) follow-up time, yrs 1.9 (1.3–2.7) 3.1 (2.0–3.9) Incident malignancies, no. 9 13 Rate per 1,000 person-years 38.3 (17.5–72.7) 25.3 (13.4–43.2) IRR (95% CI) 1.0 (referent) 0.56 (0.23–1.35) Patients with incident malignancies, no. 9 11 Rate of incident cancers in patients with prior malignancy Dixon WG et al. Arthritis Care Res (Hoboken) June; 62(6): 755–763

Serious infections under anti-TNF treatment – Reasons for “conflicting” results
Comparing results of the various data sources is difficult Differences in absolute & relative rates may result from: Differences in patient populations (e.g. inclusion / exclusions, comorbidities) Sites & seriousness of infection Definition of an “infection” Timing since starting / stopping anti-TNF Concomitant therapies Special situations (e.g. surgery)

Understanding Infection Risks: Site of Infection
9000 patients, followed Dec 2001-Sept 2005 Physician documented infection Anti - TNF Non - biologic (n=7,664) (n=1,354) RR serious * 1.03 ( ) Ref. Infection RR skin/soft tissue * 4.3 ( ) Ref. infection 19 intracellular infections---all in anti-TNF treated (10 were TB, 1 NTM, 3 listeria, 3 salmonella, 3 legionella) No significant difference between lnfliximab, adalimumab, etanercept Trend toward more TB with monoclonal antibiodies (adjusted IRR 4.9 ( ) and 3.5 ( ) for inflix and ada compared to etanercept 4-fold higher incidence of respiratory infection in UK vs. German *Adjusted for age, sex, RA severity, extraarticular manifestations, steroids, diabetes, COPD/asthma, smoking Dixon WG et al. Arthritis and Rheum 2006; 54(8):

Understanding Infection Risks: Time since starting anti-TNF & DMARDs
Propensity score adjusted rate ratio (95% CI) Drug exposure Short-term effects (1-90 days after treatment initiation) Longer-term effects (>90 days after treatment initiation) TNF α antagonists 1.39 ( ) 0.90 ( ) Glucocorticoids 2.99 ( ) 1.20 ( ) Cytotoxic DMARDs 2.99 ( ) 0.95 ( ) Non-cytotoxic DMARDs 1.11 ( ) 1.51 ( ) Schneeweiss, S. et al., Arthritis Rheum 2007;56:

Special Situations: What to do with Biologics around Time of Surgery?
n=Operations Group A (n=1023) Group B (n=104) Group C (n=92) Number of Infections 45 6 8 Cumulative Incidence of Infection 4% 5.8% 8.7% Group A: Had not received anti-TNF Group B: Stopped anti-TNF at least 4 half lives preop: (adalimumab 64 days, etanercept 12 days, infliximab 36 days Group C: Continued anti-TNF preoperatively Perioperative use of anti-TNF was not significantly associated with an increase in surgical site infection rates (OR 1.5, 95% CI 0.43–5.2) The infections in the TNF group were NOT more severe that the other groups MTX was continued perioperatively Why different from ACR 2004 report? “There is a wide variation in infection rates in different centres” Limitations: this study is not prospective or randomized; the low infection rate resulted in a low power to detect small differences. The Dutch have started a larger multicenter study. Authors conclusions: Factors associated with postop infections are similar to other studies in patients not using anti-TNFs Continued perioperative blockade of TNF does not seem to be a strong risk factor for postoperative infection Den Broeder et. al. J Rheum 2007; 34:

Characteristics and Recommendations for Vaccination for Autoimmune Disease Patients
Inactivated Virus Live Virus* Pneumovax X - Influenza H1N1 (swine flu) Zoster vaccine Hepatitis B HPV vaccine** * Live virus vaccines not recommended for patients on biologics ** recommended for women up to age 26 Pneumovax, yearly influenza given to < 50% of RA patients age >65*** Saag, et al. ACR Recommendations for the Use of Non-biologic and Biologic Therapies in RA. ACR 2008; 59(6): *** Curtis JR. Arthritis Res Ther. 2010;12(4):R144.

Rates of GI Perforations for Patients on Biologics and DMARDs
Drug Exposure Group Rate/1000 PYs (95% CI) Biologics with glucocorticoids 1.87 (1.46–2.35) Biologics w/o glucocorticoids 1.02 (0.80–1.29) Methotrexate with glucocorticoids 2.24 (1.82–2.74) Methotrexate w/o glucocorticoids 1.08 (0.86–1.35) Other DMARDs* with glucocorticoids 3.03 (2.34–3.85) Other DMARDs* w/o glucocorticoids 1.71 (1.34–2.16) Glucocorticoids w/o any DMARD or biologic 2.86 (2.27–3.56) No DMARDs, biologics, or glucocorticoids 1.68 (1.44–1.96) Total 1.70 (1.58–1.83) Rates of perforation ~0.4-1/1000py higher with NSAID use DMARD=disease modifying antirheumatic drug; PYs=person years. *Azathioprine, chloroquine, hydroxychloroquine, cyclosporine, D-penicillamine, leflunomide, sulfasalazine, gold compounds. Curtis JR et. al. presented at EULAR 2011, London 124

Relative Risk of GI Perforation During Follow-up–Adjusted Results
1 2 3 4 11 13 15 17 19 Urban Female Age 40-64 Age 65+ Baseline CCI NSAID No DMARD or glucocorticoid Other DMARDs w/o glucocorticoids Biologics w/o glucocorticoids Biologics w/ glucocorticoids Methotrexate w/ glucocorticoids Glucocorticoids w/o any DMARD Other DMARDs w/ glucocorticoids Diverticulosis w/o diverticulitis Diverticulitis Hazard Ratios With 95% Confidence Intervals Exposure On or After Index Results of Sensitivity Analysis that Varied Definition of GI Perforation Exclusion of diverticulitis/diverticulosis + GI surgery decreased incidence rate to (95% CI, 1.12–1.34) per 1000 PYs Hazard ratio for diverticulitis ranged from 3.6 to 14.5 Reference groups are as follows: for all drug groups except NSAIDs = methotrexate without steroids; for NSAIDs = the absence of NSAIDs; for all binary variables = the absence of the condition or status. CCI=Charlson Comorbidity Index; DMARD=disease-modifying antirheumatic drug; NSAIDS=Non-Steroidal Anti-Inflammatory Drug. 125

RA Is an Independent Risk Factor for MI, Stroke
18-49 50-64 65-74 75+ (per 1000 person-years) Incidence Rate Age Range (y) Patients With RA (n=25,385) Patients Without RA (n=252,976) 10 20 30 40 50 60 70 Solomon DH et al. Ann Rheum Dis. 2006;65:

Changes in Lipids Associated with Tocilizumab (IL-6Ra)
ACT 8 (n = 288) ACT 8 + DMARD (n = 1582) ACT 4 + MTX (n = 774) Mean Change From Baseline in 6-Month Controlled Period HDL (mg/dL) LDL (mg/dL) * From tocilizumab prescribing information (PI)

Increase in Total Cholesterol associated with Anti-TNF therapy
Waiting on n-values n = 69 n = 33 n = 8 n = 50 Study *Two additional studies with total n of 35 had a mean change in total cholesterol of -5.4 (Popa, et al. Ann rheum Dis 64(2): ) and -2.3 (Perez-Galan, et al. Med Clin (Barc) 126(19): 757) mg/dL. Pollono EN. Clin Rheumatol. 2010; 29(9): 128

TNF Inhibitor Therapy in RA and CV Outcomes
Examined 10,870 patients with RA from CORRONA registry Median RA duration: 7 years Median follow-up: 2 years Conclusions Compared with non-biologic therapies excluding methotrexate (MTX) Substantial reduction in CVD risk for patients treated with TNF inhibitors (RR 0.3) Intermediate reduction in CVD risk for patients treated with MTX (RR 0.6) Prednisone an independent risk factor for CVD CV Events 2.0 1.5 HR 1.0 0.6 0.5 0.3 MTX TNF Blocker Use and Cardiovascular Outcomes Pres. Time:Monday, Oct 27, 2008, 9:00 AM -11:00 AM Location:Hall A, Poster Board: 277 Category:17. RA: clinical aspects Author(s):D. H. Solomon1, J. Curtis2, J. M. Kremer3, G. Reed4, M. A. Hochberg5, S. Setoguchi1, A. Nasir6, M. Farkhouh7, J. Greenberg6. 1Brigham & Womens Hospital, Boston, MA; 2UAB, Birmingham, AL; 3Albany Med College, Albany, NY; 4U Mass, Worcester, MA; 5U Maryland, Baltimore, MD; 6NYU-HJD, New York, NY; 7Mount Sinai, New York, NY Abstract: Purpose: Cardiovascular (CV) disease represents a major source of morbidity and mortality in RA. Data suggest that MTX may reduce CV disease. Controversy surrounds the role of TNF blockers with respect to CV risk. We examined the relative risk of MI, stroke, or TIA (CV events) for patients with RA enrolled in CORRONA treated with different DMARDs. Methods: Participants with RA in the CORRONA registry with at least two visits were selected for study. Subjects whose rheumatologists reported a new CV event and then confirmed these events on follow-up were considered to have experienced an outcome. In patients with available medical records, confirmed CV events were found to fulfill standard diagnostic criteria in 85% (95% CI 72% - 93%). Rheumatologist’s medication reports formed the basis of a longitudinal time-varying exposure record. Medication categories of interest included TNF blockers (alone or in combination), MTX (alone or in combination), and all other non-biologic DMARDs. The use of NSAIDs and steroids were included as covariates in Cox proportional hazards regression models. All models also accounted for CV risk factors (hypertension, treatment for hyperlipidemia, diabetes, prior CAD, cigarettes) and RA disease markers (DAS, HAQ, duration of RA, RF/anti-CCP status, nodules). Covariates were updated at the start of a new treatment period. Standard errors were corrected using a robust sandwich estimator to account for multiple observations from the same subject. Results: We analyzed data from 10,870 subjects, with a median follow-up of 24 monthsin CORRONA and a median RA duration of 7 years. 75% were seropositive. During the course of follow-up, 71 patients experienced a confirmed CV event MIs and 45 strokes or TIAs. Adjusted models for CV events found a reduced risk for TNF blockers (HR 0.3, 95% CI ) compared to non-biologic DMARDs that did not include MTX. MTX was associated with a trend toward a reduced risk compared with this reference group (HR 0.6, 95% CI ). Similar trends were observed for MI and stroke/TIA as secondary endpoints. Variables associated with a trend toward an increased relative risk of CV events included: older age (45-64yrs HR 4.4, 95% CI and 65+ HR 13.9, 95% CI compared with < 45yrs), male (HR 1.6, 95% CI ), modified HAQ score (HR 1.4, 95% CI per point), DAS-28 (1.2, 95% CI per point), nodules (HR 1.3, 95% CI ), hyperlipidemia (HR 2.3, 95% CI ), current cigarettes (HR 1.6, 95% CI ), and current prednisone dose (HR 1.2, 95% CI per mg). Conclusions: In this cohort study, we found a reduced risk of CV events associated with the use of TNF blockade as well as a similar trend for MTX. Several RA-related variables raised the risk of CV events as did the use of prednisone, in a dose-dependent manner. TNF Greenberg JD. Ann Rheum Dis Apr;70(4):

Risk of MI in TNF-treated patients compared to new DMARD users*
* Both TNF and DMARD users had ‘failed’ MTX Solomon et. al., ACR 2011 annual meeting (unpublished data)

Subgroup Analyses for Risk of MI in new TNF-treated patients compared to new DMARD users
Solomon et. al., ACR 2011 annual meeting (unpublished data)

Putting It All Together: Applying Research Results to Clinical Care
Communicating Risk

Know What Your Patients are Reading about Safety
“The most common side effects of Prolia® are back pain, pain in your arms and legs, high cholesterol, muscle pain, and bladder infection.” (manufacturer website at Denosumab* (n = 3886) Placebo* (n = 3876) Back pain 1347 (34.7%) 1340 (34.6%) Pain in extremity 453 (11.7%) 430 (11.1%) Musculoskeletal pain 297 (7.6%) 291 (7.5%) Hypercholesterolemia 280 (7.2%) 236 (6.1%) Cystitis 228 (5.9%) 225 (5.8%) * As observed in pivotal 3 year trial

Communicating Benefits and Risks of Biologics to Patients
“Ms. Jones, there’s a good chance that you will respond to this medication, but… “It may increase your risk of infection by 50 to 100%” OR “There is an extra 2 out of 100 chance over the next year of having a serious infection

100 patients, Active Disease, on MTX
 10 20 30 40 50 60 70 80 90 100

Likelihood of Achieving an Good Clinical Response, Remaining on MTX
☻  10 20 30 40 50 60 70 80 90 100

Likelihood of Achieving a Good Clinical Response, Adding a Biologic
☻  10 20 30 40 50 60 70 80 90 100

Likelihood of a Serious Bacterial Infection, Remaining on MTX
  10 20 30 40 50 60 70 80 90 100

Likelihood of a Serious Bacterial Infection, After Adding a Biologic
  10 20 30 40 50 60 70 80 90 100

Risk:Benefit Curve of Aggressive Therapy
Risk of Therapy limited toxicity + benefit (control of inflammation lowers risk) increased toxicity +/- benefit ……older age, disability,steroids, etc Need for Aggressive Rx Serious adverse event Death Severity of Comorbidities

Summary & Conclusions Biomarkers appear useful to assess disease activity in an objective manner and may predict future outcomes (e.g. structural damage, CV risk, future response to tx) Clinical data, perhaps in conjunction with biomarkers, may be maximally useful; technology may assist in collecting this data Infections Increased risk of infections, largely early after starting Risk difference compared to non-biologic therapies low (~1-4 / 100py) Appears similar for low vs. high risk patients No greater than risk for moderate dose glucocorticoid use Risk for zoster does not appear to be increased with vaccination, even for biologic users No apparent increase in primary or recurrent malignancy except possibly non-melanoma skin cancer

Summary & Conclusions Increases in lipids but neutral or even reduced CV risk Low absolute rates of other SAEs (e.g. gastrointestinal perforation) Lots of data, new methods needed to study rare SAE Overall risk-benefit profile of biologic therapy likely to be favourable for almost all patients who need it Communicating Risk to Patients Challenging, Better Tools Needed Absolute risk (not relative risk) likely to be most informative

Acknowledgements & Collaborators
UAB John Baddley, MD MPH Tim Beukelman, MD MSCE Aseem Bharat, MPH Lang Chen, PhD Elizabeth Delzell, ScD Mary Melton Paul Muntner, PhD Ryan Outman, MS Nivedita Patkar, MD MPH Kenneth Saag, MD MSc Monika Safford, MD Jas Singh, MD MPH Fenglong Xie, MS Shuo Yang, MS Jie Zhang, PhD OHSU Kevin Winthrop, MD U Nebraska Ted Mikuls, MD MSPH U Utah Grant Cannon, MD Scott Duvall, PhD Vanderbilt University Carlos Grijalva, MD Marie Griffin, MD Brigham and Women’s Hospital Dan Solomon, MD MPH Jeremy Rassen, ScD Sebastian Schneeweiss, ScD

Issues Affecting Interpretation of Safety Data
How data is expressed Proportion of patients vs events/ patient-yrs Clinical trial vs registry vs administrative data Adjustment for disease severity DAS, number DMARD failures, disability Adjustment for co-medication and co-morbidities e.g. steroids, non-biologic DMARDs, diabetes, prior infection

German Biologic Registry
Patients enrolled 5/2001-9/2003. Follow-up 9/2004 Physician-documented infections Etanercept Infliximab Non - biologic (n=512) (n=346) (n=601) RR Infection total * 2.3 ( ) * 3.0 ( ) Ref. RR Severe 2.8 ( ) 2.7 ( ) Ref. Infection * 2.2 ( ) * 2.1 ( ) Ref. *Adjusted for probability of receiving biologic agent (# of DMARD failures, RA duration and severity, RF positivity), steroids, age, sex, diabetes, lung disease Listing et al., Arthritis Rheum 2005; 52(11):

Understanding Infection Risks: Defining an “Infection”
Least Specific Definition Most Specific Definition ICD-9 infection claims alone Physician- Assessed ‘Definite’ or ‘Empirically treated’ infection Physician Assessed ‘Definite’ infection Met criteria of pre-specified evidence based infection definition Persons classified as having an infection*, % (sensitivity) 100 85 66 35 Unadjusted HR of Infection in anti TNF-α users 1.1 ( ) 1.1 (0.8 – 1.5) 1.4 (1.0 – 2.0) 1.6 (1.0 – 2.6) Adjusted HR of Infection in anti TNF-α users 1.6 ( ) 1.7 (1.2 – 2.6) 1.9 (1.3 – 2.8) 2.3 (1.2 – 4.3) * n = 217 suspected infections HR = hazard ratio Curtis J, et al. Arthritis Rheum. 2007;56:

Understanding Infection Risks: Time since Starting anti-TNF
Factors Associated with Hospitalization TNF Inhibitor (N=937) MTX (N=2933) P Age, years 50 55 < .001 Prior bacterial infection, % 8 7 .06 COPD, % 9 NS Diabetes, % 10 .08 6 5 4 3 2 [THU0135] DRUG-SPECIFIC, TIME-DEPENDENT RISKS OF SERIOUS BACTERIAL INFECTIONS ASSOCIATED WITH TNF-ALPHA ANTAGONISTS J.R. Curtis 1, N. Patkar 1, A. Xie 1, C. Martin 2, K. Saag 1 1Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, 2Center for Healthcare Policy and Evaluation, (CHCPE), Eden Prarie, United States Background: The risk of serious bacterial infections associated with TNF-a antagonists (anti-TNF) remains controversial. Objectives: We hypothesized that the risk of bacterial infections would differ between antibody-based and non-antibody-based anti-TNF drugs and would be highest immediately after initiating anti-TNF therapy. Methods: Using administrative data from a large U.S. healthcare organization, we identified adult RA patients during 5/98 – 12/03 receiving anti-TNF (exposed) or methotrexate (MTX) only (unexposed). Anti-TNF therapy was categorized as antibody-based (infliximab or adalimumab) or non-antibody-based (etanercept). Date of the first filled anti-TNF prescription or 3rd MTX prescription defined the `index date'. Hospitalizations with suspected bacterial infections were identified using claims data and must have occurred within 90 days of the most recent filled prescription. Associated hospital medical records were then abstracted and independently reviewed by two blinded infectious disease specialists to confirm infections. Incidence rates and rate ratios (IRRs) were calculated for each exposure group (antibody-based anti-TNF vs. MTX and non-antibody-based anti-TNF vs. MTX) in < 6 month and > 6 month risk windows following the index date. Results: We confirmed 24 bacterial infections that occurred during hospitalization among 937 antibody-based anti-TNF users, 26 infections among 1,201 non-antibody-based anti-TNF users, and 54 infections among 2,933 MTX users. Within 6 months of the index date, the corresponding incidence rates (per 100 person-yrs) and 95% confidence intervals of confirmed bacterial infections in these three groups were 4.5 (2.7 – 7.1), 1.9 (0.9 – 3.5), and 1.7 (1.0 – 2.6), respectively. Referent to MTX alone users, the related IRRs for antibody-based anti-TNF and non-antibody-based anti-TNF were 2.4 (1.4 – 5.2) and 1.2 (0.5 – 2.5), respectively. Six months after the index date, the incidence of infections in the antibody-based TNF users, non-antibody-based anti-TNF-users, and MTX users was 1.1 (0.4 – 2.4), 1.2 (0.7 – 2.0) and 1.5 (1.1 – 2.1). Corresponding IRRs were 0.7 (0.3 – 1.7) and 0.8 (0.5 – 1.5). Conclusion: Compared to RA patients using MTX, the risks of physician-confirmed bacterial infections among anti-TNF users were increased only within the first 6 months after beginning infliximab or adalimumab therapy and were not increased at later time points, nor were they increased among persons using etanercept. High vigilance for infections among RA patients is warranted, particularly early in the course of anti-TNF therapy. 1 ETN <6 INF <6 ETN >6 INF >6 Conclusion: Adjusted relative rate of infection associated with TNF inhibitor use = 1.9 Months Months Months Months Curtis J, et al. Arthritis Rheum. 2007;56: ; Curtis JR, et. al. Arthritis Rheum 2007; 56: 147

Dixon et. al. Arthitis Rheum 2007; 56(9): 2896-2904.
Relative Rate of Infection: Overall: 1.0 (0.7–1.6) Dixon et. al. Arthitis Rheum 2007; 56(9):

Serious Infection Rates
Clinical Trials Serious Infection Rates Etanercept Infliximab Adalimumab Pt Exposure (pt-yrs) 8336 2458 4870 - TNF Antagonist - Placebo 4.0 * 4.0 3.0 2.0 * Incidence per 100 pt years FDA AAC March 2003

Understanding Infection Risks: Time since Stopping anti-TNF
BSRBR: Adj. IRR for serious infections compared to controls using different at-risk periods DMARD All anti-TNF On drug (until 1st missed dose) IRR Rate per 100 PYs Referent 3.9 1.2 5.6 On drug + 90 days 1.3 6.1 Ever treated 1.4 6.3 Dixon WG et al., Arthritis Rheum 2007;56:

Infection Due To RA & Prednisone
Risk factors for serious infection RA severity, extra-articular RA, Diabetes, chronic lung disease, older age No increased risk with disease modifying anti-rheumatic drugs (DMARDS) Corticosteroids HR=1.56 (95% CI ) Doran et al. Arthritis Rheum 2002

Prednisone & Infection in RA Patients
16,788 RA patients over 3.5 years (NDB for Rheumatic Diseases) Risk of hospitalization for pneumonia Any prednisone use: Hazard Ratio (HR) 1.7 ( ) Dose effect noted < 5mg/day: HR 1.4 ( ) 5-10mg/day: HR 2.1 ( ) 10-15mg/day: HR 2.3 ( ) Controlled for RA severity and length, other co-morbidities No increase with DMARD or anti-TNF therapy Wolfe et al., Arthritis Rheum 2006; 54(2):

Infectious Complications in Elective Surgery Post Anti-TNF
Design Retrospective cohort study - RA patients undergoing elective orthopedic surgery 696 operations in 474 pts Infections criteria (pus or positive culture PLUS symptoms) Group A had not received anti-TNF Group B stopped anti-TNF at least 4 half-lives preoperatively (adalimumab 64 days, etanercept 12 days, infliximab 36 days) Group C continued anti-TNF preoperatively The most common and important complication following joint surgery is postoperative infection. It is not known whether perioperative use of anti-TNF increases the risk for infection. The objective of this Dutch study was to assess the incidence and consequences of postoperative infections and to identify risk factors for infection; and to assess whether anti-TNF treatment is associated with increased postoperative infection. All RA patients that underwent elective orthopedic surgery between January 2001 and September 2004 at this centre in the Netherlands were included in a retrospective cohort study. Multiple operations in one patient were considered independent events when the second operation was not related to complications of the first operation and when the interval between the two operations exceeded 3 months. Patients with a preoperative infection were excluded. All infections < 30 days and between 30 days and 1 year were classified using the 1992 CDC criteria and according to judgment of the treating physician. Patients were divided in three groups: group A did not use anti-TNF, group B used anti-TNF but stopped preoperatively and group C used anti-TNF but did not stop preoperatively. The cut-off point between group B and C was the cessation of anti-TNF therapy four half-lives of the drug before the operation (infliximab 36 days, etanercept 12 days and adalimumab 64 days). Infection rates were compared between groups and logistic regression analysis was performed to examine risk factors for postoperative infection. Reference: Den Broeder AA, Schraven T, De Jong E, et al. Infectious complications in elective surgery in RA patients in the anti-TNF era: a retrospective study. EULAR 2005, Abstract #OP0014. Den Broeder AA, et al. EULAR 2005, Abstract OP0014.

British Biologic Registry
Anti-TNF Therapy And Risk Of Serious Post-operative Infection: Results From BSR Biologics Register (BSRBR) Methods: Pts who had received anti-TNF any time prior to surgery were categorised in the following manner: Group A: exposed to time of surgery or Group B: not exposed to the time of surgery Group 1: Exposed to drug < 28 days preceding surgery or Group 2: Exposed to drug > 28 days preceding surgery OR calculated (adjusted using logistic regression) Dixon, W G. et al. Abstract # OP0215 EULAR 2007

British Biologic Registry
Anti-TNF Therapy And Risk Of Serious Post-operative Infection: Results From BSR Biologics Register (BSRBR) Gp 1: On drug during 28 days pre-surgery Gp 2: Off drug during 28 days pre-surgery Gp A: On drug at time of surgery Gp B: Off drug at time of surgery DMARD Group # of operations 1421 273 1357 337 179 SPOI n (%) 103 (7.3) 13 (4.8) 96 (7.1) 20 (5.9) 7.3% Odds Ratio (95%CI)* Referent 0.63 (0.35, 1.14) 0.81 (0.49, 1.35) 1.07 (0.58,1.96) Odds Ratio (95%CI)** 0.56 (0.30, 1.04) 0.75 (0.44, 1.28) 1.71 (0.60, 4.89) *Sex & age adjusted; ** Fully adjusted Group A: Exposed to time of surgery Group B: Not exposed to the time of surgery Group 1: Exposed to drug < 28 days preceding surgery or Group 2: Exposed to drug > 28 days preceding surgery Conclusion: Exposure to anti-TNF therapy at the time of surgery increases risk of SPOI compared to DMARD cohort Risk of infection nearly two-fold lower in pts not exposed to anti-TNF therapy < 28 days prior to surgery than those who were Dixon, W G. et al. Abstract # OP0215 EULAR 2007

Are Serious Bacterial Infections Increased in RA Patients Receiving TNFi?
Complicated issue Appears to be increased early after starting anti-TNF therapy Data controversial Differences in populations, comorbidities, and methodologies likely account for seemingly different results For responders, may be offset by reduction in disease activity and tapering glucocorticoids Importance of a drug-related increased relative rate of infection largely related to patient factors No greater risk than with moderate dose glucocorticoid use Overall risk-benefit profile of anti-TNF therapy likely to be favourable for almost all patients Communicating Risk to Patients Remains Challenging

Measurement Considerations In Rheumatology: Integrating Biomarkers, Technology, Safety, and Comorbidities to Assess Risks and Benefits of Treatment Jeffrey.

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Measurement Considerations In Rheumatology: Integrating Biomarkers, Technology, Safety, and Comorbidities to Assess Risks and Benefits of Treatment Jeffrey.

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