a quantitative approach to accurate classification of ra. tom huizinga
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A quantitative approach to accurate classification of RA. Tom Huizinga. Overview of seminar. RA as a disease versus syndrome - perspective from a disease - perspective from a syndrome - PowerPoint PPT PresentationTRANSCRIPT
A quantitative approach to accurate classification of RA.
Tom Huizinga
Overview of seminar
• RA as a disease versus syndrome- perspective from a disease- perspective from a syndrome
• Treatment and being quantitative- early treatment- treatment focussed at a target- is there any difference in the way a target is defined?
Classification: syndrome versus disease
• RA=classic syndrome defined by criteria.Now new criteria based on the decision to start with MTX.
• RA as a disorder based on pathogenesis• Syndrome
Disease
Disease subsets witha pathway leading tosymptoms
244 (58%)109 (51%)42 (21%)- / -
153 (36%)88 (41%)111 (55%)+ / -
26 (6%)16 (7%)50 (25%)+ / +
negativepositive
Anti-CCP antibodies
ControlsLeiden EAC RA patients
SE-status*
Association between anti-CCP-responses and HLA-DRB1 SE-alleles
OR allele frequency: CCP+ vs Controls: 3.38 (2.61-4.38)
CCP- vs Controls: 1.22 (0.93-1.60)
Huizinga TW…..Criswell L, A&R, 2005
RA consists of two syndromes: ACPA+ versus ACPA-
ACR-classification proces:define disease based on characteristic cases
ACPA+ versus ACPA-
What about other risk factors?Histology?
Clinical Course?Treatment response?
RA consists of two syndromes: ACPA+ versus ACPA-
ACR-classification proces:define disease based on characteristic cases
ACPA+ versus ACPA-
HLA-SE
PTPN22
rs- C5-TRAF1
rs- TNFAIP3-OLIG3
rs- CTLA4
rs- STAT4
rs- CCL21rs-MMEL1-TNFRSF14
rs-CDK6, PRKCQ, KIF5A
CD40, IL2RA, IL2RB
HLA-DR3rs- IRF5rs- STAT4
Raychaudhuri S et al. Nat Genet. 2008 Oct;40(10):1216-23van der Helm A & Huizinga T. Arthr Res Ther. 2008;10(2):205.Huizinga et al. A&R, Arthritis Rheum. 2005 Nov;52(11):3433-8.
Conclusions
Synovitis of anti-CCP positive RA differs from anti-CCP negative:
•More infiltrating lymphocytes in anti-CCP positive RA
•More fibrosis and increased synovial lining layer in anti-CCP negative RA
•Difference is already present early in the disease
van Oosterhout M, Bajema I, Levarht EW, Toes RE, Huizinga TW, van Laar JM.
Arthritis Rheum. 2008 Jan;58(1):53-60
Phenotype clearly different
Joint destruction over time drug free remission rate
Fulfillment of the criteria for RA after1 Year 2 Years 3 Years
#69 CCP+ Pts 83% 90% 93%
249 CCP- Pts 18% 24% 25%
318 Pts 32% 38% 40%
Can the Course of UA being altered by Early Therapy ?
Undifferentiated Arthritis
ACR-criteria RA
if so verum MTX
Inclusion: Primary End point:
Increase MTX based on DAS
Placebo
t = 0 t = 3 t = 12 t = 18
15 mg
6 tabs
0 mg
0 tabs
15 – 30 mg
6 – 12 tabs
t = 6 t = 9 t = 15
MTX Taper MTX to 0
Anti-CCP pos group (n=27)p=0.0002
Anti-CCP neg group (n=83)p=0.51
Time to diagnosis RA (months)
Cu
mu
lati
ve S
urv
ival
(%
)
MTX groupPlacebo group
0 3 6 9 12 15 18 21 24 27 300
20
40
60
80
100
0 3 6 9 12 15 18 21 24 27 300
20
40
60
80
100
30 Months Follow-up
Anti-CCP pos group (n=27)p=0.03
Anti-CCP neg group (n=83)p=0.46
Ra
dio
gra
ph
ic p
rog
res
sio
n(S
har
p/v
an
de
r H
eijd
e s
co
re)
Cumulative probability (%)MTX group
Placebo group
0 25 50 75 100
0
5
10
15
20
10
15
49
0 25 50 75 100
0
5
Radiographic Progression
DAS in time stratifiedDAS in time stratified
MTXPlacebo
DA
S
Time (months)
ACPA pos ACPA neg
Summary of ACPA positive versus ACPA negative RA
• HLA, PTPN22, smoking point to two diseases• C5-TRAF point to two diseases• Output of WGAS studies point to two diseases• Phenotypic data more “formally” studied• Histological differences• Subanalysis of PROMPT-study• Propose as new criteria RA-type 1 and RA-type
2, to get criteria closer to pathogenesis
Overview of seminar
• RA as a disease versus syndrome- perspective from a disease- perspective from a syndrome
• Treatment and being quantitative- early treatment- treatment focussed at a target- is there any difference in the way a target is defined?
Chronic,destructivepolyarthritis
Slowly progressive
Rapidlyprogressive
Generalpopulation
Undifferentiatedarthritis
Timing and Uncertainty
Window of Opportunity hypothesis• Concept of time not a biological basis
• Criteria discussion leads to nosology – better to stick to probabilities
• Biology of probabilities – masterswitch
Tom Huizinga. Personal dataTom Huizinga. Personal data
Since 1993 2400 patients included with > two year follow-up
800 undifferentiated arthritis
900 RA700 other diagnosis
Diagnosis at inclusion
Lessons from Leiden Early Arthritis Cohort
40 % remission 40 % RA
Prediction Rule for Development of RA
1. What is the age? Multiply with 0.02
2. What is the gender? In case female 1 point________
3. How is the distribution of involved joints?
In case small joints hands or feet: 0.5 point________
In case symmetric 0.5 point________
In case upper extremities 1 point________
Or: In case upper & lower extremities 1.5 points ________
4. What is the length of the morning stiffness (minutes)?
In case 30–59 minutes 0.5 point________
In case ≥60 minutes 1 point________
5. What is the number of tender joints?
In case 4–10 0.5 point________
In case 11 or higher 1 point________
6. What is the number of swollen joints?
In case 4–10 0.5 point________
In case 11 or more 1 point________
7. What is the C-reactive protein level (mg/L)?
In case 5–50 0.5 point________
In case 51 or higher 1.5 points________
8. Is the rheumatoid factor positive? If yes 1 point________
9. Are the anti-CCP antibodies positive? If yes 2 points ________
TOTAL SCORE:________
van der Helm-van Mil AH, et al. Arthritis Rheum 2008;58:2241–7
Predicted Risk on RA vs Prediction Score
AUC
0.84
0.88
Replicated in UK, Norway, Germany, Japan, Middle east and Latin America
AUC=area under the curve;van der Helm-van Mil AH, et al. Arthritis Rheum 2008;58:2241–7
Prediction Thinking is Now Implemented in the 2010 Criteria
1. Age (multiply by 0.02)2. Gender (female 1)3. Distribution of involved joints
– Small joints hands and feet (0.5)– Symmetric (0.5)– Upper extremities (1)
or upper and lower extremities (1.5)4. VAS morning stiffness
– 26–90 mm (1)– 90 mm (2)
1. Morning stiffness 2. Arthritis of 3 or more joint areas3. Arthritis of hand joints 4. Symmetric arthritis5. Rheumatoid nodules6. Serum rheumatoid factor7. Radiographic changes
1. Joint involvement – 1 medium-large joint (0)– 2–10 medium-large joints– 1–3 small joints (large joints not counted) (2)– 4–10 small joints (large joints not counted (3)– >10 joints (at least one small joint) (5)
2. Serology – Negative RF and negative ACPA (0)– Low positive RF or now positive ACPA (2)– High positive RF or high positive ACPA (3)
5. Number of tender joints – 4–10 (0.5)– 11 or more (1)
6. Number of swollen joints – 4–10 (0.5)– 11 or more (1)
7. C-reactive protein (mg/L)– 5–50 (0.5)– 51 or more (1.5)
8. Rheumatoid factor positive (1)9. Anti-CCP antibodies positive (2)
3. Acute phase reactants– Normal CRP and normal
ESR (0)– Abnormal CRP or
abnormal ESR (1)
4. Duration of symptoms – <6 weeks (0)– ≥6 weeks (1)
Points are shown in parenthesis. Cut point for RA ≥8 points
Four of these 7 criteria must be present. Criteria 1 through 4 must have been
present for at least 6 weeks
Points are shown in parenthesis. Cut point for RA ≥6 points. Patients are also classified as having RA if they have (a) typical erosions; (b) long-standing disease previously satisfying
the classification criteria
1. Arnett FC, et al. Arthritis Rheum 1988;31:315-24; 2. New ACR/EULAR diagnostic criteria. Presented at ACR, Philadelphia, 10–16th October 2009; 3. van der Helm-van Mil AHM, et al. Arthritis & Rheum 2007:56;433–440
ACR 1987 criteria1 ACR/EULAR 2010 criteria2
Early Arthritis Prediction 2007-van der Helm3
A more sensitive tool for identifying early arthritis patients
(n=2258 Leiden Early Arthritis Patients)
2010 ACR/EULAR Classification Criteria
RA at baseline no RA at baseline
1987 ACRClassification
Criteria
RA at baseline 644 82
no RA at baseline 455 1077
Total 1099 1159
Earlier detection of RA
297 patients fulfilled the 1987 ACR criteria during the first year, but not at baseline
202 (68.0%) however did fulfill the 2010 criteria at baseline
RA patients classified in an earlier phase of the disease
Performance in early arthritis
Outcome Measure
MTX-initiation DMARD-initiation 5-year Persistency
Criteria Set Sens. Spec. AUC Sens. Spec. AUC Sens. Spec. AUC
1987 ACRClassification
Criteria0.61 0.74 0.67 0.54 0.87 0.71 0.53 0.75 0.61
2010 ACR/EULAR
Classification Criteria
0.84 0.60 0.72 0.74 0.74 0.74 0.71 0.65 0.65
Overview of seminar
• RA as a disease versus syndrome- perspective from a disease- perspective from a syndrome
• Treatment and being quantitative- early treatment: biology & observational- treatment focussed at a target- is there any difference in the way a target is defined?
ACPA characteristics :a biomarker of the window of opportunity
Population Undifferentiated Artritis
Reumatoide Artritis
ACPAFew isotypes
limited epitope recognitiononly low avidities
Many isotypesextensive epitope
recognitionhigh and low avidities
No changesin ACPA
characteristics
The developing autoimmune response associates with worse prognosis
Results pre-RA versus RA 2
Number of epitopes recognized by sera from:
Recognition of ≥ 1 peptide: 38% 66% p=0.013
None≥ 1 peptide
pre-RA RA
Vimentin peptide A
Vimentinpeptide B
Fibrinogen peptide A
Enolase peptide
Fibrinogen peptide B
Number of epitopes recognized increase from pre-RA to RA
Median number of peptides recognized over time
ACPA characteristics :a biomarker of the window of opportunity
Population Undifferentiated Artritis
Reumatoide Artritis
ACPAFew isotypes
limited epitope recognitiononly low avidities
Many isotypesextensive epitope
recognitionhigh and low avidities
No changesin ACPA
characteristics
What is the relevance of this developing autoimmune response during early artritis?
A broader isotype usage is associated with Radiographic progression
* comparison ≤4 isotypes versus ≥5 isotypes: p<0.05
EAC
* comparison ≤4 isotypes versus ≥5 isotypes: p<0.05
A broader isotype usage is associated with Radiographic progression
EURIDISS
Aim of early treatment
• To prevent functional disability• To prevent structural damage• To prevent comorbidity
(cardiovascular disease, amyloidosis)• To prevent “MasterSwitches” turned on that induce
chronicity
Time
is
important
Delay < 12 weeks
associates with:
lower rate of joint destruction*
higher chance of DMARD-free remission*
Conclusion:Delay should bediminished
RA-only
Chronic,destructive
polyarthritis
Slowly progressive
Rapidlyprogressive
Generalpopulation
Undifferentiatedarthritis
Why Recommendation 1: Window of Opportunity
Window of Opportunity hypothesis:- Criteria discussion: probabilities.- Biology of probabilities: masterswitch- ACPA only know marker of this process
Overview of seminar
• RA as a disease versus syndrome- perspective from a disease- perspective from a syndrome
• Treatment and being quantitative- early treatment: biology & observational- treatment focussed at a target- is there any difference in the way a target is defined?
Importance of patient monitoring: evidence from RCT
• TICORA1
– Intensive: monthly, DAS guided– Routine: every 3 months– Remission: 65% (intensive) vs. 16% (routine)
• CAMERA2 – Intensive: monthly, computer program– Routine: every 3 months usual care rheumatologist– Remission: 50% (intensive) vs. 37% (routine)
1.Grigor et al. Lancet 2004; 364: 263–269
2.Verstappen et al. Ann Rheum Dis 2007; 66: 1443–1449
Importance of patient monitoring: evidence from longitudinal patient cohorts
• Early Arthritis Cohort Leiden– Patients treated from ’93–’95 with Pyramid strategy– Patients treated from ’95–’98 with DMARD within
two weeks
Comparison after 4 years EAC
Delayed treatment
1.0
0.9
0.8
0.7
0.6
0 2 4 6 8 10 12 14
Years after inclusion
Su
rviv
al p
rob
abili
ty
1993–1995
Survival curves of RA patients and the general Dutch population
Early Arthritis Cohort Leiden
Early treatment
1.0
0.9
0.8
0.7
0.6
0 2 4 6 8 10 12 14
Years after inclusion
Su
rviv
al p
rob
abili
ty
1996–1998
Survival curves of RA patients and the general Dutch population
Early Arthritis Cohort Leiden
Early, aggressive treatment, goal-driven
1.0
0.9
0.8
0.7
0.6
0 2 4 6 8 10 12 14
Years after inclusion
Su
rviv
al p
rob
abili
ty
1999–2006
Survival curves of RA patients and the general Dutch population
Early Arthritis Cohort Leiden
RA management today
• Remission– Clinical– Radiographic
• Low disease activity
Processes
Goals“Remission”
Tools“More & Better”• More conventional
DMARDs• Biologics available as
highly effective alternatives
“More & Better”• Early treatment is key• Aggressive therapy
approach with better results• Disease activity measurement
(e.g. DAS28)
Overview of seminar
• RA as a disease versus syndrome- perspective from a disease- perspective from a syndrome
• Treatment and being quantitative- early treatment: biology & observational- treatment focussed at a target- is there any difference in the way a target is defined?Perspective : ?Biology?-?Swollen joint etc.?-?Function?
Biomarker-based DAS
42
IRIDESCENTAcademic database of
relationships from abstracts
IRIDESCENTAcademic database of
relationships from abstracts
IngenuityCommercial database of curated scientific facts
IngenuityCommercial database of curated scientific facts
Bioinformatics Knowledge
bases
Literature ReviewHundreds of
scientific articles and posters
Literature ReviewHundreds of
scientific articles and posters
Manual Survey of Scientific
Publications
Gene Expression1416 genes with secreted
proteins profiled in 424 RA patients
Gene Expression1416 genes with secreted
proteins profiled in 424 RA patients
Protein Arrays180 proteins profiled in 410
patients
Protein Arrays180 proteins profiled in 410
patients
Proprietary Molecular Profiling
Data
Review evidence and prioritize
Identify Assays:Analysis of Multiple
Platforms
Optimize Assays:Dilutions
RF BlockingQC metrics
396 Candidate Markers
Shen et al. Stepwise discovery of disease activity biomarkers in rheumatoid arthritis. EULAR 2010; Poster # THU0066
Pre-Analytic Validity: Results Individual Markers
Biomarker Avg. % Difference“OTC” vs. “Fresh”
R2 Conc.[log10 pg/mL]
CRP 0 1.00
EGF 1005 .58
ICAM-1 -2 0.97
IL-6 779 0.05
IL6-R 0 0.56
IL-8 83383 0.01
IL-B 2940 0.05
Leptin -29 0.94
MDC 0 0.91
MMP-1 20 0.97
MMP-3 -1 0.97
Resistin 230 0.74
SAA -4 1.00
TNF-RI 16 0.97
Biomarker Avg. % Difference“OTC” vs. “Fresh”
`
VCAM-1 -1 0.97
VEGF 121 0.85
YKL-40 80 0.87
COMP 1 1.00
ICAM-3 59 0.74
ICTP -7 0.87
IL-2RA 24 0.91
IP-10 10 0.98
MCSF 88 0.71
OPG 32 0.23
RANKL 0 1.00
THBD 10 0.96
TIMP-1 6 0.94
Qureshi et al. Pre-Analytical Effects of Serum Collection and Handling in Quantitative Immunoassays for Rheumatoid Arthritis; ACR 2010; Poster #1606
Training: 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
CRP
IL-6SAA
YKL-40
EGFTNF-RI
LeptinVEGF-AVCAM-1
MMP-1MMP-3
Resistin
TJC28SJC28
PatientGlobal CRP
Biomarkers Used To Predict Each DAS
Component
Bakker et al. Development of a Multi-Biomarker Test for Rheumatoid Arthritis (RA) Disease Activity (Vectra™ DA). ACR 2010; Poster #1753
DAS28CRP=0.56√TJC + 0.28√SJC + 0.14PG + 0.36log(CRP+1) + 0.96TJC=tender joint count; SJC=swollen joint count; PG =patient global health
Vectra DA Score =(0.56√PTJC + 0.28√PSJC + 0.14PPG + 0.36log(CRP+1) + 0.96) * 10.53 +1PT JC=predicted TJC, PSJC=predicted SJC, PPG =predicted PG
Vectra™ DA Validation (RF+ and/or Anti-CCP+):Patient Cohort Characteristics
Parameter BRASS Leiden InFoRM Total
n 87 77 66 230
Gender, % female 83 70 76 77
Median Age (IQR) 58 (48-69) 56 (45-65) 59 (50-66) 58 (48-66)
RF-positive, % 95 91 94 93
CCP-positive, % 93 87 82 88
Median Tender Joint Count (IQR) 15 (4-22) 1 (0-6) 6 (0-21) 5 (0-18)
Median Swollen Joint Count (IQR) 12 (5-17) 0 (0-4) 4 (0-11) 4 (0-12)
Median CRP in mg/L (IQR) 7 (3-15) 7 (3-17) 6 (2-21) 7 (3-17)
Mean Patient Global VAS (IQR) 47 (25-70) 34 (17-50) 45 (16-70) 42 (19-65)
Median DAS28CRP (IQR) 5.5 (3.8-6.5) 2.7 (2.0-4.2) 4.2 (2.2-6.0) 4.1 (2.3-5.8)
Curtis et al. Validation of a Multi-Biomarker Test for Rheumatoid Arthritis (RA) Disease Activity (Vectra™ DA) in a Multi-Cohort Study. ACR 2010; Poster #1782
Vectra™ DA Validation (RF+ and/or Anti-CCP+): Results
• Pearson Correlation = 0.56• The Vectra DA score was also associated with DAS28-CRP (p<0.05) within subgroups of RA patients who were <65 years of
age, ≥65, male, female, overweight (body-mass index >25),not overweight, on anti-TNF medications, on methotrexate but not biologics and on steroids.
Curtis et al. Validation of a Multi-Biomarker Test for Rheumatoid Arthritis (RA) Disease Activity (Vectra™ DA) in a Multi-Cohort Study. ACR 2010; Poster #1782; Data on file Crescendo Bioscience
Vectra™ DA Validation (RF+ and/or Anti-CCP+): Ability to Detect Low Disease Activity
• The exploratory analysis shows that patients with low Vectra DA scores tended to have a higher likelihood of low joint counts than those with low CRP
• Although these results were not statistically significant, they do suggest that the Vectra DA score may more accurately detect low joint counts than CRP.
Curtis et al. Validation of a Multi-Biomarker Test for Rheumatoid Arthritis (RA) Disease Activity (Vectra™ DA) in a Multi-Cohort Study. ACR 2010; Poster #1782
Vectra™ DA Validation (RF+ and/or Anti-CCP+): Biomarkers Other Than CRP
• In a multivariate regression analysis of predictors of the DAS28CRP using the Vectra DA score (without CRP) and CRP as predictors, both the Vectra DA score (without CRP) and CRP were statistically significant (p<0.001)
• Since the DAS28CRP includes CRP itself, a multivariate regression analysis was carried out to evaluate both CRP and the Vectra DA Score (without CRP) as predictors of the DAS28CRP with CRP removed– The Vectra DA score (without CRP) was statistically
significant (p<0.001), and the CRP term was not significant (p=0.22).
Curtis et al. Validation of a Multi-Biomarker Test for Rheumatoid Arthritis (RA) Disease Activity (Vectra™ DA) in a Multi-Cohort Study. ACR 2010; Poster #1782
Predictors of HAQ response after 3 months of treatment with different
strategies in recent onset active RA are different than predictors of rapid
radiological progression
BeStTreatment Strategies in Rheumatoid Arthritis
MTX
monotherapy
MTX
monotherapy
MTX + SSA + pred MTX + IFX
Sequential monotherapy
n=126
Step-up combination
n=121
Initial combination with prednisone
n=133
Initial combination with infliximab
n=128
BeSt trial
Each strategy further treatment steps per 3 months if DAS >2.4
Predictors RRP
Predictors Odds ratio 95% CIRF/ACPA both negative 1 positive both positive
ref2.54.0
1.01-6.11.9-8.5
Erosions 0 1-4 4
ref1.33.8
0.6-3.11.6-8.9
CRP mg/L <10 10-35 35
ref1.54.8
0.7-3.22.3-9.7
Therapy mono combi prednisone combi IFX
ref0.20.1
0.1-0.40.1-0.3
RF and ACPA
+/+-/-
321
321
1063<10
431
631
1494
1283
15104
34241135
Erosions (num
ber)
Initial combination with IFX
CR
P (
mg/
L)
321321
41-40
10634316311494128315104342411
10-35
+/- or -/+
1-4
41-40
41-40
RF and ACPA+/+-/-
321
321
1063<10
431
631
1494
1283
15104
34241135
Erosions (num
ber)
Initial combination with IFX
CR
P (
mg/
L)
321321
41-40
41-40
10634316311494128315104342411
10-35
+/- or -/+
1-4
41-40
41-40
41-40
41-40
RF and ACPA+/+-/-
<10
35
Erosions (num
ber)
Initial monotherapy
CR
P (
mg/
L)
41-40
10-35
+/- or -/+
1-4
41-40
41-40
17115
21146
433216
23167
29209
544222
493719
564424
786947
17115211464332162316729209544222493719564424786947
RF and ACPA+/+-/-
<10
35
Erosions (num
ber)
Initial monotherapy
CR
P (
mg/
L)
41-40
41-40
10-35
+/- or -/+
1-4
41-40
41-40
41-40
41-40
17115
21146
433216
23167
29209
544222
493719
564424
786947
17115211464332162316729209544222493719564424786947
RF and ACPA+/+-/-
<10
35
Erosions (num
ber)
Initial combination with prednisone
CR
P (
mg/
L)
41-40
10-35
+/- or -/+
1-4
41-40
41-40
421
531
1384
641
852
19125
16104
20136
423015
4215311384641852191251610420136423015
RF and ACPA+/+-/-
<10
35
Erosions (num
ber)
Initial combination with prednisone
CR
P (
mg/
L)
41-40
41-40
10-35
+/- or -/+
1-4
41-40
41-40
41-40
41-40
421
531
1384
641
852
19125
16104
20136
423015
4215311384641852191251610420136423015
<10
10-20
20-50
Risk of RRP (%)
50
Matrix: RRP after 1 year of treatment
Predictors HAQ >=1Baseline predictors OR (95% CI)
Initial treatment mono
combo prednisone
combo infliximab
ref
0.3 (0.2 - 0.5)
0.4 (0.2 - 0.6)
HAQ < 1.4
1.4 - 2.0
> 2.0
ref
2.6 (1.6 - 4.2)
5.3 (2.9 - 9.5)
VAS pain < 40
(tertiles) 40 - 60
> 60
ref
2.2 (1.3 - 3.8)
2.7 (1.4 - 5.1)
RAI < 10
(tertiles) 10 -16
> 16
ref
1.7 (1.02 - 3.0)
2.7 (1.5 - 4.7)
> 2 45 64 69 50 68 73 >16
35 54 59 39 58 63 10-16
23 40 45 27 45 50 <10
1.4 - 2 29 47 52 25 46 51 >16
21 37 41 24 41 46 10-16
13 25 29 15 29 33 <10
<1.4 14 25 29 16 29 33 >16
9 18 21 11 21 25 10-16
5 11 14 7 13 16 <10
< 40 40-60 >60 < 40 40-60 >60
> 2 73 86 88 >16
64 80 83 10-16
51 70 74 <10
1.4 - 2 58 75 79 >16
47 66 70 10-16
34 53 58 <10
< 1.4 34 53 58 >16
25 43 48 10-16
16 30 35 <10
< 40 40-60 >60
Matrix: predicted risk HAQ ≥ 1 after 3 monthsMonotherapy
Combo with prednisone Combo with infliximab
VAS pain
VAS pain
HA
QH
AQ
RA
I
RA
I
High risk
Intermediate risk
Lower risk
Low risk
Differences RRP and HAQ model
• Of all 508 patients in the BeSt, 12% had a HAQ ≥ 1 after three months of treatment as well as RRP after one year.
• Thus, it seems that short-term functional ability and radiological damage progression are different concepts.
• The choice of the best initial treatment is dependent on the relevance of the respective outcome measures for an individual patient.
Which target is relevant for which patient?
Patient developssymptoms Patient visits GP
GP refers patient toRheumatologist
Guidance of treatment possible by prediction based on serum-based activity measurments
or measurements focussed at
prevention of damage versus function
Relevance of CCP-test
DELAY has a price (less remission, more destruction,
more suffering)
Measure disease activity