Skip to main content

Main menu

  • Home
  • Content
    • First Release
    • Current
    • Archives
    • Collections
    • Audiovisual Rheum
    • 50th Volume Reprints
  • Resources
    • Guide for Authors
    • Submit Manuscript
    • Payment
    • Reviewers
    • Advertisers
    • Classified Ads
    • Reprints and Translations
    • Permissions
    • Meetings
    • FAQ
    • Policies
  • Subscribers
    • Subscription Information
    • Purchase Subscription
    • Your Account
    • Terms and Conditions
  • About Us
    • About Us
    • Editorial Board
    • Letter from the Editor
    • Duncan A. Gordon Award
    • Privacy/GDPR Policy
    • Accessibility
  • Contact Us
  • JRheum Supplements
  • Services

User menu

  • My Cart
  • Log In

Search

  • Advanced search
The Journal of Rheumatology
  • JRheum Supplements
  • Services
  • My Cart
  • Log In
The Journal of Rheumatology

Advanced Search

  • Home
  • Content
    • First Release
    • Current
    • Archives
    • Collections
    • Audiovisual Rheum
    • 50th Volume Reprints
  • Resources
    • Guide for Authors
    • Submit Manuscript
    • Payment
    • Reviewers
    • Advertisers
    • Classified Ads
    • Reprints and Translations
    • Permissions
    • Meetings
    • FAQ
    • Policies
  • Subscribers
    • Subscription Information
    • Purchase Subscription
    • Your Account
    • Terms and Conditions
  • About Us
    • About Us
    • Editorial Board
    • Letter from the Editor
    • Duncan A. Gordon Award
    • Privacy/GDPR Policy
    • Accessibility
  • Contact Us
  • Follow Jrheum on BlueSky
  • Follow jrheum on Twitter
  • Visit jrheum on Facebook
  • Follow jrheum on LinkedIn
  • Follow jrheum on YouTube
  • Follow jrheum on Instagram
  • Follow jrheum on RSS
Research ArticleSpondyloarthritis
Open Access

Cut-Offs for Disease Activity States in Axial Spondyloarthritis With Ankylosing Spondylitis Disease Activity Score (ASDAS) Based on C-Reactive Protein and ASDAS Based on Erythrocyte Sedimentation Rate: Are They Interchangeable?

Stylianos Georgiadis, Lykke Midtbøll Ørnbjerg, Brigitte Michelsen, Tore K. Kvien, Daniela Di Giuseppe, Johan K. Wallman, Jakub Závada, Sella A. Provan, Eirik Klami Kristianslund, Ana Maria Rodrigues, Maria José Santos, Žiga Rotar, Katja Perdan Pirkmajer, Dan Nordström, Gary J. Macfarlane, Gareth T. Jones, Irene van der Horst-Bruinsma, Pasoon Hellamand, Mikkel Østergaard and Merete Lund Hetland
The Journal of Rheumatology July 2024, 51 (7) 673-677; DOI: https://doi.org/10.3899/jrheum.2023-1217
Stylianos Georgiadis
1S. Georgiadis, PhD, L.M. Ørnbjerg, MD, PhD, Copenhagen Center for Arthritis Research (COPECARE), Center for Rheumatology and Spine Diseases, Centre for Head and Orthopaedics, Rigshospitalet, Glostrup, Denmark;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Stylianos Georgiadis
  • For correspondence: stylianos.georgiadis@regionh.dk
Lykke Midtbøll Ørnbjerg
1S. Georgiadis, PhD, L.M. Ørnbjerg, MD, PhD, Copenhagen Center for Arthritis Research (COPECARE), Center for Rheumatology and Spine Diseases, Centre for Head and Orthopaedics, Rigshospitalet, Glostrup, Denmark;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Lykke Midtbøll Ørnbjerg
Brigitte Michelsen
2B. Michelsen, MD, PhD, Center for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, and Research Unit, Sørlandet Hospital, Kristiansand, Norway, and Copenhagen Center for Arthritis Research (COPECARE), Center for Rheumatology and Spine Diseases, Centre for Head and Orthopaedics, Copenhagen University Hospital Rigshospitalet, Glostrup, Denmark;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Brigitte Michelsen
Tore K. Kvien
3T.K. Kvien, MD, PhD, E.K. Kristianslund, MD, PhD, Center for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Tore K. Kvien
Daniela Di Giuseppe
4D. Di Giuseppe, PhD, Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Daniela Di Giuseppe
Johan K. Wallman
5J.K. Wallman, MD, PhD, Department of Clinical Sciences Lund, Rheumatology, Skåne University Hospital, Lund University, Lund, Sweden;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Johan K. Wallman
Jakub Závada
6J. Závada, MD, PhD, Institute of Rheumatology, Prague, Czech Republic and Department of Rheumatology, First Faculty of Medicine, Charles University, Prague, Czech Republic;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jakub Závada
Sella A. Provan
7S.A. Provan, MD, PhD, Center for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, and Public Health Section, Inland Norway University of Applied Sciences, Elverum, Norway;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Sella A. Provan
Eirik Klami Kristianslund
3T.K. Kvien, MD, PhD, E.K. Kristianslund, MD, PhD, Center for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Eirik Klami Kristianslund
Ana Maria Rodrigues
8A.M. Rodrigues, MD, PhD, EpiDoC Unit, CEDOC, Nova Medical School, and Rheumatology Unit, Hospital dos Lusíadas, Lisbon, Portugal;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ana Maria Rodrigues
Maria José Santos
9M.J. Santos, MD, PhD, Department of Rheumatology, Hospital Garcia de Orta, Almada, and Instituto Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Centro Académico de Medicina de Lisboa, Lisbon, Portugal;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Maria José Santos
Žiga Rotar
10Ž. Rotar, MD, PhD, K. Perdan Pirkmajer, MD, Department of Rheumatology, University Medical Centre Ljubljana, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Žiga Rotar
Katja Perdan Pirkmajer
10Ž. Rotar, MD, PhD, K. Perdan Pirkmajer, MD, Department of Rheumatology, University Medical Centre Ljubljana, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Katja Perdan Pirkmajer
Dan Nordström
11D. Nordström, MD, PhD, Departments of Medicine and Rheumatology, Helsinki University Hospital, Helsinki, Finland;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Dan Nordström
Gary J. Macfarlane
12G.J. Macfarlane, MD, PhD, G.T. Jones, PhD, Aberdeen Centre for Arthritis and Musculoskeletal Health (Epidemiology Group), University of Aberdeen, Aberdeen, UK;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Gary J. Macfarlane
Gareth T. Jones
12G.J. Macfarlane, MD, PhD, G.T. Jones, PhD, Aberdeen Centre for Arthritis and Musculoskeletal Health (Epidemiology Group), University of Aberdeen, Aberdeen, UK;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Gareth T. Jones
Irene van der Horst-Bruinsma
13I. van der Horst-Bruinsma, MD, PhD, Rheumatology, Radboud University Medical Center, Nijmegen, the Netherlands;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Irene van der Horst-Bruinsma
Pasoon Hellamand
14P. Hellamand, MD, Department of Rheumatology and Clinical Immunology, Amsterdam University Medical Center, Amsterdam, Netherlands and Amsterdam Rheumatology Immunology Center, Reade, and Amsterdam UMC, Amsterdam, the Netherlands;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Pasoon Hellamand
Mikkel Østergaard
15M. Østergaard, MD, PhD, DMSc, M.L. Hetland, MD, PhD, DMSc, Copenhagen Center for Arthritis Research (COPECARE), Center for Rheumatology and Spine Diseases, Centre for Head and Orthopaedics, Rigshospitalet, Glostrup, and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Mikkel Østergaard
Merete Lund Hetland
15M. Østergaard, MD, PhD, DMSc, M.L. Hetland, MD, PhD, DMSc, Copenhagen Center for Arthritis Research (COPECARE), Center for Rheumatology and Spine Diseases, Centre for Head and Orthopaedics, Rigshospitalet, Glostrup, and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Merete Lund Hetland
  • Article
  • Figures & Data
  • Supplemental
  • Info & Metrics
  • References
  • PDF
PreviousNext
Loading

Abstract

Objective Ankylosing Spondylitis Disease Activity Score based on C-reactive protein (ASDAS-CRP) is recommended over ASDAS based on erythrocyte sedimentation rate (ASDAS-ESR) to assess disease activity in axial spondyloarthritis (axSpA). Although ASDAS-CRP and ASDAS-ESR are not interchangeable, the same disease activity cut-offs are used for both. We aimed to estimate optimal ASDAS-ESR values corresponding to the established ASDAS-CRP cut-offs (1.3, 2.1, and 3.5) and investigate the potential improvement of level of agreement between ASDAS-ESR and ASDAS-CRP disease activity states when applying these estimated cut-offs.

Methods We used data from patients with axSpA from 9 European registries initiating a tumor necrosis factor inhibitor. ASDAS-ESR cut-offs were estimated using the Youden index. The level of agreement between ASDAS-ESR and ASDAS-CRP disease activity states was compared against each other.

Results In 3664 patients, mean ASDAS-CRP was higher than ASDAS-ESR at both baseline (3.6 and 3.4, respectively) and aggregated follow-up at 6, 12, or 24 months (1.9 and 1.8, respectively). The estimated ASDAS-ESR values corresponding to the established ASDAS-CRP cut-offs were 1.4, 1.9, and 3.3. By applying these cut-offs, the proportion of discordance between disease activity states according to ASDAS-ESR and ASDAS-CRP decreased from 22.93% to 19.81% in baseline data but increased from 27.17% to 28.94% in follow-up data.

Conclusion We estimated the optimal ASDAS-ESR values corresponding to the established ASDAS-CRP cut-off values. However, applying the estimated cut-offs did not increase the level of agreement between ASDAS-ESR and ASDAS-CRP disease activity states to a relevant degree. Our findings did not provide evidence to reject the established cut-off values for ASDAS-ESR.

Key Indexing Terms:
  • axial spondyloarthritis
  • patient outcome assessment
  • registry data
  • validation study

The Ankylosing Spondylitis Disease Activity Score (ASDAS) is the recommended instrument for assessing disease activity in patients with axial spondyloarthritis (axSpA).1 ASDAS is a composite index with continuous measurement properties developed by the Assessment of SpondyloArthritis international Society (ASAS) for assessing ankylosing spondylitis.2 ASDAS was developed in 2 versions that include the same variables, except for the acute-phase reactants C-reactive protein (CRP) or erythrocyte sedimentation rate (ESR), which have slightly different weighting.2 ASDAS-CRP is the recommended version of ASDAS, with ASDAS-ESR as an alternative in case CRP is not available.3 Although both ASDAS versions demonstrated a high level of agreement when they were developed, it has been suggested that they might not be interchangeable.3

The ASAS group has established cut-off values (1.3, 2.1, and 3.5) to classify patients into 4 distinct disease activity states: inactive disease (ID; < 1.3), low disease activity (LDA; 1.3 ≤ ASDAS-CRP < 2.1), high disease activity (HDA; (2.1 ≤ ASDAS-CRP ≤ 3.5) and very high disease activity (VHDA; > 3.5).4,5 These cut-offs were first developed for ASDAS-CRP and then cross-validated for ASDAS-ESR, leading to a recommendation to use the same cut-off values for both ASDAS versions. However, ASDAS-ESR showed a trend to classify higher numbers of patients in lower disease activity states compared to ASDAS-CRP and lower numbers of patients in higher disease activity states.4 Thereafter, some studies reported equal mean ASDAS scores,6-8 whereas others have observed differences.9-12 Hence, there may be clinically relevant discrepancies between the 2 versions and, consequently, between their cut-off values for disease activity states, which could affect treatment decisions in a treat-to-target regimen.

For this study based on a large, multinational cohort of patients with axSpA, we aimed to (1) assess the level of agreement between ASDAS-CRP and ASDAS-ESR disease activity states using the established formulas and cut-off values; (2) identify optimal ASDAS-ESR values corresponding to the established ASDAS-CRP cut-off values; and (3) assess the level of agreement between ASDAS-CRP and ASDAS-ESR disease activity states when applying the newly estimated cut-off values.

METHODS

Study population. We used data from 9 European registries with available ASDAS-ESR and ASDAS-CRP values that participated in the EuroSpA Research Collaboration Network: ARC (the Netherlands), ATTRA (Czech Republic), Biorx.si (Slovenia), BSRBR-AS (UK), NOR-DMARD (Norway), Reuma.pt (Portugal), ROB-FIN (Finland), RRBR (Romania), and SRQ (Sweden). We included patients with a clinical diagnosis of axSpA at age ≥ 18 years, who initiated a tumor necrosis factor inhibitor (TNFi) as a first biologic treatment between January 1, 2010, and December 31, 2018. Data at baseline and 3 follow-up visits (6, 12, and 24 months) during the first TNFi treatment course were collected within specified time windows.13

To have the best representation of the disease activity states, follow-up data (6, 12, or 24 months) were used to assess the ASDAS-CRP cut-offs for ID and between LDA and HDA, whereas baseline data were used to assess the cut-off for VHDA. Therefore, only patients with a complete registration of ASDAS scores at baseline and at least 1 complete follow-up registration of ASDAS scores were included.

Statistical methods. The relationship between ASDAS-CRP and ASDAS-ESR was analyzed using Spearman correlation coefficient (ρ) and the agreement between the 2 ASDAS versions was additionally visualized using Bland-Altman plots. ASDAS-CRP and ASDAS-ESR were compared according to their cut-off values using proportion of discordance, sensitivity, specificity, balanced accuracy and Cohen kappa coefficient (Embedded Image). In addition, the level of agreement between disease activity states based on the ASDAS-CRP and ASDAS-ESR cut-off values was measured using proportion of discordance and weighted Embedded Image coefficient. These analyses were carried out in data from up to 3 different follow-up timepoints for each patient included in the study.

We performed receiver-operating characteristic (ROC) analyses to determine the optimal ASDAS-ESR values corresponding to ASDAS-CRP cut-offs using the Youden index (= sensitivity + specificity − 1). The stability of the selected optimal cut-off values was assessed by nonparametric bootstrap sampling, based on 500 bootstrap samples.14 Multiple samples were drawn randomly with replacement from the original data, and the optimal cut-off values were estimated in each sample. For the selection of ASDAS-CRP cut-offs for ID and between LDA and HDA (follow-up data), the earliest timepoint was prioritized.

Ethics. All participating registries obtained the necessary approvals in accordance with legal, compliance, and regulatory requirements from national data protection agencies and/or research ethics boards prior to the data transfer to the EuroSpA coordinating center.

RESULTS

We analyzed data from 3664 patients with a mean (SD) age of 42.0 (12.3) years and disease duration of 6.7 (8.4) years at initiation of their first TNFi, of whom 2244 (61.24%) were men. The mean (SD) values of ASDAS scores at all available baseline (N = 3664) and follow-up (N = 7824) observations were 3.6 (1.0) and 1.9 (0.9) for ASDAS-CRP, and 3.4 (1.0) and 1.8 (0.9) for ASDAS-ESR, respectively (see Supplementary Table S1 for corresponding values of ASDAS components and disease activity states per timepoint, available with the online version of this article).

ASDAS-CRP and ASDAS-ESR were very strongly correlated at all timepoints (ρ ranged from 0.87 to 0.92, P < 0.001). Bland-Altman plots showed that ASDAS-CRP was higher than ASDAS-ESR; however, discrepancies seemed to be evenly distributed (Supplementary Figure S1, available with the online version of this article). Fewer patients met the ID and LDA cut-offs of < 1.3 and < 2.1, respectively, by ASDAS-CRP than by ASDAS-ESR, whereas the percentage of patients with VHDA by the ASDAS-CRP cut-off of > 3.5 was higher than by ASDAS-ESR (Table 1). We observed good agreement between the established ASDAS-ESR and ASDAS-CRP cut-offs, but slightly worse agreement for the cut-off for VHDA compared to the other 2 cut-offs. The proportions of discordance between disease activity states based on the ASDAS-ESR and ASDAS-CRP cut-off values were 22.93% in baseline data and 27.17% in follow-up data, with weighted Embedded Image values of 0.59 (P < 0.001) and 0.54 (P < 0.001), respectively, indicating a moderate level of agreement (Table 2).

View this table:
  • View inline
  • View popup
Table 1.

Statistical measures comparing ASDAS-ESR and ASDAS-CRP cut-off values for classifying patients in different disease activity states.

View this table:
  • View inline
  • View popup
Table 2A.

Distribution of observations in different disease activity states by established ASDAS-ESR and ASDAS-CRP cut-off values and level of agreement between disease activity states in baseline data (N = 3664).

View this table:
  • View inline
  • View popup
Table 2B.

Distribution of observations in different disease activity states by established ASDAS-ESR and ASDAS-CRP cut-off values and level of agreement between disease activity states in follow-up data (N = 7066).

The estimated ASDAS-ESR values for ID, between LDA and HDA, and for VHDA corresponding to ASDAS-CRP cut-offs 1.3, 2.1, and 3.5 were 1.4, 1.9, and 3.3, respectively (Table 3). The stability of the optimal cut-off values was assessed by bootstrapping. Out of the 500 bootstrap samples, the triplet of the estimated cut-off values (1.4, 1.9, and 3.3) was selected 114 (22.8%) times, whereas (1.4, 1.9, and 3.2) was selected 72 (14.4%) times. Frequencies of selection for individual values are shown in Supplementary Figure S2 (available with the online version of this article).

View this table:
  • View inline
  • View popup
Table 3.

ASDAS-ESR cut-off estimation that corresponds to established ASDAS-CRP cut-off values.

The proportion of discordance comparing the estimated ASDAS-ESR (< 1.4, < 1.9, and > 3.3) and ASDAS-CRP (< 1.3, < 2.1, and > 3.5) cut-off values was better overall in baseline data as compared to applying the same cut-offs for both ASDAS versions, whereas the proportion of discordance in follow-up data increased slightly with the estimated cut-off values (Supplementary Table S2, available with the online version of this article). Proportions of discordance between disease activity states based on the estimated ASDAS-ESR and the established ASDAS-CRP cut-off values decreased slightly from 22.93% to 19.81% in baseline data, but it increased from 27.17% to 28.94% in follow-up data, with weighted Embedded Image values of 0.64 (P < 0.001) and 0.53, respectively (P < 0.001; see Supplementary Table S3). Comparing the estimated and the established ASDAS-CRP cut-off values, we observed that 11.35% and 13.38% of observations changed disease activity states at baseline and follow-up, respectively (Supplementary Table S4).

DISCUSSION

In our present study, using large European observational datasets, fewer patients were classified as having ID and LDA when using the ASDAS-CRP than ASDAS-ESR, whereas more patients were classified as having VHDA. The ASDAS-ESR cut-off values identified through our analyses were only slightly different from the established ASDAS-CRP cut-offs. Applying the newly estimated cut-offs did not yield a clinically relevant decrease in discordance in classification. Our findings are important for clinicians managing patients with axSpA, as they indicate that the established ASDAS-CRP cut-offs can be reliably used for assessing disease activity and guiding treatment decisions, despite the slight differences observed between ASDAS-CRP and ASDAS-ESR.

Our cohort revealed higher mean ASDAS-CRP than mean ASDAS-ESR at both baseline and follow-up, which is consistent with a previous smaller study,12 although other studies have demonstrated lower ASDAS-CRP than ASDAS-ESR.9-11 In previous studies, the absolute difference between mean ASDAS-CRP and mean ASDAS-ESR ranged from zero6-8 to 1 unit or higher,9,10 whereas we found corresponding differences close to zero. This discrepancy suggests that findings from small single-country cohorts may not be generalizable to the overall axSpA population. Considering the disease activity states according to the established ASDAS-CRP and ASDAS-ESR cut-off values (1.3, 2.1, and 3.5), the observed proportions of discordance were in line with a German cross-sectional study.8 However, the reported weighted Embedded Image coefficient was higher in the German study than in our data.

We used ROC analyses to identify ASDAS-ESR cut-off values corresponding to established ASDAS-CRP cut-off values for disease activity states. An alternative would have been to redevelop cut-offs for both ASDAS-ESR and ASDAS-CRP against external clinical criteria. When we performed bootstrapping analyses, a high stability of the identified values was shown, whereas a trend to even lower estimated cut-offs between LDA and HDA and for VHDA than the established ones was illustrated.

Although Machado et al4 developed the original ASDAS cut-offs based on the first published formula for ASDAS-CRP,2 we applied the updated ASDAS-CRP formula, where the constant value of 2 mg/L was used for CRP values < 2 mg/L, which showed better agreement with ASDAS using high-sensitivity CRP.15 An additional strength of our analyses is the use of a large dataset from 9 countries, limiting the effect of single-center factors (eg, genetic composition of the included population). In addition, our study used real-world data, enhancing the generalizability of our findings. The lack of strict inclusion criteria is evident, since a minority of patients were already in LDA or inactive at baseline. In clinical practice, such patients may have initiated a TNFi due to extramusculoskeletal manifestations such as psoriasis or inflammatory bowel disease. A potential limitation for our analyses is the varying limits of detection for laboratory instruments measuring CRP across centers in these countries, which may have influenced the estimation of the optimal cut-off values, especially the cut-off for ASDAS-ESR ID.

We observed good agreement between established ASDAS-CRP and ASDAS-ESR cut-offs in terms of proportion of discordance, sensitivity, specificity, balanced accuracy, and Cohen Embedded Image. When comparing the newly estimated and the established ASDAS-ESR cut-offs, an improvement in the statistical measures was demonstrated only for VHDA. Somewhat to our surprise, we did not find an overall increase in agreement regarding the disease activity state of the individual patients when assessing the newly estimated and the established ASDAS-ESR cut-offs against ASDAS-CRP.

Across European registries, we estimated the optimal ASDAS-ESR values corresponding to established ASDAS-CRP cut-off values. Regarding the agreement between disease activity state between ASDAS-CRP and ASDAS-ESR, the original cut-offs overall performed similarly as the ones we estimated. Our findings did not provide evidence to reject the established ASDAS-ESR cut-off values.

ACKNOWLEDGMENT

On behalf of the EuroSpA Scientific Committee, the authors acknowledge Novartis Pharma AG and IQVIA for supporting the EuroSpA Research Collaboration Network.

Footnotes

  • The EuroSpA Research Collaboration Network was financially supported by Novartis Pharma AG. Novartis had no influence on the data collection, statistical analyses, manuscript preparation, or decision to submit the manuscript.

  • S. Georgiadis and L.M. Ørnbjerg contributed equally as joint first authors.

  • M.L. Hetland and M. Østergaard contributed equally as joint senior authors.

  • The following conflicts of interest are reported: SG: Novartis; LMØ: Novartis; BM: Novartis; TKK: AbbVie, Amgen, BMS, Celltrion, Galapagos, Gilead, Grünenthal, Novartis, Pfizer, Sandoz, UCB; JKW: AbbVie, Amgen, Eli Lilly, Novartis, Pfizer; JZ: AbbVie, AstraZeneca, Egis, Eli Lilly, Novartis, Sandoz, Sanofi, Sobi, UCB; SAP: Boehringer Ingelheim; AMR: AbbVie, Amgen, Novartis, Pfizer; MJS: AbbVie, AstraZeneca, Janssen, Lilly, Novartis, Pfizer; ZR: AbbVie, Amgen, AstraZeneca, Biogen, Eli Lilly, Janssen, Lek, Medis, MSD, Novartis, Pfizer, Sanofi, SOBI, Swixx BioPharma; KPP: AbbVie, Boehringer Ingelheim, Eli Lilly, Janssen, Lek, Medis, MSD, Novartis, Pfizer; DN: AbbVie, BMS, Lilly, MSD, Novartis, Pfizer, Roche, UCB; GJM: GSK; GTJ: AbbVie, Amgen, GSK, Janssen, Pfizer, UCB; IvdHB: AbbVie, BMS, Lilly MSD, Novartis, Pfizer, UCB; PH: Novartis; MØ: AbbVie, BMS, Boehringer Ingelheim, Celgene, Eli Lilly, Hospira, Janssen, Merck, Novartis, Novo, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, UCB; MLH: AbbVie, Biogen, BMS, CellTrion, Eli Lilly, Janssen Biologics, Lundbeck Fonden, MSD, Medac, Novartis, Pfizer, Roche, Samsung Biopies, Sandoz, UCB. DDG and EKK declare no conflicts of interest relevant to this article.

  • Accepted for publication March 13, 2024.
  • Copyright © 2024 by the Journal of Rheumatology

This is an Open Access article, which permits use, distribution, and reproduction, without modification, provided the original article is correctly cited and is not used for commercial purposes.

REFERENCES

  1. 1.↵
    1. Ramiro S,
    2. Nikiphorou E,
    3. Sepriano A, et al.
    ASAS-EULAR recommendations for the management of axial spondyloarthritis: 2022 update. Ann Rheum Dis 2022;17:1-16.
    OpenUrl
  2. 2.↵
    1. Lukas C,
    2. Landewé R,
    3. Sieper J, et al.
    Development of an ASAS-endorsed disease activity score (ASDAS) in patients with ankylosing spondylitis. Ann Rheum Dis 2009;68:18-24.
    OpenUrlAbstract/FREE Full Text
  3. 3.↵
    1. van der Heijde D,
    2. Lie E,
    3. Kvien TK, et al.
    ASDAS, a highly discriminatory ASAS-endorsed disease activity score in patients with ankylosing spondylitis. Ann Rheum Dis 2009;68:1811-8.
    OpenUrlAbstract/FREE Full Text
  4. 4.↵
    1. Machado P,
    2. Landewé R,
    3. Lie E, et al.
    Ankylosing Spondylitis Disease Activity Score (ASDAS): defining cut-off values for disease activity states and improvement scores. Ann Rheum Dis 2011;70:47-53.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    1. Machado PM,
    2. Landewé R,
    3. van der Heijde DV
    ; Assessment of SpondyloArthritis international Society (ASAS). Ankylosing Spondylitis Disease Activity Score (ASDAS): 2018 update of the nomenclature for disease activity states. Ann Rheum Dis 2018;77:1539-40.
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. Godfrin-Valnet M,
    2. Prati C,
    3. Puyraveau M,
    4. Toussirot E,
    5. Letho-Gyselink H,
    6. Wendling D.
    Evaluation of spondylarthritis activity by patients and physicians: ASDAS, BASDAI, PASS, and flares in 200 patients. Joint Bone Spine 2013;80:393-8.
    OpenUrlCrossRefPubMed
  7. 7.
    1. Fernández-Espartero C,
    2. De Miguel E,
    3. Loza E, et al.
    Validity of the Ankylosing Spondylitis Disease Activity Score (ASDAS) in patients with early spondyloarthritis from the Esperanza programme. Ann Rheum Dis 2014;73:1350-5.
    OpenUrlAbstract/FREE Full Text
  8. 8.↵
    1. Proft F,
    2. Schally J,
    3. Brandt HC, et al.
    Validation of the ASDAS with a quick quantitative CRP assay (ASDAS-Q) in patients with axial SpA: a prospective multicentre cross-sectional study. Ther Adv Musculoskelet Dis 2022;14:1759720X221085951.
    OpenUrl
  9. 9.↵
    1. Eroğlu Demir S,
    2. Aytekin E,
    3. Özgönenel L,
    4. Rezvani A,
    5. Pekin Doğan Y,
    6. Sayiner Çağlar N.
    A possible correlation among different disease activity parameters and functional status in patients with ankylosing spondylitis. Turk J Rheumatol 2013;28:117-22.
    OpenUrl
  10. 10.↵
    1. Chung HY,
    2. Chui ETF,
    3. Lee KH,
    4. Tsang HHL,
    5. Chan SCW,
    6. Lau CS.
    ASDAS is associated with both the extent and intensity of DW-MRI spinal inflammation in active axial spondyloarthritis. RMD Open 2019;5:e001008.
    OpenUrlAbstract/FREE Full Text
  11. 11.↵
    1. Song BW,
    2. Jeong HJ,
    3. Kim BY, et al.
    Bath Ankylosing Spondylitis Disease Activity Index is associated with the quality of sleep in ankylosing spondylitis patients. J Rheum Dis 2021;28:143-9.
    OpenUrl
  12. 12.↵
    1. Chen YH,
    2. Huang WN,
    3. Chen YM, et al.
    The BASDAI cut-off for disease activity corresponding to the ASDAS scores in a Taiwanese cohort of ankylosing spondylitis. Front Med 2022;9:856654.
    OpenUrl
  13. 13.↵
    1. Ørnbjerg LM,
    2. Brahe CH,
    3. Askling J, et al.
    Treatment response and drug retention rates in 24 195 biologic-naïve patients with axial spondyloarthritis initiating TNFi treatment: routine care data from 12 registries in the EuroSpA collaboration. Ann Rheum Dis 2019;78:1536-44.
    OpenUrlPubMed
  14. 14.↵
    1. Hesterberg T.
    Bootstrap. WIREs Computational Stats 2011;3:497-526.
    OpenUrl
  15. 15.↵
    1. Machado P,
    2. Navarro-Compán V,
    3. Landewé R,
    4. Van Gaalen FA,
    5. Roux C,
    6. van der Heijde D.
    Calculating the ankylosing spondylitis disease activity score if the conventional C-reactive protein level is below the limit of detection or if high-sensitivity C-reactive protein is used: an analysis in the DESIR cohort. Arthritis Rheumatol 2015;67:408-13.
    OpenUrl

DATA AVAILABILITY

The data in this article were collected in the individual registries and made available for secondary use through the EuroSpA Research Collaboration Network (https://eurospa.eu/#registries).

ONLINE SUPPLEMENT

Supplementary material accompanies the online version of this article.

PreviousNext
Back to top

In this issue

The Journal of Rheumatology
Vol. 51, Issue 7
1 Jul 2024
  • Table of Contents
  • Table of Contents (PDF)
  • Index by Author
  • Editorial Board (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word about The Journal of Rheumatology.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Cut-Offs for Disease Activity States in Axial Spondyloarthritis With Ankylosing Spondylitis Disease Activity Score (ASDAS) Based on C-Reactive Protein and ASDAS Based on Erythrocyte Sedimentation Rate: Are They Interchangeable?
(Your Name) has forwarded a page to you from The Journal of Rheumatology
(Your Name) thought you would like to see this page from the The Journal of Rheumatology web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Cut-Offs for Disease Activity States in Axial Spondyloarthritis With Ankylosing Spondylitis Disease Activity Score (ASDAS) Based on C-Reactive Protein and ASDAS Based on Erythrocyte Sedimentation Rate: Are They Interchangeable?
Stylianos Georgiadis, Lykke Midtbøll Ørnbjerg, Brigitte Michelsen, Tore K. Kvien, Daniela Di Giuseppe, Johan K. Wallman, Jakub Závada, Sella A. Provan, Eirik Klami Kristianslund, Ana Maria Rodrigues, Maria José Santos, Žiga Rotar, Katja Perdan Pirkmajer, Dan Nordström, Gary J. Macfarlane, Gareth T. Jones, Irene van der Horst-Bruinsma, Pasoon Hellamand, Mikkel Østergaard, Merete Lund Hetland
The Journal of Rheumatology Jul 2024, 51 (7) 673-677; DOI: 10.3899/jrheum.2023-1217

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero

 Request Permissions

Share
Cut-Offs for Disease Activity States in Axial Spondyloarthritis With Ankylosing Spondylitis Disease Activity Score (ASDAS) Based on C-Reactive Protein and ASDAS Based on Erythrocyte Sedimentation Rate: Are They Interchangeable?
Stylianos Georgiadis, Lykke Midtbøll Ørnbjerg, Brigitte Michelsen, Tore K. Kvien, Daniela Di Giuseppe, Johan K. Wallman, Jakub Závada, Sella A. Provan, Eirik Klami Kristianslund, Ana Maria Rodrigues, Maria José Santos, Žiga Rotar, Katja Perdan Pirkmajer, Dan Nordström, Gary J. Macfarlane, Gareth T. Jones, Irene van der Horst-Bruinsma, Pasoon Hellamand, Mikkel Østergaard, Merete Lund Hetland
The Journal of Rheumatology Jul 2024, 51 (7) 673-677; DOI: 10.3899/jrheum.2023-1217
del.icio.us logo Twitter logo Facebook logo  logo Mendeley logo
  • Tweet Widget
  •  logo
Bookmark this article

Jump to section

  • Article
    • Abstract
    • METHODS
    • RESULTS
    • DISCUSSION
    • ACKNOWLEDGMENT
    • Footnotes
    • REFERENCES
    • DATA AVAILABILITY
    • ONLINE SUPPLEMENT
  • Figures & Data
  • Supplemental
  • Info & Metrics
  • References
  • PDF

Keywords

AXIAL SPONDYLOARTHRITIS
PATIENT OUTCOME ASSESSMENT
registry data
VALIDATION STUDY

Related Articles

Cited By...

More in this TOC Section

  • Knowledge, Awareness, and Attitudes Regarding Axial Spondyloarthritis Among Nonrheumatology Physicians in the United States
  • Effect of Age on Active and Structural Magnetic Resonance Imaging Lesions in Sacroiliac Joints of Healthy Individuals and Patients With Nonspecific Back Pain
  • Predictors of Secukinumab Treatment Response and Continuation in Axial Spondyloarthritis: Results From the EuroSpA Research Collaboration Network
Show more Spondyloarthritis

Similar Articles

Keywords

  • axial spondyloarthritis
  • patient outcome assessment
  • registry data
  • validation study

Content

  • First Release
  • Current
  • Archives
  • Collections
  • Audiovisual Rheum
  • COVID-19 and Rheumatology

Resources

  • Guide for Authors
  • Submit Manuscript
  • Author Payment
  • Reviewers
  • Advertisers
  • Classified Ads
  • Reprints and Translations
  • Permissions
  • Meetings
  • FAQ
  • Policies

Subscribers

  • Subscription Information
  • Purchase Subscription
  • Your Account
  • Terms and Conditions

More

  • About Us
  • Contact Us
  • My Alerts
  • My Folders
  • Privacy/GDPR Policy
  • RSS Feeds
The Journal of Rheumatology
The content of this site is intended for health care professionals.
Copyright © 2025 by The Journal of Rheumatology Publishing Co. Ltd.
Print ISSN: 0315-162X; Online ISSN: 1499-2752
Powered by HighWire