Predicting the outcome of ankylosing spondylitis therapy

Nathan Vastesaeger, Désirée Van Der Heijde, Robert D. Inman, Yanxin Wang, Atulya (Atul) Deodhar, Benjamin Hsu, Mahboob U. Rahman, Ben Dijkmans, Piet Geusens, Bert Vander Cruyssen, Eduardo Collantes, Joachim Sieper, Jürgen Braun

Research output: Contribution to journalArticle

113 Citations (Scopus)

Abstract

Objectives To create a model that provides a potential basis for candidate selection for anti-tumour necrosis factor (TNF) treatment by predicting future outcomes relative to the current disease profile of individual patients with ankylosing spondylitis (AS). Methods ASSERT and GO-RAISE trial data (n=635) were analysed to identify baseline predictors for various disease-state and disease-activity outcome instruments in AS. Univariate, multivariate, receiver operator characteristic and correlation analyses were performed to select final predictors. Their associations with outcomes were explored. Matrix and algorithm-based prediction models were created using logistic and linear regression, and their accuracies were compared. Numbers needed to treat were calculated to compare the effect size of anti-TNF therapy between the AS matrix subpopulations. Data from registry populations were applied to study how a daily practice AS population is distributed over the prediction model. Results Age, Bath ankylosing spondylitis functional index (BASFI) score, enthesitis, therapy, C-reactive protein (CRP) and HLA-B27 genotype were identified as predictors. Their associations with each outcome instrument varied. However, the combination of these factors enabled adequate prediction of each outcome studied. The matrix model predicted outcomes as well as algorithm-based models and enabled direct comparison of the effect size of anti-TNF treatment outcome in various subpopulations. The trial populations reflected the daily practice AS population. Conclusion Age, BASFI, enthesitis, therapy, CRP and HLA-B27 were associated with outcomes in AS. Their combined use enables adequate prediction of outcome resulting from anti-TNF and conventional therapy in various AS subpopulations. This may help guide clinicians in making treatment decisions in daily practice.

Original languageEnglish (US)
Pages (from-to)973-981
Number of pages9
JournalAnnals of the Rheumatic Diseases
Volume70
Issue number6
DOIs
StatePublished - Jun 2011

Fingerprint

Ankylosing Spondylitis
Tumor Necrosis Factor-alpha
HLA-B27 Antigen
C-Reactive Protein
Therapeutics
Baths
Population
Linear regression
Logistics
Numbers Needed To Treat
Registries
Linear Models
Decision Making
Logistic Models
Genotype

ASJC Scopus subject areas

  • Rheumatology
  • Immunology
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Allergy

Cite this

Vastesaeger, N., Van Der Heijde, D., Inman, R. D., Wang, Y., Deodhar, A. A., Hsu, B., ... Braun, J. (2011). Predicting the outcome of ankylosing spondylitis therapy. Annals of the Rheumatic Diseases, 70(6), 973-981. https://doi.org/10.1136/ard.2010.147744

Predicting the outcome of ankylosing spondylitis therapy. / Vastesaeger, Nathan; Van Der Heijde, Désirée; Inman, Robert D.; Wang, Yanxin; Deodhar, Atulya (Atul); Hsu, Benjamin; Rahman, Mahboob U.; Dijkmans, Ben; Geusens, Piet; Cruyssen, Bert Vander; Collantes, Eduardo; Sieper, Joachim; Braun, Jürgen.

In: Annals of the Rheumatic Diseases, Vol. 70, No. 6, 06.2011, p. 973-981.

Research output: Contribution to journalArticle

Vastesaeger, N, Van Der Heijde, D, Inman, RD, Wang, Y, Deodhar, AA, Hsu, B, Rahman, MU, Dijkmans, B, Geusens, P, Cruyssen, BV, Collantes, E, Sieper, J & Braun, J 2011, 'Predicting the outcome of ankylosing spondylitis therapy', Annals of the Rheumatic Diseases, vol. 70, no. 6, pp. 973-981. https://doi.org/10.1136/ard.2010.147744
Vastesaeger, Nathan ; Van Der Heijde, Désirée ; Inman, Robert D. ; Wang, Yanxin ; Deodhar, Atulya (Atul) ; Hsu, Benjamin ; Rahman, Mahboob U. ; Dijkmans, Ben ; Geusens, Piet ; Cruyssen, Bert Vander ; Collantes, Eduardo ; Sieper, Joachim ; Braun, Jürgen. / Predicting the outcome of ankylosing spondylitis therapy. In: Annals of the Rheumatic Diseases. 2011 ; Vol. 70, No. 6. pp. 973-981.
@article{e4826b766ee04ffa8f83db97b6be182f,
title = "Predicting the outcome of ankylosing spondylitis therapy",
abstract = "Objectives To create a model that provides a potential basis for candidate selection for anti-tumour necrosis factor (TNF) treatment by predicting future outcomes relative to the current disease profile of individual patients with ankylosing spondylitis (AS). Methods ASSERT and GO-RAISE trial data (n=635) were analysed to identify baseline predictors for various disease-state and disease-activity outcome instruments in AS. Univariate, multivariate, receiver operator characteristic and correlation analyses were performed to select final predictors. Their associations with outcomes were explored. Matrix and algorithm-based prediction models were created using logistic and linear regression, and their accuracies were compared. Numbers needed to treat were calculated to compare the effect size of anti-TNF therapy between the AS matrix subpopulations. Data from registry populations were applied to study how a daily practice AS population is distributed over the prediction model. Results Age, Bath ankylosing spondylitis functional index (BASFI) score, enthesitis, therapy, C-reactive protein (CRP) and HLA-B27 genotype were identified as predictors. Their associations with each outcome instrument varied. However, the combination of these factors enabled adequate prediction of each outcome studied. The matrix model predicted outcomes as well as algorithm-based models and enabled direct comparison of the effect size of anti-TNF treatment outcome in various subpopulations. The trial populations reflected the daily practice AS population. Conclusion Age, BASFI, enthesitis, therapy, CRP and HLA-B27 were associated with outcomes in AS. Their combined use enables adequate prediction of outcome resulting from anti-TNF and conventional therapy in various AS subpopulations. This may help guide clinicians in making treatment decisions in daily practice.",
author = "Nathan Vastesaeger and {Van Der Heijde}, D{\'e}sir{\'e}e and Inman, {Robert D.} and Yanxin Wang and Deodhar, {Atulya (Atul)} and Benjamin Hsu and Rahman, {Mahboob U.} and Ben Dijkmans and Piet Geusens and Cruyssen, {Bert Vander} and Eduardo Collantes and Joachim Sieper and J{\"u}rgen Braun",
year = "2011",
month = "6",
doi = "10.1136/ard.2010.147744",
language = "English (US)",
volume = "70",
pages = "973--981",
journal = "Annals of the Rheumatic Diseases",
issn = "0003-4967",
publisher = "BMJ Publishing Group",
number = "6",

}

TY - JOUR

T1 - Predicting the outcome of ankylosing spondylitis therapy

AU - Vastesaeger, Nathan

AU - Van Der Heijde, Désirée

AU - Inman, Robert D.

AU - Wang, Yanxin

AU - Deodhar, Atulya (Atul)

AU - Hsu, Benjamin

AU - Rahman, Mahboob U.

AU - Dijkmans, Ben

AU - Geusens, Piet

AU - Cruyssen, Bert Vander

AU - Collantes, Eduardo

AU - Sieper, Joachim

AU - Braun, Jürgen

PY - 2011/6

Y1 - 2011/6

N2 - Objectives To create a model that provides a potential basis for candidate selection for anti-tumour necrosis factor (TNF) treatment by predicting future outcomes relative to the current disease profile of individual patients with ankylosing spondylitis (AS). Methods ASSERT and GO-RAISE trial data (n=635) were analysed to identify baseline predictors for various disease-state and disease-activity outcome instruments in AS. Univariate, multivariate, receiver operator characteristic and correlation analyses were performed to select final predictors. Their associations with outcomes were explored. Matrix and algorithm-based prediction models were created using logistic and linear regression, and their accuracies were compared. Numbers needed to treat were calculated to compare the effect size of anti-TNF therapy between the AS matrix subpopulations. Data from registry populations were applied to study how a daily practice AS population is distributed over the prediction model. Results Age, Bath ankylosing spondylitis functional index (BASFI) score, enthesitis, therapy, C-reactive protein (CRP) and HLA-B27 genotype were identified as predictors. Their associations with each outcome instrument varied. However, the combination of these factors enabled adequate prediction of each outcome studied. The matrix model predicted outcomes as well as algorithm-based models and enabled direct comparison of the effect size of anti-TNF treatment outcome in various subpopulations. The trial populations reflected the daily practice AS population. Conclusion Age, BASFI, enthesitis, therapy, CRP and HLA-B27 were associated with outcomes in AS. Their combined use enables adequate prediction of outcome resulting from anti-TNF and conventional therapy in various AS subpopulations. This may help guide clinicians in making treatment decisions in daily practice.

AB - Objectives To create a model that provides a potential basis for candidate selection for anti-tumour necrosis factor (TNF) treatment by predicting future outcomes relative to the current disease profile of individual patients with ankylosing spondylitis (AS). Methods ASSERT and GO-RAISE trial data (n=635) were analysed to identify baseline predictors for various disease-state and disease-activity outcome instruments in AS. Univariate, multivariate, receiver operator characteristic and correlation analyses were performed to select final predictors. Their associations with outcomes were explored. Matrix and algorithm-based prediction models were created using logistic and linear regression, and their accuracies were compared. Numbers needed to treat were calculated to compare the effect size of anti-TNF therapy between the AS matrix subpopulations. Data from registry populations were applied to study how a daily practice AS population is distributed over the prediction model. Results Age, Bath ankylosing spondylitis functional index (BASFI) score, enthesitis, therapy, C-reactive protein (CRP) and HLA-B27 genotype were identified as predictors. Their associations with each outcome instrument varied. However, the combination of these factors enabled adequate prediction of each outcome studied. The matrix model predicted outcomes as well as algorithm-based models and enabled direct comparison of the effect size of anti-TNF treatment outcome in various subpopulations. The trial populations reflected the daily practice AS population. Conclusion Age, BASFI, enthesitis, therapy, CRP and HLA-B27 were associated with outcomes in AS. Their combined use enables adequate prediction of outcome resulting from anti-TNF and conventional therapy in various AS subpopulations. This may help guide clinicians in making treatment decisions in daily practice.

UR - http://www.scopus.com/inward/record.url?scp=79955816720&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79955816720&partnerID=8YFLogxK

U2 - 10.1136/ard.2010.147744

DO - 10.1136/ard.2010.147744

M3 - Article

C2 - 21402563

AN - SCOPUS:79955816720

VL - 70

SP - 973

EP - 981

JO - Annals of the Rheumatic Diseases

JF - Annals of the Rheumatic Diseases

SN - 0003-4967

IS - 6

ER -