Evidence map›Paper›PMID 21402563›Full record

Trial reportAnnals of the rheumatic diseases2011

Predicting the outcome of ankylosing spondylitis therapy.

Nathan Vastesaeger, Désirée van der Heijde, Robert D Inman, Yanxin Wang, Atul Deodhar, Benjamin Hsu, Mahboob U Rahman, Ben Dijkmans, Piet Geusens, Bert Vander Cruyssen and 3 more

Erratum issuedOpen access · bronzeAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Annals of the rheumatic diseases, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 78 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
78citing papers in PubMed, 4 pooled it
15.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

78 citing papers in PubMed, 4 syntheses or guidelines pooled it, 189 citations in OpenAlex.

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  7. Efficacy and Safety of Intravenous Golimumab in Ankylosing Spondylitis Patients With Early and Late Disease Through One Year of the GO-ALIVE Study.Journal of clinical rheumatology : practical reports on rheumatic & musculoskeletal diseases · 2022
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18 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

  • Erratum issued
5 · Who and what money

Authors and funding

13 authors at 12 institutions in 7 countries.

Nathan VastesaegerSchering-Plough Corporation, Kenilworth, New Jersey, USA. nathan.vastesaeger@merck.com
Désirée van der Heijde
Robert D Inman
Yanxin Wang
Atul Deodhar
Benjamin Hsu
Mahboob U Rahman
Ben Dijkmans
Piet Geusens
Bert Vander Cruyssen
Eduardo Collantes
Joachim Sieper
Jürgen Braun
Arthritis and Rheumatic Disease Specialties · USCharité - Universitätsmedizin Berlin · DEGhent University Hospital · BEHasselt University · BEHospital Universitario Reina Sofía · ESLeiden University · NLMerck & Co., Inc., Rahway, NJ, USA (United States) · USMerck (Japan) · JPPfizer (United States) · USRheumazentrum Ruhrgebiet · DEUniversity of Toronto · CAVrije Universiteit Amsterdam · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo 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).

methodsASSERT 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.

resultsAge, 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.

conclusionAge, 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.

Indexed as

AdultAge FactorsAlgorithmsAntirheumatic AgentsBiomarkersC-Reactive ProteinEpidemiologic MethodsFemaleGenetic Predisposition to DiseaseGenotypeHLA-B27 AntigenHumansMaleMiddle AgedPatient SelectionPrognosisAntirheumatic AgentsBiomarkersC-Reactive ProteinHLA-B27 AntigenTumor Necrosis Factor-alpha

Identifiers

PMID21402563
PMCPMC3086037
OpenAlexW2138889175

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.