Evidence map›Paper›PMID 31227488›Full record

ArticleAnnals of the rheumatic diseases2019

Predictors of disease worsening defined by progression of organ damage in diffuse systemic sclerosis: a European Scleroderma Trials and Research (EUSTAR) analysis.

Mike Becker, Nicole Graf, Rafael Sauter, Yannick Allanore, John Curram, Christopher P Denton, Dinesh Khanna, Marco Matucci-Cerinic, Janethe de Oliveira Pena, Janet E Pope and 3 more

Open access · hybridAbstract readMulticenter Study
In one paragraph

Article in Annals of the rheumatic diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 3 pooled it
5.1field-weighted citation impact, top 4% 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

31 citing papers in PubMed, 3 syntheses or guidelines pooled it, 60 citations in OpenAlex.

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  6. Are we ready to predict progressive disease in juvenile systemic sclerosis?Journal of scleroderma and related disorders · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 8 institutions in 7 countries.

Mike BeckerDepartment of Rheumatology and the Centre of Experimental Rheumatology, University Hospital Zurich, Zurich, Switzerland.
Nicole GrafGraf Biostatistics, Winterthur, Switzerland.
Rafael SauterBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Yannick AllanoreRheumatology A Department, Paris Descartes University, Sorbonne Paris Cité, Cochin Hospital, Paris, France.
John CurramData Science and Analytics, Bayer plc, Reading, UK.
Christopher P DentonUCL Division of Medicine, Royal Free Campus, London, UK.
Dinesh KhannaDivision of Rheumatology, Department of Internal Medicine, University of Michigan Scleroderma Program, University of Michigan, Ann Arbor, Michigan, USA.
Marco Matucci-CerinicDepartment of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
Janethe de Oliveira PenaBayer US LLC, Whippany, New Jersey, USA.
Janet E PopeDepartment of Medicine, Division of Rheumatology, University of Western Ontario, St. Joseph's Health Care, London, Ontario, Canada.ORCID 0000-0003-1479-5302
Oliver DistlerDepartment of Rheumatology and the Centre of Experimental Rheumatology, University Hospital Zurich, Zurich, Switzerland oliver.distler@usz.ch.ORCID 0000-0002-0546-8310
EUSTAR Collaborators
EUSTAR Collaborators (numerical order of centres)
Bayer (United States) · USUniversity Hospital of Zurich · CHDélégation Paris 5 · FRThe Royal Free Hospital · GBUniversity of Florence · ITUniversity of Michigan · USUniversity of Oxford · GBWestern University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesMortality and worsening of organ function are desirable endpoints for clinical trials in systemic sclerosis (SSc). The aim of this study was to identify factors that allow enrichment of patients with these endpoints, in a population of patients from the European Scleroderma Trials and Research group database.

methodsInclusion criteria were diagnosis of diffuse SSc and follow-up over 12±3 months. Disease worsening/organ progression was fulfilled if any of the following events occurred: new renal crisis; decrease of lung or heart function; new echocardiography-suspected pulmonary hypertension or death. In total, 42 clinical parameters were chosen as predictors for the analysis by using (1) imputation of missing data on the basis of multivariate imputation and (2) least absolute shrinkage and selection operator regression.

resultsOf 1451 patients meeting the inclusion criteria, 706 had complete data on outcome parameters and were included in the analysis. Of the 42 outcome predictors, eight remained in the final regression model. There was substantial evidence for a strong association between disease progression and age, active digital ulcer (DU), lung fibrosis, muscle weakness and elevated C-reactive protein (CRP) level. Active DU, CRP elevation, lung fibrosis and muscle weakness were also associated with a significantly shorter time to disease progression. A bootstrap validation step with 10 000 repetitions successfully validated the model.

conclusionsThe use of the predictive factors presented here could enable cohort enrichment with patients at risk for overall disease worsening in SSc clinical trials.

Indexed as

Clinical Trials as TopicDisease ProgressionEpidemiologic MethodsEuropeFemaleFollow-Up StudiesHeart DiseasesHumansLung DiseasesMaleMiddle AgedMorbidityPrognosisProspective StudiesScleroderma, DiffuseScleroderma, Systemicdisease worseningmortalitypredictive factorssystemic sclerosis

Identifiers

PMID31227488
PMCPMC6788922
OpenAlexW2951415329

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.