Patterns and predictors of skin score change in early diffuse systemic sclerosis from the European Scleroderma Observational Study

Abstract

Objectives: Our aim was to use the opportunity provided by the European Scleroderma Observational Study to (1) identify and describe those patients with early diffuse cutaneous systemic sclerosis (dcSSc) with progressive skin thickness, and (2) derive prediction models for progression over 12 months, to inform future randomised controlled trials (RCTs).

Methods: The modified Rodnan skin score (mRSS) was recorded every 3 months in 326 patients. 'Progressors' were defined as those experiencing a 5-unit and 25% increase in mRSS score over 12 months (±3 months). Logistic models were fitted to predict progression and, using receiver operating characteristic (ROC) curves, were compared on the basis of the area under curve (AUC), accuracy and positive predictive value (PPV).

Results: 66 patients (22.5%) progressed, 227 (77.5%) did not (33 could not have their status assessed due to insufficient data). Progressors had shorter disease duration (median 8.1 vs 12.6 months, P=0.001) and lower mRSS (median 19 vs 21 units, P=0.030) than non-progressors. Skin score was highest, and peaked earliest, in the anti-RNA polymerase III (Pol3+) subgroup (n=50). A first predictive model (including mRSS, duration of skin thickening and their interaction) had an accuracy of 60.9%, AUC of 0.666 and PPV of 33.8%. By adding a variable for Pol3 positivity, the model reached an accuracy of 71%, AUC of 0.711 and PPV of 41%.

Conclusions: Two prediction models for progressive skin thickening were derived, for use both in clinical practice and for cohort enrichment in RCTs. These models will inform recruitment into the many clinical trials of dcSSc projected for the coming years.

Trial registration number: NCT02339441.

Keywords: autoantibodies; outcomes research; systemic sclerosis.

Conflict of interest statement

Competing interests: ALH has done consultancy work for Actelion, served on a Data Safety Monitoring Board for Apricus, received research funding and speaker’s fees from Actelion, and speaker’s fees from GSK. JHWD has consultancy relationships and/or has received research funding from Actelion, BMS, Celgene, Bayer Pharma, Boehringer Ingelheim, JB Therapeutics, Sanofi-Aventis, Novartis, UCB, GSK, Array BioPharma, Active Biotech, Galapagos, Inventiva, Medac, Pfizer, Anamar and RuiYi, and is stock owner of 4D Science. OD has received consultancy fees from 4D Science, Actelion, Active Biotech, Bayer, Biogenidec, BMS, Boehringer Ingelheim, EpiPharm, Ergonex, espeRare Foundation, Genentech/Roche, GSK, Inventiva, Lilly, Medac, Medimmune, Pharmacyclics, Pfizer, Serodapharm, Sinoxa and UCB, and received research grants from Actelion, Bayer, Boehringer Ingelheim, Ergonex, Pfizer and Sanofi, and has a patent mir-29 for the treatment of systemic sclerosis licensed. WJG has received teaching fees from Pfizer. CA has served as a consultant for AbbVie, Pfizer, Roche, UCB, MSD, BMS and Novartis, and has received research funding and speaker fees from AbbVie, Pfizer, Roche, UCB, MSD, BMS and Novartis. FCH has received research funding from Actelion. MEA has undertaken advisory board work and received honoraria from Actelion, and received speaker’s fees from Bristol-Myers Squibb. NSD has done consultancy for AbbVie, Pfizer, Roche and MSD, and received speaker’s fees from AbbVie, Boehringer-Ingelheim, Pfizer, Richter Gedeon, Roche and MSD. HG has done consultancy work and received honoraria from Actelion. UM-L is funded in part by EUSTAR, EULAR and the European Community (Desscipher programme). JMvL has received honoraria from Eli Lilly, Pfizer, Roche, MSD and BMS. SP has received research grants from Actelion Pharmaceuticals Australia, Bayer, GlaxoSmithKline Australia and Pfizer, and speaker fees from Actelion. AR receives funding from AstraZeneca. CPD has done consultancy for GSK, Actelion, Bayer, Inventiva and Merck-Serono, received research grant funding from GSK, Actelion, CSL Behring and Inventiva, received speaker’s fees from Bayer and given trial advice to Merck-Serono.

© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Figures

Figure 1

Characteristics of mRSS progression. The five histograms describe modified Rodnan skin score (mRSS) progression for all patients whose skin score during the study ever increases beyond their baseline level (n=160) and for those whose progression satisfies the 5-unit and 25% increase rule during the first 12 months (±3 months) (n=66). Here, histograms summarise the distribution of changes between baseline and peak mRSS, the mRSS value at its peak, the time elapsed between the onset of skin thickening and the recorded peak, the rate of mRSS increase per month between baseline and peak, and the time elapsed between baseline and the recorded peak. The rate of mRSS progression (in units/month) was computed by specifying individual simple linear regressions of mRSS according to time, between baseline and peak.

Figure 2

ROC of three selected models. Three ROC curves summarise the predictive power of three different models by plotting sensitivity with respect to 100-specificity. For each model/ROC curve, there is an optimal point (the one closest to the top-left corner) that corresponds to a threshold of predicted probability of progression. For each model, patients with a predicted probability above that threshold are predicted to progress. AUC, area under curve; mRSS, modified Rodnan skin score; Pol3, anti-RNA polymerase III; PPV, positive predictive value; ROC, receiver operating characteristic.

Figure 3

Rules for selecting progressive patients according to two selected models. According to each model, in order to select progressive patients, they should be selected from the area under each relevant curve. These curves are superposed over a plot of the baseline mRSS of patients with respect to their duration of skin thickening, with progressors (of at least 5 units and 25%) being highlighted. Notes to the figure: (1) Analysing patients separately according to all autoantibody groups (TOPO, Pol3, ACA, ‘no autoantibodies’) was avoided because of the small number of ACA+ patients who could be included (n=16). Another possible approach was the inclusion of two indicator variables: one for Pol3+ and another for TOPO+, meaning that ACA+ and ‘no autoantibody’ patients formed the reference group, for which the resulting model proved too restrictive: only 2 out of 75 patients in the reference group were predicted to progress. Another reason for considering Pol3 patients separately was that they were suspected from preliminary analysis to be the most clinically different group, and stratifying by Pol3 status produced a higher AUC than doing so by TOPO status. (2) Each prediction model is based on a logistic regression model, where the outcome for patient i is Yi=progression and X are a selection of covariates. Using ROC curve analysis, each model has an optimal p∗ for which, if Pr^(Yi=1|X)>p∗, the patient is predicted to progress. Each frontier in the graphs above corresponds to the combination of mRSS and disease duration points, for which Pr^(Yi=1|X)=p∗ in the domains where both predictors are defined. Therefore, if a patient is in the area under the relevant curve, she/he is predicted to progress according to the model. ACA, anticentromere; AUC, area under curve; mRSS, modified Rodnan skin score; Pol3, anti-RNA polymerase III; ROC, receiver operating characteristic; TOPO, topoisomerase.