OP0269 A COMBINED CLINICAL AND BIOMARKER ALGORITHM TO PREDICT FVC DECLINE IN SYSTEMIC SCLEROSIS ASSOCIATED INTERSTITIAL LUNG DISEASE: RESULTS FROM AN INTERNATIONAL MULTICENTRE OBSERVATIONAL COHORT
Authors: M. Hutchinson, G. Abignano, J. Blagojevic, S. L. Bosello, Y. Allanore, C. Denton, O. Distler, P. Emery, M. Matucci-Cerinic, F. Del Galdo
Journal: Annals of the Rheumatic Diseases (2021)

Background: Interstitial lung disease (ILD) is the leading cause of mortality in patients with Systemic Sclerosis (SSc). Forced Vital Capacity (FVC) is a major indicator of severity in SSc ILD. The ELF serum test and its constituent biomarkers (HA, PIIINP and TIMP-1) have shown to correlate with FVC in two large, independent multicentre cohorts of 457 patients, but also showed a correlation with age. Objectives: Here we aimed to investigate the relationship of the ELF biomarkers and age in a large population of healthy controls and to identify a combined clinical and biomarkers model to stratify for risk of ILD progression in a multicentre longitudinal cohort of patients with SSc. Methods: ELF score was measured in sera from 925 healthy controls in one centre and 869 longitudinal samples from 254 SSc patients from 6 centres across 4 European countries. Clinical data were recorded according to EUSTAR Minimal Essential dataset. FVC% change over time was estimated by Mixed-effects modelling. Patients were then divided in two groups: progressors, with a %FVC drop &gt; 3%/year (according to published MCID) and a group of patients with stable or improving FVC. Lasso penalised regression was carried out with biomarkers and the available clinical and demographic variables at patient’s first visit as potential predictors. The resulting linear predictor was used to derive two thresholds, one for optimal sensitivity (rule-out) and one for optimal specificity (rule-in). Patients within thresholds were further selected according to the ratio of TIMP-1: PIIINP (Figure 1). Results: HA was the only ELF biomarker that correlated significantly with age in the healthy control cohort. Therefore, we defined by linear regression a “residual HA” which accounted for age. TIMP1, PIIINP and residual HA were then considered as distinct biomarkers in the analysis of the SSc cohort. 189 SSc patients with 785 time-points had complete datasets and were included in the analysis. Median follow up was 33 months (IQR 18-48). One-hundred and forty patients (74%) were classified as non progressors, 94 (50%) with no change or improving FVC and 46 (24%) with FVC drop &lt;3% year. 49 patients (26%) were classed as progressors (drop of 3%/year or more in FVC, median slope -4.7%/year). Variable selection via Lasso penalised logistic regression resulted in a model with a c-index of 0.69 (95% CI: 0.60-0.78)) and contained age, disease duration (from first non-Raynaud’s symptom), residual HA, anti-centromere antibodies (ACA) status, previous diagnosis of ILD, joint synovitis and history of protein pump inhibitor use. A two-step process was developed using the linear predictor from the model and the ratio of TIMP-1 and PIIINP (Figure 1). The stratification tool increased by nearly two-fold the ability to predict progressors in any 12 months interval (46 to 49% predictive value vs 26% probability) identifying an 82 to 91% negative predictive value for progression. Conclusion: Building on the face and content validity of the biomarkers included in the ELF score, here we identify an easy to assess combined clinical and biomarker model to stratify patients for their risk of ILD progression. Despite its derivation from a large multicentre cohort, independent validation will determine the clinical value of Scleroscore as a stratification tool for risk of progression of SSc ILD. Disclosure of Interests: Michelle Hutchinson: None declared, Giuseppina Abignano: None declared, Jelena Blagojevic: None declared, Silvia Laura Bosello: None declared, Yannick Allanore Grant/research support from: Alpine, Boehringer Ingelheim, Genentech/Roche, Medsenic, and Sanofi, Christopher Denton Consultant of: Corbus, Actelion, GlaxoSmithKline, Bayer, Sanofi, Galapagos, Inventiva, Boehringer Ingelheim, Roche, CSL Behring, Acceleron, Horizon, Arxx Therapeutics, Grant/research support from: Corbus, Actelion, GlaxoSmithKline, Bayer, Sanofi, Galapagos, Inventiva, Boehringer Ingelheim, Roche, CSL Behring, Acceleron, Horizon, Arxx Therapeutics, Oliver Distler Consultant of: Abbvie, Acceleron Pharma, Amgen, AnaMar, Arxx Therapeutics, Baecon Discovery, Blade Therapeutics, Bayer, Boehringer Ingelheim, ChemomAb, Corbus Pharmaceuticals, CSL Behring, Galapagos NV, Glenmark Pharmaceuticals, GSK, Horizon (Curzion) Pharmaceuticals, Inventiva, iQvia, Italfarmaco, iQone, Kymera Therapeutics, Lilly, Medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Novartis, Pfizer, Roche, Sanofi, Serodapharm, Topadur, Target Bioscience and UCB, Grant/research support from: Abbvie, Acceleron Pharma, Amgen, AnaMar, Arxx Therapeutics, Baecon Discovery, Blade Therapeutics, Bayer, Boehringer Ingelheim, ChemomAb, Corbus Pharmaceuticals, CSL Behring, Galapagos NV, Glenmark Pharmaceuticals, GSK, Horizon (Curzion) Pharmaceuticals, Inventiva, iQvia, Italfarmaco, iQone, Kymera Therapeutics, Lilly, Medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Novartis, Pfizer, Roche, Sanofi, Serodapharm, Topadur, Target Bioscience and UCB, Paul Emery Consultant of: Lilly, Abbvie, Roche, Grant/research support from: Lilly, Marco Matucci-Cerinic Consultant of: Chemomab, Lilly, Abbvie, Actelion, Francesco Del Galdo Speakers bureau: Astra-Zeneca, Boehringer Ingelheim, Actelion, Consultant of: Astra-Zeneca, Mitsubishi-Tanabe, Capella Biosciences, Chemomab, Actelion, Boehringer-Ingelheim, Grant/research support from: Capella Biosciences, Chemomab, Kymab, Mitsubishi-Tanabe