{
  "abstract": "Introduction Systemic sclerosis (SSc) is a clinically heterogeneous disease with variable organ involvement and outcomes. Reliable biomarkers are needed to improve risk management. Persistent systemic inflammation, captured by longitudinal C-reactive protein (CRP) measurements, may help predict disease course. Integrating biomarker-defined phenotypes with machine learning could enhance clinical stratification. We aim to examine the relationship between CRP-defined inflammatory phenotypes and machine learning–derived clusters, and to evaluate associations with organ involvement, lung function decline, and survival.Material and Methods Patients from the EUSTAR registry with at least 3 visits including CRP were analysed. Inflammatory phenotype was classified over 24 ± 6 months as: non-inflammatory (<5 mg/L), low-grade (5–9.9 mg/L), intermediate-grade (10–14.9 mg/L), or high-grade (15 mg/L or more). Baseline characteristics were compared using Chi 2/ANOVA. Survival was assessed with Kaplan–Meier and multivariable Cox models adjusting for demographics, disease duration, cutaneous subset, and organ involvement. Longitudinal %pFVC and %pDLCO were modelled with mixed-effects models. Unsupervised k-means clustering (k=4) on baseline %pFVC, %pDLCO, modified Rodnan skin score, and organ involvement identified data-driven groups.Results Among 4,474 patients, 3,019 (67.5%) were non-inflammatory, 377 (8.4%) low-grade, 369 (8.2%) intermediate, and 709 (15.8%) high-grade. Ten-year survival was 82.4% in non-inflammatory, 71.1% in low-grade, 69.5% in intermediate, and 63.8% in high-grade groups (p<0.001). In adjusted models, intermediate/high grades were associated with increased risk of mRSS progression (HR 1.9, 95% CI 1.4–2.5), digital ulcers (HR 2.3, 1.7–3.1), right bundle branch block (HR 1.8, 1.2–2.7), auricular arrhythmias (HR 1.7, 1.2–2.5), ILD (HR 1.6, 1.2–2.1), %pFVC decline of at least 10 (HR 1.5, 1.1–2.0), and %pDLCO decline of at least 15 (HR 1.7, 1.3–2.2) at 12 months. Intermediate/high grades were also linked to composite endpoints of any heart (IRR 1.9, 1.5–2.5) and any organ involvement (IRR 2.0, 1.6–2.6). K-means clustering identified four groups: C0 (preserved lung, low mRSS, minimal organ involvement, enriched for non-inflammatory); C1 (mild lung decline, moderate mRSS, more GI involvement, enriched for intermediate); C2 (intermediate measures, balanced phenotypes); and C3 (markedly reduced %pFVC and %pDLCO, a high prevalence of ILD, and greater gastrointestinal involvement and was enriched for high-grade).Conclusions Persistently elevated CRP in SSc, particularly at moderate/high levels, defines a phenotype with greater multi-organ involvement and worse survival. CRP-based phenotyping provides a pragmatic prognostic tool that complements machine learning clustering. These findings support closer monitoring and tailored management of inflammatory phenotypes and may inform future trial design and stratification.Abstract OC.39 Figure 1Kaplan-Meier survival curve according to inflammatory phenotypeAbstract OC.39 Figure 2Forest plot of adjusted hazard ratios for organ involvement and persistent intermediate-high inflammatory phenotypeAbstract OC.39 Figure 3Cluster profile heatmap for k-means-derived subgroups in systemic sclerosis (k-4)",
  "authors": [
    {
      "affiliations": [
        "Centre for Musculoskeletal Research, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester, UK"
      ],
      "name": "Cristiana Sieiro Santos"
    },
    {
      "affiliations": [
        "Department of Allergy, Immunology, Rheumatology and Rare Diseases Ospedale San Raffaele, Milan, Italy"
      ],
      "name": "Corrado Campochiaro"
    },
    {
      "affiliations": [
        "Department of Rheumatology Hospital Universit,ario Dr. Peset, Valencia, Spain"
      ],
      "name": "Juan Jose Alegre Sancho"
    },
    {
      "affiliations": [
        "Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy"
      ],
      "name": "Silvia Bellando Randone"
    },
    {
      "affiliations": [
        "Rheumatology Department, Hospital de Santa Maria, U.L.S. Santa Maria, Lisbon, Portugal"
      ],
      "name": "Goncalo Boleto"
    },
    {
      "affiliations": [
        "Department of Rheumatology. Hospital Universitari de la Santa Creu i Sant Pau. Barcelona, Spain"
      ],
      "name": "Ivan Castellvi"
    },
    {
      "affiliations": [
        "Laboratory of Experimental Rheumatology and Division of Rheumatology, University of Genoa, Genova, Italy"
      ],
      "name": "Maurizio Cutolo"
    },
    {
      "affiliations": [
        "Raynaud’s and Scleroderma Programme, National Institute for Health Research Biomedical Research Centre, Leeds, UK"
      ],
      "name": "Francesco Del Galdo"
    },
    {
      "affiliations": [
        "Schön Klinik Hamburg Eilbek, Hamburg, Germany"
      ],
      "name": "Ivan Foeldvari"
    },
    {
      "affiliations": [
        "Department of Medicine, Keio University School of Medicine, Tokyo, Japan"
      ],
      "name": "Masataka Kuwana"
    },
    {
      "affiliations": [
        "Department of Medicine E, Meir Hospital, Kfar Saba, Israel Sackler School of Medicine, University of Tel Aviv, Tel Aviv, Israel"
      ],
      "name": "Yair Levy"
    },
    {
      "affiliations": [
        "Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy"
      ],
      "name": "Marco Matucci Cerinic"
    },
    {
      "affiliations": [
        "Department of Clinical and Molecular Sciences, Marche Polytechnic University, Ancona, Italy"
      ],
      "name": "Gianluca Moroncini"
    },
    {
      "affiliations": [
        "Department of Internal Medicine and Department of Rheumatology, Ghent University, Ghent, Belgium"
      ],
      "name": "Vanessa Smith"
    },
    {
      "affiliations": [
        "Department of Rheumatology, National Reference Center for Systemic Autoimmune Rare Diseases, Bordeaux University Hospita, Bordeaux, France"
      ],
      "name": "Marie-Elise Truchetet"
    },
    {
      "affiliations": [
        "Department of Rheumatology, Basle University, Felix Platter Spital, Basle, Switzerland"
      ],
      "name": "Ua Walker"
    },
    {
      "affiliations": [
        "Division of Rheumatology, Hospital Cochin, Paris, France"
      ],
      "name": "Yannick Allanore"
    }
  ],
  "title": "OC.39 Machine learning–driven phenotyping in systemic sclerosis: CRP-defined inflammation and clinical clusters—an EUSTAR registry analysis",
  "uid": "79bbea6d-e1db-5bba-91e2-8d2ce6ebcc22"
}