POS0388 DEVELOPING A SCREENING TOOL FOR THE DETECTION OF INTERSTITIAL LUNG DISEASE IN SYSTEMIC SCLEROSIS: THE ILD-RISC RISK SCORE

Abstract

BackgroundHigh resolution computed tomography (HRCT) is the gold standard for the diagnosis of systemic sclerosis associated interstitial lung disease (SSc-ILD). Although there is agreement in performing HRCT as a screening test at time of SSc diagnosis, some physicians do not regularly perform baseline HRCTs. In addition, it is unclear according to which criteria HRCTs should be repeated during the follow-up of baseline ILD negative patients.ObjectivesTo develop a risk score for the presence of SSc-ILD (the ILD-RISC), to guide physicians in ordering both baseline and follow-up HRCTs.MethodsThe steering board included six SSc-ILD experts from referral centers, two fellows and a patient research partner. Items for regression analysis were selected according to face validity, feasibility, scientific background, and personal experience using the nominal group technique (NGT). The prediction model for the presence of ILD was developed from baseline visits of SSc patients from the six centers using multivariable logistic regression with backward selection. Patients were randomly divided into a derivation and validation cohort consisting of 66% and 34% of patients respectively. Patients with missing data in the selected covariates and in the ILD status (outcome) were excluded. After identifying a cut-off favoring sensitivity >85% from the ROC curve analysis, the derived ILD-RISC score was applied first in the validation cohort and then longitudinally in a cohort of SSc patients with negative baseline HRCT.ResultsThe steering board selected 13 variables deemed important in the identification of SSc-ILD: sex, age, disease duration from first non-Raynaud’s phenomenon symptom, skin subset (diffuse/limited), presence of esophageal symptoms, digital ulcers (DU) ever, arthritis ever, smoking ever, increased inflammatory markers, NYHA functional class, SSc autoantibody status (SSc_Atb), FVC% and DLCO%. Among 780/3240 patients fulfilling the inclusion criteria, 533 (43% ILD) and 247 (48% ILD) respectively constituted the derivation and the validation cohort. In the derivation cohort, a model including FVC%, DLCO%, DU ever, age and SSc_Atb (Table 1A) showed an OR of 133.9 (95% CI 53.4-335.9) and an AUC of 79.1% (95% CI 75.3-83.0%) for the presence of ILD on HRCT (Figure 1). An ILD-RISC score ≥0.3 showed sensitivity of 85.6% and specificity of 53.6%, NPV of 83.2% and PPV of 58.2%, which were replicated in the validation cohort (Table 1B). Among 819 patients with negative baseline HRCT, 170 (20.8%) developed ILD during a 3.8±3.0 years follow up (1988 visits). Longitudinally, the ILD-RISC score showed comparable sensitivity and specificity (Table 1B).Table 1.A)ILD-RISC MODEL VARIABLESOR95% CIp valueDigital ulcers, ever2.0581.347-3.145<0.001Age1.0261.010-1.0420.001SSc_ATBAnti-centromere0.3340.198-0.563<0.001Anti-topoisomerase I2.3791.326-4.2670.004Anti-RNA-polymerase III1.4070.636-3.1130.399Anti-Pm/Scl2.5560.916-7.1350.073None of the aboveComparatorFVC%0.9900.979-1.0020.091DLCO%0.9710.960-0.982<0.001B)ILD-RISC SCORE PERFORMANCEDerivation cohortValidation cohortLongitudinal cohortAll Patients/ILD patients533/229247/119819/170AUC %, 95% CI79.1 (75.3 – 83.0)76.4 (71.0 – 82.7)72.6 (68.9 – 76.2)Sensitivity %, 95% CI85.6 (80.4 – 89.9)85.7 (78.1 – 91.5)80.4 (73.9 – 86.0)Specificity %, 95% CI53.6 (47.8 – 59.3)49.2 (40.3 – 58.2)50.5 (48.2 – 52.9)Negative Predictive Value %, 95% CI83.2 (77.2 – 88.1)78.8 (68.2 – 87.1)96.3 (94.9 – 97.4)Positive Predictive Value %, 95% CI58.2 (52.7 – 63.5)61.1 (53.2 – 68.5)13.9 (11.8 – 16.1)ConclusionWe developed and validated the ILD-RISC score to predict the presence of ILD at time of diagnosis and evaluated its performance during follow-up. The ILD-RISC may be useful in routine practice when resources for HRCTs might be limited. In particular, it may also help to decide when to order HRCTs at follow up, thus limiting unnecessary HRCTs and reducing the burden for patients and institutions.Disclosure of InterestsCosimo Bruni Speakers bureau: Actelion, Consultant of: Boehringer-Ingelheim, Eli-Lilly, Grant/research support from: New Horizon fellowship, FOREUM, EUSTAR, GILS, Lorenzo Tofani: None declared, Håvard Fretheim: None declared, Sophie Liem: None declared, Arthiha Velauthapillai: None declared, Hilde Jenssen Bjørkekjær: None declared, Imon Barua: None declared, Ilaria Galetti: None declared, Alexandru Garaiman: None declared, Mike O. Becker Speakers bureau: Mepha, MSD, Novartis, GSK, Bayer and Vifor, Consultant of: Mepha, MSD, Novartis, GSK, Bayer and Vifor, Grant/research support from: Mepha, MSD, Novartis, GSK, Bayer and Vifor, Anna-Maria Hoffmann-Vold Consultant of: Actelion, ARXX therapeutics, Bayer, Janssen, MSD, Lilly, Roche, Boehringer-Ingelheim, Medscape., Jeska de Vries-Bouwstra Speakers bureau: Payment for presentations and educational events by Boehringer Ingelheim and Janssen, Consultant of: Janssen and Boehringer Ingelheim, Abbvie, Grant/research support from: Roche, Galapagos and Janssen, ZonMW and ReumaNederland, Madelon Vonk: None declared, Jörg H.W. Distler Shareholder of: J.H.W.D. is stock owner of 4D Science and Scientific head of FibroCure., Grant/research support from: J.H.W.D. has received research funding from Anamar, Active Biotech, Array Biopharma, aTyr, BMS, Bayer Pharma, Boehringer Ingelheim, Celgene, Galapagos, GSK, Inventiva, Novartis, Sanofi-Aventis, RedX, UCB., Marco Matucci-Cerinic Speakers bureau: Biogen, Bayer, Boehringer-Ingelheim, CSL Behring, Eli-Lilly., Consultant of: Actelion, Biogen, Bayer, Boehringer-Ingelheim, CSL Behring, Eli-Lilly., Grant/research support from: Actelion, Oliver Distler Speakers bureau: Bayer, Boehringer Ingelheim, Janssen, Medscape, Consultant of: Abbvie, Acceleron, Alcimed, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, Corbus, CSL Behring, 4P Science, Galapagos, Glenmark, Horizon, Inventiva, Kymera, Lupin, Miltenyi Biotec, Mitsubishi Tanabe, MSD, Novartis, Prometheus, Roivant, Sanofi and Topadur, Grant/research support from: Kymera, Mitsubishi Tanabe, Boehringer Ingelheim