The Classification Criteria for Anti-Synthetase Syndrome (Class) Project

Giovanni Zanframundo¹, Sara Faghihi Kashani², Akira Yoshida³, Eduardo Dourado⁴, Iazsmin Bauer Ventura⁵, Daphne Rivero Gallegos⁶, Aravinthan Loganathan⁷, Gianluca Sambataro⁸, Francisca Bozan⁹, Sangmee Bae¹⁰, Darosa Lim¹¹, Marta Cheli¹², Yasuhiko Yamano¹³, Francesco Bonella¹⁴, Tamera Corte¹⁵, Tracy J. Doyle¹⁶, David Fiorentino¹⁷, Miguel Gonzalez-Gay¹⁸, Marie Hudson¹⁹, Masataka Kuwana²⁰, Ingrid Lundberg²¹, Andrew Mammen²², Neil McHugh²³, Frederick Miller²⁴, Carlomaurizio Montecucco¹, Chester Oddis²⁵, Jorge Rojas-Serrano⁶, Jens Schmidt²⁶, Carlo Alberto Scirè²⁷, Albert Selva-O'Callaghan²⁸, Victoria Werth¹¹, Davide Rozza²⁹, Monica Morosini³⁰, Sara Bozzini³⁰, Lorenzo Cavagna¹, Rohit Aggarwal²⁵ and the CLASS project investigators, ¹Università di Pavia, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy, ²University of Pittsburgh School of Medicine, Pittsburgh, PA, USA and University of California, San Francisco, CA, ³Nippon Medical School Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan, ⁴Unidade Local de Saùde da Regiao de Aveiro and Aveiro Rheumatology Research Centre, Aveiro, Portugal; Universidade de Lisboa, Lisboa, Portugal, ⁵University of Chicago, Chicago, IL, ⁶Instituto Nacional de Enfermedades Respiratorias, Ismael Cosío Villegas, Mexico City, Mexico, ⁷Royal National Hospital for Rheumatic Diseases and University of Bath, UK; Arthritis Australia, Broadway, Glebe, NSW, Australia, ⁸University of Enna "Kore", Enna, Italy, ⁹Hospital Clinico Universidad de Chile, Santiago de Chile, Chile, ¹⁰UCLA, Los Angeles, CA, ¹¹University of Pennsylvania, Philadelphia, PA, ¹²Fondazione IRCCS Istituto Neurologico C. Besta, Milano, Italy, ¹³Tosei General Hospital, Aichi, Japan, ¹⁴University of Duisburg-Essen, Essen, Germany, ¹⁵Royal Prince Alfred Hospital and University of Sydney, Sydney, Australia, ¹⁶Brigham and Women's Hospital, Boston, MA, ¹⁷Stanford University, Redwood City, CA, ¹⁸IIS-Fundación Jiménez Díaz, Madrid, Spain, ¹⁹McGill University, Montreal, QC, Canada, ²⁰Nippon Medical School Graduate School of Medicine, Tokyo, Japan, ²¹Karolinska Institutet, Stockholm, Sweden, ²²NIH, Bethesda, MD, ²³University of Bath, Bath, United Kingdom, ²⁴NIH, NIEHS, Durham, NC, ²⁵University of Pittsburgh, Pittsburgh, PA, ²⁶University Medical Center Goettingen, Goettingen, Germany, ²⁷Univeristy of Milano Bicocca, Milan, Italy and IRCCS San Gerardo dei Tintori Foundation, Monza, Italy, ²⁸Hospital Universitari Vall d'Hebron, Barcelona, Spain, ²⁹Italian Society for Rheumatology, Milano, Italy, ³⁰Fondazione IRCCS Policlinico San Matteo Hospital of Pavia, Pavia, Italy

Meeting: ACR Convergence 2024

Date of first publication: October 24, 2024

Keywords: classification criteria, Late-Breaking 2024, Myositis, pulmonary

Session Information

Date: Monday, November 18, 2024

Title: (L01–L14) Late-Breaking Posters

Session Type: Poster Session C

Session Time: 10:30AM-12:30PM

Background/Purpose: The aim of this study was to develop and validate new classification criteria for Anti-Synthetase Syndrome (ASSD) using data and consensus driven methodologies and global participation.

Methods: A literature review identified features used to define ASSD and develop initial variable list, which was used for data collection of cases and controls.

Two distinct but complementary approaches were adopted to develop new candidate criteria: data-driven analysis of patient information and multi-criteria decision analysis (MCDA) under steering committee (SC) involving expert rheumatologists, dermatologists, neurologists, and pulmonologists worldwide.

Data on ASSD cases and controls (mimickers of ASSD) were collected through RedCap database. Patient information included demographics, symptoms, laboratory tests, and anti-synthetase antibody results. A preliminary univariate analysis was conducted to inform a multivariate analysis resulting in the data-driven candidate model using derivation dataset followed by internal validation. The MCDA method involved ranking and scoring the importance of different clinical features for ASSD classification, resulting in weighted score-based criteria followed by internal validation on clinical vignette.

Patients’ serum was tested for anti-synthetase antibodies using a central gold standard method to develop a Gold standard dataset for validation. Cases and controls not used for derivation developed an independent validation cohort.

The experts selected the best model through a Consensus Conference performed with Nominal Group Technique.

Results: CLASS project database included 120 variables collected on cases (N=1952) and controls (N=2097), with 19% of patients centrally tested for antibodies. A SC comprising of 20 prominent myositis experts, identified one and three candidate models from the data-driven and the MCDA approaches, respectively.

After discussion and refinement, consensus was developed on a final set of criteria based on its face validity, ease of use, feasibility and performance in both MCDA-based clinical vignettes and real-world data (Fig. 1).

The final criteria define two levels of classification: Probable and Definite ASSD. Both levels require the presence of at least one core clinical feature (arthritis, myositis, or lung disease) and assign points based on additional features and anti-synthetase antibody status. In addition, definite ASSD requires confirmation by anti-synthetase antibody testing. The criteria for Definite ASSD displayed a sensitivity/specificity of 86.8/99.2% on the independent validation dataset and 94.3/99.7% on the gold standard dataset. The criteria for Probable ASSD displayed a sensitivity/specificity of 92.7/87.3% on the independent validation dataset and 97.5/87.5% on the gold standard dataset (Fig. 2 and 3).

Conclusion: Novel, data and consensus driven classification criteria for ASSD have been established with excellent validity, feasibility and global consensus. These criteria should improve the consistency and accuracy of ASSD classification across the world and facilitate future research.

The project was funded by ACR/EULAR and ASSD criteria will be submitted for their approval.

Figure 1: The classification criteria for Anti-synthetase syndrome

Figure 2: performance of Define ASSD on A) Independent validation dataset and B) Gold standard dataset

Figure 3: performance of Probable ASSD on a) Independent validation dataset and b) Gold standard dataset

Disclosures: G. Zanframundo: None; S. Faghihi Kashani: None; A. Yoshida: None; E. Dourado: None; I. Bauer Ventura: None; D. Rivero Gallegos: None; A. Loganathan: None; G. Sambataro: Boehringer-Ingelheim, 6; F. Bozan: None; S. Bae: None; D. Lim: None; M. Cheli: None; Y. Yamano: None; F. Bonella: AstraZeneca, 12, support for attending meetings/travel, Atyr, 12, Support for attending meetings and/or travel, Boehringer-Ingelheim, 1, 2, 6, 12, support for attending meetings/travel, Bristol-Myers Squibb(BMS), 1, 2, Sanofi, 1, 2, 6, Savara Pharma, 2, 12, Support for attending meetings and/or travel; T. Corte: 4D, 2, 5, Avalyn Therapeutics, 1, 2, 5, Boehringer-Ingelheim, 1, 2, 5, 6, 12, support for attending meetings/travel, Bridge Biotherapeutics, 1, 2, 5, Bristol-Myers Squibb(BMS), 1, 2, 5, 6, 12, support for attending meetings/travel, Cincera, 2, DevPro, 1, 2, Endeavour BioMedicine, 1, 2, Pharmaxis, 2, 5, Pliant, 1, 2, 5, Roche, 1, 2, 5, 6; T. Doyle: Bayer, 5, Genentech, 5, Sanofi, 3, 5; D. Fiorentino: Argenx, 2, Biogen, 2, Kyverna, 2, Pfizer, 2, Priovant, 2, Serono, 5; M. Gonzalez-Gay: None; M. Hudson: AstraZeneca, 6, Boehringer-Ingelheim, 2, 5, 6, Bristol-Myers Squibb(BMS), 5, Merck/MSD, 6; M. Kuwana: Argenx, 1, Boehringer-Ingelheim, 2, 5, 6, GlaxoSmithKlein(GSK), 2, MBL, 5, 9; I. Lundberg: Boehringer-Ingelheim, 6, Novartis, 11, Roche, 11; A. Mammen: None; N. McHugh: None; F. Miller: Hope Pharmaceuticals, 5; C. Montecucco: None; C. Oddis: None; J. Rojas-Serrano: None; J. Schmidt: CSL Behring, 2, 6, 12, Support for attending meetings and/or travel, Griflos, 2, Janssen, 2, Kezar, 5, Takeda, 2, 6, UCB, 6; C. Scirè: AbbVie/Abbott, 2, 5, Eli Lilly, 5, 12, Galapagos - Grant/Research support; A. Selva-O'Callaghan: None; V. Werth: AbbVie/Abbott, 2, Akari, 2, Amgen, 2, 5, Argenx, 2, AstraZeneca, 1, 2, 5, Bayer, 2, Beacon Bioscience, 2, Biogen, 2, 5, Bristol-Myers Squibb(BMS), 2, 5, Cabaletta Bio, 2, Calyx, 2, Corbus Pharmaceuticals, 5, Corcept, 2, Crisalis, 2, CSL Behring, 2, 5, Cugene, 2, Eli Lilly, 2, EMD Sorona, 2, Gilead, 2, 5, GlaxoSmithKlein(GSK), 2, Horizon, 2, 5, Incyte, 2, Janssen, 2, 5, Kezar, 2, Kwoya Kirin, 2, Merck/MSD, 2, Nektar, 2, Nuvig Pharmaceuticals, 2, Octapharma, 2, Pfizer, 2, 5, Priovant, 5, Regeneron, 2, 5, Rome Pharmaceuticals, 2, 5, Sanofi, 2, Takeda, 2, UCB, 2, Viela Bio, 2; D. Rozza: None; M. Morosini: None; S. Bozzini: None; L. Cavagna: Boehringer-Ingelheim, 1, 6, GlaxoSmithKlein(GSK), 6; R. Aggarwal: Alexion, 2, ANI Pharmaceuticals, 2, Argenx, 2, Artasome, 2, AstraZeneca, 2, Boehringer-Ingelheim, 2, 5, Bristol-Myers Squibb(BMS), 2, 5, Cabaletta Bio, 2, Capella Bioscience, 2, Capstanx, 2, Corbus, 2, CSL Behring, 2, EMD Serono, 2, 5, Galapagos, 2, Horizon Therapeutics, 2, I-Cell, 2, Immunovant, 2, Janssen, 2, 5, Kezar, 2, Kyverna, 2, Lilly, 2, Mallinckrodt, 5, Manta Medicines Corporation, 2, Novartis, 2, Nuvig Therapeutics, 2, Octapharma, 2, Pfizer, 2, 5, Q32, 5, Roivant, 2, Sanofi, 2, Teva, 2, Tourmaline Bio, 2, Verismo Therapeutics, 2.

To cite this abstract in AMA style:

Zanframundo G, Faghihi Kashani S, Yoshida A, Dourado E, Bauer Ventura I, Rivero Gallegos D, Loganathan A, Sambataro G, Bozan F, Bae S, Lim D, Cheli M, Yamano Y, Bonella F, Corte T, Doyle T, Fiorentino D, Gonzalez-Gay M, Hudson M, Kuwana M, Lundberg I, Mammen A, McHugh N, Miller F, Montecucco C, Oddis C, Rojas-Serrano J, Schmidt J, Scirè C, Selva-O'Callaghan A, Werth V, Rozza D, Morosini M, Bozzini S, Cavagna L, Aggarwal R. The Classification Criteria for Anti-Synthetase Syndrome (Class) Project [abstract]. Arthritis Rheumatol. 2024; 76 (suppl 9). https://acrabstracts.org/abstract/the-classification-criteria-for-anti-synthetase-syndrome-class-project/. Accessed .

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