Including myositis-specific autoantibodies improves performance of the idiopathic inflammatory myopathies classification criteria
Notice bibliographique
Résumé
Including additional myositis-specific autoantibodies into the EULAR/ACR classification criteria identifies idiopathic inflammatory myopathy patients and subtypes more accurately. Sir, The 2017 EULAR/ACR classification criteria for idiopathic inflammatory myopathies (IIM) were developed to identify homogeneous populations of IIM patients for research purposes [1, 2]. The presence or absence of certain disease features contribute to an IIM probability score, with ⩾50% indicating ‘possible’, ⩾55% ‘probable’ and ⩾90% ‘definite’ IIM. Using the ‘probable IIM’ probability cut-off, sensitivity remains high (93%) [1]. However, while classification as ‘definite IIM’ is the suggested threshold for inclusion in studies where high specificity levels are required, sensitivity is lower (around 70%), limiting the number of patients eligible for enrolment [1]. Those with ‘definite IIM’ or ‘probable IIM’ can be further distinguished using a classification tree into one of four IIM subtypes: PM, IBM, amyopathic dermatomyositis and DM. As immune-mediated necrotizing myopathy (IMNM) was only recently recognized as a distinct entity, only small numbers of these cases were included in the classification design process. The authors were thus unable to distinguish PM from IMNM in the classification tree [1, 3]. As highlighted by Lundberg and Tjärnlund, another limitation of the criteria is the limited use of myositis-specific autoantibodies (MSAs), with only anti-Jo1 status included in the final criteria [4]. As the project to define these criteria commenced over a decade ago, many MSAs were either undiscovered or their detection assays were not widely accessible, preventing inclusion [1, 4]. However, recent years have seen a revolution in the availability of MSA testing, with highly specific and reliable line blot immunoassays commercially available and able to test for a broad complement of MSAs simultaneously. It is considered that integration of a wider repertoire of MSAs into updated classification criteria might improve performance both in terms of case definition and in assigning IIM subtype. To evaluate this, we conducted a study of patients in our IIM cohort where a panel of MSA results, in addition to anti-Jo1, were available. We identified all adult patients (⩾18 years at disease onset) with a physician-verified diagnosis of IIM. Details of data source and case ascertainment are available in the supplementary material, section Case ascertainment, available at Rheumatology online. The EULAR/ACR criteria were applied to each case and results were categorized using the suggested cut-points into non-IIM, possible IIM, probable IIM and definite IIM. We then identified all patients with a non-anti-Jo1 MSA including anti-PL7, anti-PL12, anti-EJ, anti-OJ, anti-Mi2, anti-MDA5, anti-SAE1, anti-transcription intermediary factor 1γ, anti-NXP2 and anti-signal recognition particle using a line blot immunoassay (EUROLINE Inflammatory Myopathies 16 Ag, Euroimmun, Lubeck, Germany). This assay has not been fully validated yet, but has high reported specificity for IIM [5]. Anti-3-hydroxy-3-methyl-glutaryl-coenzyme A reductase was also identified via ELISA. The same criteria including classification tree were then reapplied, with the non-anti-Jo1 MSAs assigned the same weight as an anti-Jo1 antibody. We identified 309 patients with an average age of 55.6 years, of whom 62.8% were female. Of these, 27/309 (8.7%) possessed anti-Jo1 antibodies, while 78/309 (25.2%) were negative for anti-Jo1 antibodies, but had an alternative MSA. This work forms part of a national quality improvement project aimed at accurate identification of IIM cases for development of specialized disease commissioning and service planning. Given this context, approval for the conduct of the project was granted without a recommendation to seek more formal ethics authorization. In the non-anti-Jo1 MSA-positive subgroup, according to the EULAR/ACR criteria, 57/78 (73.1%) had ‘definite IIM’, 13/78 (16.7%) ‘probable IIM’, 0/78 ‘possible IIM’ and 8/78 (10.3%) ‘non-IIM’. When other MSAs were given the same weight as an anti-Jo1 in the antibody criterion, classification of definite IIM increased to 75/78 (96.2%) patients. Those with probable IIM reduced to 3/78 (3.8%) and no patients were defined as ‘non-IIM’ (Table 1). The relationship between EULAR/ACR classification criteria with and without inclusion of non-anti-Jo1 MSAs IIM: idiopathic inflammatory myopathy; MSA: myositis-specific autoantibody. The relationship between EULAR/ACR classification criteria with and without inclusion of non-anti-Jo1 MSAs IIM: idiopathic inflammatory myopathy; MSA: myositis-specific autoantibody. Currently, MSAs are not included in the classification tree used to define IIM subtypes. In our cohort, 25/78 (32.1%) patients were subtyped as PM and had an MSA other than anti-Jo1. Fifteen of 78 (19.2%) patients had a diagnosis of IMNM with either anti-3-hydroxy-3-methyl-glutaryl-coenzyme A reductase or anti-signal recognition particle antibodies and met 2017 European Neuromuscular Centre criteria for IMNM [3]. A further 8/78 (10.3%) cases had antisynthetase syndrome with a relevant antisynthetase antibody. One of 78 (1.3%) patients had dermatomyositis sine dermatitis with an anti-NXP2 antibody. We highlight improved performance of the EULAR/ACR classification criteria after inclusion of widely available MSA results, building on the experience of others who have examined the effect of including antisynthetase antibodies [6]. We have demonstrated that including non-anti-Jo1 MSAs increases the likelihood of classifying patients as ‘definite IIM’ or ‘probable IIM’, facilitating accurate diagnosis and inclusion of patients into clinical trials and research studies. Additionally, inclusion of MSAs into the classification tree can more precisely define IIM subtypes, reducing the number of patients incorrectly classified as PM and potentially supporting the use of targeted treatments for individual subgroups and facilitating accurate inclusion in subgroup-specific clinical trials. We suggest that an additional layer of the classification tree referring to MSA status is added at the point where patients are currently subtyped as PM. Elements from the 2017 European Neuromuscular Centre criteria for IMNM may be integrated into this. Limitations to the study include its retrospective single-centre design and the absence of a comparator group. Finally, the specificities of all MSAs are not yet fully determined [7]. Funding: This work was supported by a grant from the Medical Research Council (MR/N003322/1). This report includes independent research supported by the National Institute for Health Research (NIHR) Biomedical Research Centre Funding Scheme. The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, the NIHR or the Department of Health. Disclosure statement: The authors have declared no conflicts of interest.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».