PERFORMANCE OF THE 2019 EULAR/ACR AND 2012 SLICC CLASSIFICATION CRITERIA FOR SLE AS DIAGNOSTIC CRITERIA
Notice bibliographique
Résumé
PV240 / #328 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose 2019 EULAR/ACR SLE criteria were validated for classification from cases of established SLE compared to other diseases (sensitivity 0.96 (0.95 - 0.98); specificity 0.93 (0.91 - 0.95)),[1] but have not been assessed as diagnostic criteria in an undiagnosed cohort of ANA positive patients clinically suspected to have SLE. The aim is to evaluate, in these patients, the performance of 2019 EULAR/ACR SLE criteria against consultants’ diagnosis of SLE after 3 years follow-up and consultants’ treatment decision. Methods We included all consecutive consenting patients with ANA positive ≥ 1:80, new symptoms of ≤ 1 year, and SLE treatment naïve who had been referred to a specialist lupus clinic from primary care. 2019 EULAR/ACR SLE criteria and 2012 SLICC criteria were evaluated by research fellows at the time of the enrollment (T0) and evaluated annually, or on additional visits, during a 3-years follow-up. Clinical charts (at T0, and at the visit in which patients met classification criteria or last follow-up visit for those who did not meet them, T1) were anonymized and any documented diagnosis was redacted. Charts were reviewed by 4 consultants who had been independent of the patients’ clinical care to assess the diagnosis as well as confidence in diagnosis and suggested treatment decision. Interrater agreement was analyzed by Cohen’s k. Results 141 patients were included. Table 1 reports baseline characteristics. At T1, SLE was diagnosed by consultants in 26 patients (18.4%); a moderate interrater agreement was observed (k=0.52; CI 0.31–0.72). The 2019 EULAR/ACR classification criteria were met in 36 patients (26%) and the 2012 SLICC criteria in 33 patients (23.4%). Other consultants’ diagnoses were: Undifferentiated CTD (UCTD, 19.1%), Sjögren’s Disease (SjD, 8%), inflammatory arthritis (IA, 3.5%). Table 2 reports sensitivity, specificity, PPV, NPV of the 2019 EULAR/ACR SLE and 2012 SLICC criteria. At T1, in 21 patients (14.8%) SLE diagnosis was agreed by the consultants and 2019 EULAR/ACR SLE criteria; consultants’ suggested treatments for these patients were: immunosuppressant (IS) +/- hydroxychloroquine (HCQ) in 11 cases (52.4%), HCQ alone in 9 (42.8%), and no treatment in 1 (4.8%). In 5 patients (3.5%) SLE was diagnosed by consultants but not classified by the EULAR/ACR SLE criteria; the suggested treatment from the consultants were: IS in 3 cases (60%), HCQ in 2 (40%). In 15 patients where 2019 EULAR/ACR criteria were met, consultants did not diagnose SLE (10.6%); consultants’ diagnoses were: SjD (40%), UCTD (33.3%), IA (26.7%%); treatment were: 7 HCQ (46.7%; in 1 case with im steroid), 5 IS (33.3%), 1 im steroid (6.7%) and no treatment in 2 (13%). In addition to the analyses above, at T0, the consultants diagnosed 11 patients who did not meet EULAR/ACR criteria as SLE. But at T1, 6/11 (55%) of these were subsequently classified as SLE. Thus, SLE could be diagnosed earlier than using classification criteria in some patients. Table 1: Features at baseline Table 2: Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value Conclusions When used in a diagnostic setting, the performance of 2019 EULAR/ACR SLE criteria was less good than for classification. Patients meeting classification criteria were usually clinically diagnosed as SLE as well, but some patients with a consultant diagnosis of SLE did not meet criteria and in these cases, treatment decisions were similar to those meeting criteria. Classification criteria should be used with caution for the diagnosis when evaluating patients with suspected SLE. Future work will analyze a second cohort. References: [1.] Aringer M. ARD 2019;78(9):1151-9.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».