DELAYED DIAGNOSIS IN SYSTEMIC LUPUS ERYTHEMATOSUS
Notice bibliographique
Résumé
O014 / #382 Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes ABSTRACT CONCURRENT SESSION 02: SLE METRICS – IMPROVING OUTCOMES & MEASURES 22-05-2025 1:40 PM - 2:40 PM Background/Purpose Systemic lupus erythematosus (SLE) is a multisystemic autoimmune disease of unknown etiology. Diagnosis is often delayed because it frequently mimics symptoms of other diseases; this also delays treatment initiation. Previous studies have reported that this delay in diagnosis was associated with a worse prognosis including higher disease activity, damage accrual, decreased quality of life and increased use of healthcare resources and, therefore, higher costs. In the Grupo Latino Americano de Estudio del Lupus (GLADEL) original cohort, a maximum time to SLE diagnosis of 24 months did not negatively influence disease outcomes (damage accrual and mortality).[1] This study aimed to characterize delay in the diagnosis in SLE patients and its associated factors. Methods GLADEL 2.0 is an observational multiethnic, multinational Latin American SLE cohort. Forty-three centers from 10 Latin American countries enrolled patients ≥ 18 years of age who fulfilled the 1982/1997 American College of Rheumatology (ACR) and/or the 2012 Systemic Lupus International Collaborating Clinics (SLICC) classification criteria. Patients were categorized into 4 subsets according to the presence or absence of active or inactive lupus nephritis (LN) 2 . Baseline demographics, clinical manifestations, disease activity (SLEDAI-2K), SLICC/ACR damage index (SDI), and treatments were examined. Based on the original GLADEL report, variables were examined according to time to diagnosis shorter vs equal or longer than 24 months, as no impact was found on outcomes before this time.[1] Continuous variables are summarized as median (Q1, Q3) and categorical variables as counts and percentages. Logistic regression models were used to identify factors independently associated with a delay in diagnosis ≥ 24 months. P-values < 0.05 were considered significant. All analyses were done using R v4.4.0. Results Of the 1083 patients included in this GLADEL cohort, 985 were included in these analyses. The remaining patients were excluded because of insufficient data for analysis. The median time to diagnosis was 8 months (0.27–5.67); in 97 patients (9.84%) the time to diagnosis was ≥ 24 months. Table 1 depicts the sociodemographic and clinical characteristics of SLE patients according to time to diagnosis. Patients with a time to diagnosis ≥ 24 months were found to be older at diagnosis, having a higher frequency of thrombocytopenia, associated comorbidities, antiphospholipid syndrome (APS), anti-beta-2-glycoprotein-I (B2GPI) positivity and cumulative damage with lower frequency of low complement at cohort entry. After adjusting for sociodemographic, clinical and immunologic features, multivariate analysis showed that older age, middle socioeconomic status and associated APS were associated with a higher probability of diagnostic delay (Table 2). Table 1. Clinical and sociodemographic characteristics of SLE patients from the GLADEL 2.0 cohort according to time to diagnosis Table 2. Univariable and multivariable Cox regression analyses of factors associated with delayed diagnosis in SLE patients from the GLADEL 2.0 cohort Conclusions In the GLADEL 2.0 multiethnic cohort, we found that delay in diagnosis was more likely to occur in older SLE patients and it was associated with APS. Future analyses will allow us to identify the impact of delayed diagnosis on outcome of SLE patients. References: [1.] Nieto R. Lupus 2024;33(4):340-6. [2.] Gómez-Puerta JA. Lupus 2021;28:961203320988586.
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 | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».