SOCIODEMOGRAPHIC PROFILE AND INITIAL CLINICAL PRESENTATIONS IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS AMONG BANGLADESHI POPULATION
Notice bibliographique
Résumé
PV200 / #278 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose Systemic lupus erythematosus (SLE) is an autoimmune multisystem disease characterized by a diverse range of organ involvement and clinical manifestations. The presentation of SLE has significant geographical variation, with the disease being more prevalent among Asian, Hispanic, and Afro-American populations. The incidence and prevalence of SLE vary widely across different regions. Alongside variations in epidemiological subgroups such as race/ethnicity, gender, and age of onset; other sociodemographic factors including socioeconomic status, education level, and access to medical care, can influence disease activity and outcomes. This study aimed to identify the sociodemographic profile and initial clinical presentation of SLE patients among Bangladeshi population. Methods This cross-sectional observational study was conducted at the Green Life Center for Rheumatic Care and Research and the Green Life Medical College Hospital from July 2022 to June 2024. Patients diagnosed with SLE who met the classification criteria of either 1997 American College of Rheumatology (ACR) or 2012 Systemic Lupus International Collaborating Clinics (SLICC), or 2019 EULAR/ACR were included. Patients with overlap, MCTD or unwilling to participate were excluded. A total of 252 consecutive SLE patients were enrolled. Investigators conducted face-to-face interviews and reviewed medical records to gather information on the sociodemographic profile and initial clinical presentations within the first 3 months of disease onset. Data were collected using a preformed data sheet and analyzed using SPSS version 25, calculating percentages and frequencies as appropriate. Results Among the 252 patients, the majority were female (92%), resulting in a female-to-male ratio of 11.6:1. The mean age was 29.5 ± 10.6 years, with men being younger on average than women (27.5 years vs. 29.6 years). Approximately two-thirds of the participants resided in urban areas, with 62% being married, 48% identified as homemakers, and 35% as students. Over 90% of the participants had at least a primary education, and 26% were graduates. Most participants (58%) belonged to the low to lower-middle socioeconomic class, with a monthly income ranging from 10,000 to 30000 BDT. None were smoker. The most common initial clinical presentations were alopecia (85%), followed by fatigue (82.5%) and arthralgia/arthritis (80%). Additionally, 68% reported a history of fever, 64% presented with oral ulcers, 60% had various rashes, and 47% had photosensitivity. Headaches were reported by 60% of the patients. The most common initial major organ involvement was renal (41% with proteinuria and 11% with elevated serum creatinine). Anemia was observed in 70% of patients, with 24% having autoimmune hemolytic anemia, 22% had thrombocytopenia, and 14% with leukopenia. Other notable features included a history of Raynaud’s phenomenon (35%), pleural effusion (15%), convulsions (12%), psychosis (8%), pericardial effusion (4%), and other manifestations (18%). Around 60% of patients presented with more than 2 features initially. Over 50% of patients experiencing a delay of more than 6 months, and 23% requiring more than 2 years from initial presentations to final diagnosis. Conclusions The sociodemographic profile of this cohort showed that lupus affects young females, most of them are married or students and belonged to low socioeconomic backgrounds. The initial presentations were largely nonspecific. High index of suspicion is required for early diagnosis. Alopecia, fatigue, and arthralgia/arthritis were identified as the most frequent initial symptoms. Other common presenting features included fever, headaches, and anemia. Some patients may present with major organ involvement, especially renal or neuropsychiatric. Recognition of these features in young females should prompt further evaluation for SLE.
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 | 0,000 | 0,001 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».