COMORBIDITY BURDEN AND DEMOGRAPHICS OF PATIENTS ACROSS SUBTYPES OF CLE FROM A LARGE US ELECTRONIC HEALTH RECORD DATABASE STUDY
Notice bibliographique
Résumé
PV097 / #349 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose Cutaneous lupus erythematosus (CLE) is an autoimmune disease with various skin manifestations, which can occur with or without systemic lupus erythematosus (SLE).[1] Few population-based observational studies have examined demographic and clinical characteristics of patients with CLE since 4 new CLE codes were introduced in the International Classification of Disease-10, Clinical Modification (ICD-10 CM) system in 2015. Electronic Health Record (EHR) databases are a rich source of data with large numbers of patients with CLE. This cross-sectional study evaluated demographics and comorbidities in a large cohort of US patients with CLE, and CLE subtypes stratified by coexisting SLE, from 2016 to 2022. Methods This analysis was performed using the Optum ® deidentified EHR data set (N = ~113 million people in the US). CLE, SLE,[2] and comorbidities were defined using ICD-9/10-CM codes. Informed by a recent study validating EHR-based algorithms to identify CLE patients,[3] CLE patients were defined as having ≥ 2 ICD-10-CM codes for CLE, with ≥ 1 code from a dermatologist or a rheumatologist during the study period (2016-2022).The date of the first CLE diagnostic code on record (index date) was considered the diagnosis date. Comorbidities were identified by ≥ 1 ICD-10 CM code. Descriptive statistics for demographics and comorbidity frequency were summarized. Results Demographics: Among the 10,025 identified patients with CLE, 47.1% had coexisting SLE (CLE+SLE). Discoid lupus erythematosus (DLE) occurred in 56.8% and 72.0% of the CLE-only and CLE+SLE patients, respectively, while subacute CLE (SCLE) occurred in 13.3% and 5.7% of those same patient groups. Demographic findings by CLE subtype, as reported in CLE-only and CLE+SLE patients, respectively, included proportion of female patients (DLE: 76.8%, SCLE: 81.1%; DLE: 89.9%, SCLE: 87.1%), median age of onset (years) (DLE: 52, SCLE: 61; DLE: 48, SCLE: 56), and proportion of African American patients (DLE: 29.1%, SCLE: 4.8%; DLE: 33.9%, SCLE: 8.9%) (Table 1). Comorbidities: Among the comorbidities of interest, the frequency of some cardiovascular risk factors and mental health disorders in patients with CLE by subtype, as reported in CLE-only and CLE+SLE patients, respectively, included: hypertension (DLE: 35.7%, SCLE: 34.1%; DLE: 54.2%, SCLE: 47.2%), obesity (DLE: 21.9%, SCLE: 16.2%; DLE: 33.9%, SCLE: 25.1%), type 2 diabetes (DLE: 10.4%, SCLE: 9.1%; DLE: 14.3%, SCLE: 9.2%), depression (DLE: 13.0%, SCLE: 13.5%; DLE: 30.1%, SCLE: 25.5%), and anxiety disorder (DLE: 16.4%, SCLE: 16.0%; DLE: 31.2%, SCLE: 31.0%) (Figure 1). Table 1: Demographic characteristics of patients with CLE, by CLE subtype, in the Opium ® EHR database 2016–2022 Figure 1. Comorbidities at interest in patients with CLE occurring anytime between 2016–2022, by CLE subtype, in the Opium ® EHR database Conclusions CLE patients across all subtypes, and with or without SLE, experience serious comorbidities including cardiovascular risk factors and mental health disorders, underlining the seriousness of CLE. Characterizing this comorbidity burden could encourage earlier screening and treatment and improve understanding of CLE beyond cutaneous manifestations. First presented at AAD 2025. References: [1.] Durosaro O. Arch Dermatol 2009;145:249-53. [2.] Barnado A. Arthritis Care Res (Hoboken) 2017;69:687-93. [3.] Guo L. Arthritis Rheumatol 2022;74(Suppl. 9) (Abstract 0318). Funding: This study was funded by Biogen (Cambridge, MA, USA). Writing and editorial support were provided by Selene Medical Communications (Macclesfield, UK), funded by Biogen.
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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 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 ».