COMORBIDITY BURDEN AND DEMOGRAPHICS OF PATIENTS ACROSS SUBTYPES OF CLE FROM A LARGE US ELECTRONIC HEALTH RECORD DATABASE STUDY
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".