TIME OF ONSET OF DISCOID LUPUS ERYTHEMATOSUS IMPACTS DISEASE OUTCOMES IN SYSTEMIC LUPUS ERYTHEMATOSUS: A LARGE-SCALE, PROPENSITY-MATCHED RETROSPECTIVE COHORT STUDY
Bibliographic record
Abstract
PV064 / #310 Poster Topic: AS07 - Cutaneous Lupus Background/Purpose Discoid lupus erythematosus (DLE) is the most common form of chronic cutaneous lupus erythematosus, and up to 25% of patients with systemic lupus erythematosus (SLE) develop DLE lesions during their disease course. Prior research has suggested that the presence of DLE may modify the risk of disease complications, such as lupus nephritis and serositis, in patients with SLE, however, no studies have assessed the impact of time of onset of DLE on SLE outcomes. To address this gap, we investigated the impact of DLE incidence across 3 different time points on long-term disease complications in patients with SLE. Methods We conducted a retrospective cohort study using TriNetX, a Global Collaborative Network that provides access to the deidentified medical records of more than 130 million patients across 95 healthcare organizations worldwide. TriNetX data is derived from ICD10 codes in patient records; L93.0 was used for DLE and M32.1, M32.8, or M32.9 for SLE. Four cohorts were constructed: early-onset DLE (>1 year prior to SLE), concurrent DLE (within 1 year prior to or following SLE), late-onset DLE (>1 year following SLE), and SLE patients who were never diagnosed with DLE. Within each cohort, we included adults who were diagnosed with SLE within the past 10 years and excluded patients with other systemic connective tissue disorders (M30-M31 and M33-M36). Cohorts were propensity-matched at a 1:1 ratio based on demographics, metabolic syndrome, and other chronic conditions, using greedy nearest neighbor matching. The index event was defined as the date of SLE diagnosis and the risk of 5-year incident outcomes following SLE diagnosis was compared between late-onset DLE and either early-onset DLE, concurrent DLE, or SLE without DLE using relative risk (RR) and 95% CI. All statistical analyses were conducted using the R studio package, version 3.2.3, incorporated within TriNetX. Results Following propensity score matching, 4,595 patients were included in the analyses. Across all 3 analyses, patients with late-onset DLE had increased risk of malignant neoplasms (RR 1.42 [1.06,1.90] compared to early-onset DLE, RR 1.48 [1.09,2.01] compared to concurrent DLE, and RR 2.43 [1.69,3.49] compared to SLE without DLE). In the analysis comparing early-onset to late-onset DLE, the largest number of significant differences in 5-year incident outcomes was observed. SLE patients with late-onset DLE patients had a higher RR of chronic kidney disease (RR 1.49 [1.02,2.18]), major adverse cardiovascular events (RR 1.61 [1.16,2.21]), bacterial and viral infections (RR 1.60 [1.19,2.16]), and hospitalizations (RR 1.30 [1.03,1.63]). Compared to SLE patients without DLE, patients with late-onset DLE had increased risk of UTI (RR 1.53 [1.08,2.17]) and arthritis (RR 1.39 [1.16,1.66]). In the analysis comparing SLE patients with concurrent DLE to SLE patients with late-onset DLE, the latter had increased risk of hematuria, but patients with concurrent DLE diagnosis had increased risk of mortality (RR 1.98 [1.32,2.97]) and hospitalization (RR 1.24 [1.04,1.48]). Conclusions Our findings suggest that the timing of DLE onset relative to SLE diagnosis has a substantial impact on long-term disease outcomes following SLE diagnosis. Late-onset DLE was associated with a notably higher risk of serious complications, particularly when compared to early-onset DLE. This risk stratification based on DLE onset timing highlights the importance of monitoring SLE patients for DLE development, particularly in the later stages of disease, and distinct DLE endotypes to enable earlier intervention and potentially mitigate adverse outcomes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".