ANALYZING THE RECURRENCE PATTERNS IN CUTANEOUS LUPUS ERYTHEMATOSUS: A RETROSPECTIVE ANALYSIS OF SCLE AND DLE FLARES
Bibliographic record
Abstract
PV062 / #535 Poster Topic: AS07 - Cutaneous Lupus Background/Purpose Although morphologically distinct, subacute cutaneous lupus erythematosus (SCLE) and discoid lupus erythematosus (DLE) can leave patients with damage and remnants of previous inflammation including dyspigmentation, scarring, atrophy and alopecia. Tissue resident memory T (Trm) cells have been implicated in the recurrence of SCLE and DLE. 1,2 However, the flare patterns in SCLE and DLE - whether in healed, scarred, dyspigmented, or uninvolved skin - remain underexplored. To address this gap, we conducted a retrospective study of SCLE and DLE patients, examining Cutaneous LE Disease Area and Severity Index-Activity (CLASI-A) scores and photographic documentation of inflammation recurrence. Methods Subjects were selected from a database of CLE patients seen at the autoimmune skin disease clinic of the Hospital of the University of Pennsylvania that has been ongoing since January 2007. A significant flare of skin disease was determined as an increase in the CLASI-A score of 7 or above in sequential clinic visits as previously described. 3 Two reviewers independently screened images before and after a flare and documented whether a flare occurred in previously inflamed skin, never involved skin, or both. The anatomical location of each flare observed in photographs was cross-verified with location-specific CLASI-A scores. Additional data collected included patient age, sex, smoking status, concomitant SLE, absolute change in CLASI-A score, and CLASI-Damage scores at the time of flare. Results Six-hundred and 28 patients were screened, with 77 patients having a flare. We identified 27 patients (11 SCLE and 16 DLE) with photo-documentation of their skin flare. DLE patients were significantly more likely to experience flares confined to previously inflamed sites (83%) compared to SCLE patients (36%) (relative risk [RR] 2.29; p<0.01) (Figure 1a-c). In contrast, SCLE patients more frequently developed flares involving both previously affected and new locations (RR 3.8; p<0.01) (Figure 1d and e). Involvement of the neck, face and scalp were common locations newly affected by a flare in SCLE patients. There was no statistically significant association detected from the additional data gathered (as can be seen in Table 1). Figure 1. A patient with SCLE (a-c). (a) prior to a flare, showing mild disease confined to the back, (b) and (c) following a flare of skin disease. SCLE lesions not only recur in the same anatomical location, but the patient also developed new lesions to the neck, cheeks, forehead, cutaneous lips and scalp. A patient with DLE (d and e). (d) prior to a flare, showing mild activity to the right medial brow and scarring to the nasal bridge, (e) Flare of DLE to the right medial brow, worsening at the same anatomical location as before. Table 1. Patient Demographics Conclusions Our findings reveal distinct recurrence patterns in DLE and SCLE, with implications for understanding the mechanisms driving flares in these subtypes of CLE. A high proportion of DLE flares recurred in areas of previous inflammation including areas of scarring, atrophy and dyspigmentation. Previous studies have showed a higher number of Trm cells in DLE lesions compared to SCLE, suggesting a robust accumulation and later activation of Trm cells in healed DLE skin, supporting our observations and underscoring of the role of these cells in flares. 1,2 In contrast, SCLE flares were more likely to involve both previously inflamed sites and new areas, notably on the neck, face, and scalp. This broader distribution of SCLE flares may indicate that, in addition to Trm cell activation, external triggers such as UV exposure or medication-related photosensitivity may play a more significant role in driving disease recurrence. The distinct flare patterns observed in SCLE highlight a complex interplay between resident immune cells and environmental factors, which may contribute to the spread of inflammation to new skin regions even after initial sites have healed. A deeper understanding and further studies focusing on the interactions between Trm cells, the skin microenvironment, and external triggers for CLE flares may enable the development of targeted interventions, ultimately improving quality of life and clinical outcomes for patients with CLE.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".