CHARACTERIZING SLE PATIENTS INTO TYPE 1 AND TYPE 2 DISEASE STATES: INSIGHTS FROM A SINGLE LUPUS COHORT
Bibliographic record
Abstract
PV213 / #662 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose It has been proposed that SLE may be divided into Type 1 and Type 2 states. Type 1 manifestations are well captured in disease activity scores (eg, SLEDAI-2K). Type 2 manifestations include fatigue, pain, depression, brain fog. These do not always correlate with conventional lupus disease activity, but can be pervasive and dominate in patient-reported outcomes (PROs). Patients may be classified into 4 disease states: Minimal (low type 1 activity, low type 2 activity), High Type 1 (high type 1 and low type 2), High Type 2 (low type 1, high type 2) and Mixed (high type 1 and high type 2). Our primary aim was to define the prevalence of patients who can be classified into the 4 states. Our secondary aim was to identify variables that predict each disease state. Methods This was a cross-sectional study of a single cohort of SLE patients who participated in a trial studying cognitive impairment and recruited prospectively since 2016. Type 1 activity was defined as a clinical SLEDAI-2K of >0 (excluding C3, C4 dsDNA levels). As there is no clear definition of Type 2 activity, we aimed to define this based on different PRO measures: Beck Depression and Anxiety Inventories (BDI-II and BAI), Lupus Quality of Life (Lupus QoL), Fatigue Severity Score (FSS), Perceived Deficits Questionnaire (PDQ-20) for subjective cognitive impairment, Short-Form Health Survey (SF-36). We conducted a Similarity Network Fusion (SNF) analysis that used spectral clustering to generate participant subtypes and hypothesized that distinct groups would emerge (Figure 1). For our secondary aim, we included variables such as patient characteristics, SLICC/ACR damage index (SDI), antibodies, medications. Figure 1. Results 259 patients were included in the final analysis. Four distinct patient phenotypes emerged: Minimal (32 patients; 12.4%), High Type 1 (81 patients; 31.3%), High Type 2 (50 patients; 19.3%), and Mixed (96 patients; 37.1%). Patients with High Type 2 state had poorer median PRO scores in all measured outcomes (all p-values significant to <0.0001): PDQ20 (39.5 vs 20), BDI (20.2 vs 6.2), BAI (21 vs 6), SF-36 Mental Component Summary (36.5 vs 54.8) and SF-36 Physical Component Summary (32.2 vs 51.2), FSS (5.56 vs 3.11) and all Lupus QoL domains. Statistically significant variables (p<0.05 after Bonferroni correction) differentiating the 4 states included low C3/C4, elevated dsDNA, chromatin antibody presence, glucocorticoid dose and treatment with glucocorticoids within 3 months. The presence of these variables was associated with a High Type 1 and Mixed state (Table 1). Table 1. Conclusions We identified that patients may be categorized into disease states based on Type 1 and Type 2 characteristics – Minimal 13%, High Type 1 31%, High Type 2 19% and Mixed 37%. High Type 2 patients uniformly scored poorly in all PRO domains. Multiple laboratory markers (low C3/4, elevated dsDNA, presence of chromatin antibody) as well as glucocorticoid use were found to predict High Type 1 or Mixed states.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".