VALIDATION OF A SCORE FOR THE PREDICTION OF SERIOUS INFECTION IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS: DATA FROM A LATIN AMERICAN LUPUS COHORT
Bibliographic record
Abstract
PV230 / #447 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose Patients with systemic lupus erythematosus (SLE) are at increased risk of serious infections, which in turn, are associated with morbidity and mortality. The Systemic Lupus Erythematosus Registry of the Spanish Society of Rheumatology (RELESSER) group has developed and internally validated a tool for prediction of severe infections in SLE, with a recently improved version (SLE Severe Infection Score-Revised or SLESIS-R),[1] being an accurate and reliable instrument. SLESIS-R includes age, previous SLE-related hospitalization, previous serious infection, and glucocorticoid dose. This study aimed to validate SLESIS-R in a multiethnic, multinational Latin American (LA) SLE cohort. Methods GLADEL 2.0 is an observational cohort from 10 LA countries of patients ≥ 18 years of age who fulfilled the 1982/1997 American College of Rheumatology (ACR) and/or the 2012 Systemic Lupus International Collaborating Clinics (SLICC) classification criteria. Patients with sufficient data at baseline and first annual visits were included. The outcome variable was any serious infection during the first year of follow-up that led to hospitalization. Baseline demographics and clinical manifestations, disease activity (SLEDAI-2k), SLICC/ACR Damage Index (SDI) and treatments were examined. Logistic regression was used to examine the predictive effect of baseline variables on the development of serious infection in the first year of follow-up. Receiver operator characteristics (ROC) analysis was used to define the area under the curve (AUC) for SLESIS-R. The cut-off point with the best validity parameters (sensitivity and specificity) was identified. Results Of the 1016 patients who completed 1-year follow-up, 208 (20.4%) had serious infections. Patients with serious infections were older, predominantly male, and had a longer disease duration (Table 1). This group had more frequent general, cardiac, pulmonary, hematological and gastrointestinal involvement at baseline and had a higher SDI and higher proportion of previous hospitalization. Univariate and multivariate analyses show variables associated with serious infection: disease duration, pulmonary and gastrointestinal involvements, and baseline glucocorticoid use (Table 2). The AUC for the SLESIS-R score was 0.922 (0.903-0.940). A score of 7 was chosen as the optimal cut-off point, demonstrating a sensitivity of 87% and specificity of 82%. Table 1. Comparison between groups according to their baseline clinical characteristics, disease activity, damage index, and treatments Table 2. Univariate aid multivariate analyses of serious infection in SLE GLADEL patients Conclusions Almost a third of patients had serious infections during the first year of follow-up. The score performed well in predicting serious infections, similar to the original score. References: [1.] Rua-Figueroa I. Lupus Sci Med 2024;11:e001096.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".