227 The lupus severity index accurately identifies patients with severe SLE in a multi-ethnic cohort
Bibliographic record
Abstract
Background The Lupus Severity Index (LSI)1 proposes to stratify patients by disease severity for clinical research. The LSI ranges from 0–10, is calculated using ACR classification criteria (ACRc) and demonstrated high predictive accuracy for severity anchored to major immunosuppressive drug use. We investigated the performance and characteristics of the LSI in a large multiethnic lupus cohort. Methods Patients from a single academic center were followed from 1990–2016 using a custom database. Records of all SLE patients were abstracted. Variables included birthdate, diagnosis date, ethnicity, ACRc, SLICC Damage Index (SDI), treatment and date of death. Ethnicity was categorized into White (WHI), Asian (ASN), Indigenous (IND), and Other. The LSI was calculated from ACRc, and compared between ethnicities and demographic variables known to be associated with severe SLE using t-tests, ANOVA, Pearson correlation coefficient and logistic regression. Results 832 SLE patients were identified: 497 (60%) WHI; 220 (26%) IND; 91 (11%) ASN; 24 (3%) Other. Mean age was 49 years, mean disease duration 15 years, 90% female, mean age at diagnosis 35; 163 (20%) of patients had died. The mean LSI was 6.9, range 3.2–9.7. The distribution of the LSI was similar to that in the original dataset (figure 1A) and the area under the ROC curve, measured against prescription of major immunosuppressive drugs, was 0.69 (95%CI 0.65–0.73). LSI was higher in males compared to females (7.3 vs. 6.9; p-0.019), and was negatively associated with onset age (Onset <18 years LSI=7.8; 18–50 years LSI=6.8;>50 years LSI=6.6; p<0.001). LSI correlated with SDI(Pearson 0.28, p<0.001),and was a predictor of accruing any damage (SDI>1) (OR1.2 95% CI 1.1–1.3).LSI was higher in non-whites compared to whites: WHI LSI=6.6; IND LSI=7.2; Other LSI=7.3; ASN LSI 8.1; p<0.001). LSI was a predictor of early mortality (Death at age <50, or disease duration <10 years): OR 1.2; 95% CI 1.0–1.3). The distribution of the LSI varied by ethnic group with more uniformly severe disease in ASN patients (figure 1B, C) compared to WHI and IND. Conclusions Similar to the original publication, higher LSI correlated with male sex, younger onset age, and non-white ethnicity; all groups shown to have more severe SLE. LSI was also a predictor of damage and early mortality. In addition we also found the distribution of LSI to differ between ethnicities. These findings confirm the utility of the LSI in stratifying patients by severity, and supports further exploration of the LSI to investigate contributors to severe SLE. Funding Source(s): None Reference Bello GA, et al. Lupus Science & Medicine 2016.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".