Characterization of Screening Patterns and Identification of Patients with Lupus Nephritis in a Community Rheumatology Setting
Bibliographic record
Abstract
Background: Lupus nephritis (LN) is a serious but common complication of systemic lupus erythematosus (SLE) characterized by proteinuria and decreased renal function. As delays in LN diagnosis may lead to nephron loss and delayed administration of disease-modifying therapies, current treatment recommendations suggest regular screening of patients with SLE for kidney involvement. Given the importance of early identification and treatment of LN, we assessed screening patterns for kidney involvement in patients with SLE in care of community rheumatologists in the United States. Methods: Patient selection criteria: ≥2 diagnosis codes for SLE separated by >30 days, in care of the American Rheumatology Network (ARN) between July 2018 and June 2023, and >365 days observation. Individual patient observation windows were calculated from the first date of observation by ARN (index date) to the last encounter date, up to June 30, 2023. Patients were suspected of having probable LN if they had any of the following qualifying events during their observation window: an ICD-10 code for LN, an ICD-10 code suggestive of LN (e.g., kidney issues or related testing), or laboratory results indicative of proteinuria or reduction in estimated glomerular filtration rate (eGFR >20% less than observed at earliest observation and where lower eGFR measure is <72 mL/min/1.73 m2 and not already categorized by ICD-10 code). Results: 8631 of >540,000 patients with data in the ARN database met all study criteria. Of these, 5314 (62%) had at least one qualifying event during their observation window; 24% of patients had a diagnosis of LN by ICD-10 code, whereas an additional 38% had laboratory values or other ICD-10 coding suggestive of LN. Of the 62% of patients with probable LN, 97% had record of an eGFR assessment compared to 66% and 62% of patients, respectively, who had record of protein assessment by urine test strip or urine protein. Conclusion: This study suggests that most patients with SLE will develop renal involvement over the course of their disease. These data support increased routine screening of SLE patients for LN via regular utilization of urine protein testing, consistent with the LN treatment guidelines. Funding: Commercial Support - Aurinia Pharmaceuticals, Inc.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".