Something new about prognostic factors for lupus nephritis? A systematic review
Bibliographic record
Abstract
BACKGROUND: Lupus nephritis (LN) affects 30-45% of patients with systemic lupus erythematosus (SLE) and causes great morbidity and mortality. About 10-25% of patients will develop chronic kidney disease (CKD), and it has been described a mortality of 10-20% at 10 years. The contribution of clinical and biological markers to the prediction of outcome is unclear. OBJECTIVE: To describe the factors, with measures of association, that predict the main outcomes of LN. MATERIAL AND METHODS: We have conducted a systematic review. Medline, Embase, and Cochrane Library were systematic searched from inception up to Oct 2019, with a strategy that included synonyms of all targeted outcomes of LN: (kidney failure, response to treatment, cardiovascular events, and mortality). Only studies with longitudinal prospective design or with warranties of unbiased recollection of the prognostic factors, where LN was confirmed by biopsy were included. Risk of bias was assessed with the New Castle Ottawa scale. Predictive factors and their effect measures were collected from each study. RESULTS: From 1221 studies identified, 25 studies were included, of which 15 were retrospective, nine prospective, and one was a trial extension study (range from 3 months to 11 years). The main predictive factors of renal response were serum creatinine (SCr) and glomerular filtration rate C3 levels, titer of anti-C1q, and anti-dsDNA antibodies. Renal histological findings such as class type (IV or V), tubulointerstitial or vascular lesions and chronicity index were risk factors for development of chronic kidney disease. The factors associated with persistence of activity were proteinuria, anti-dsDNA, anticardiolipin, anti C1q antibodies, and complement values. The factors associated to cardiovascular events and mortality were age, smoking, amount of proteinuria, and histological findings, such as vascular lesions. Meta-analysis was precluded given the heterogeneity of designs definitions and effect measures. CONCLUSIONS: Nowadays, we do not have new biomarkers that establish the renal prognosis of patients with LN. Classical clinical, renal, and histological markers are used in most studies. It is worth noting the heterogeneity of studies in the definition of renal outcomes, which complicates risk stratification in these patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".