Venous thromboembolism in patients with lupus nephritis: frequency and risk factors—a cohort study
Bibliographic record
Abstract
OBJECTIVES: Venous thromboembolism (VTE) is a known complication of SLE, yet there is a lack of high-quality studies specifically focused on LN. This study aimed to assess the frequency of VTE in patients with LN and identify risk factors for its development. METHODS: We included patients with biopsy-proven LN from a prospective observational cohort followed between 1970 and 2024. The primary outcome, VTE occurring after the onset of LN, was monitored longitudinally, and the time to the first event was calculated. Time-varying univariable and multivariable cause-specific Cox proportional hazards models were used to identify factors associated with the first VTE, with death considered a competing risk. RESULTS: A total of 324 patients were included, with a mean age of 34.2 years [interquartile range (IQR): 25.9-43.0] at LN onset. Over a long-term follow-up period of 9.9 years (IQR: 5.0-16.4), 30 patients (9.3%) developed VTE, with a total of 34 events. The median time to the first event from LN onset was 4.4 years (IQR, 0.1-14.1). Most events were isolated (86.7%), including 19 deep vein thromboses (DVTs, 59.4%), 5 pulmonary embolisms (PEs), and 2 events involving other venous beds. In the multivariable model, the following factors were independently associated with the development of VTE: the SLEDAI-2K score [hazard ratio (HR) = 1.05, 95% CI: 1.01-1.10] and proteinuria level (HR = 1.26, 95% CI: 1.08-1.47). CONCLUSION: VTE can complicate the course of LN at any stage. Disease activity and proteinuria are the primary risk factors for its development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".