Cardiovascular Events in Lupus Nephritis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Systemic lupus erythematous (SLE) is known to be associated with cardiovascular events (CVEs). However, the incidence of CVE has not been thoroughly investigated in lupus nephritis (LN) patients. In this meta-analysis, we aimed to assess the incidence of CVE in patients with LN. Methods: We performed a literature search in PubMed, Scopus, and Web of Science database for studies reporting CVE (myocardial infarction [MI], heart failure, cerebrovascular accident [CVA] [i.e., ischemic or hemorrhagic stroke or transient ischemic attack], any cardiovascular- or cerebrovascular-related disease or death) in patients with LN. In addition, subgroup analyses were conducted according to geographical locations and kidney disease status. We also separately reported the incidence rate of MI, CVA, and cardiovascular- or cerebrovascular-related deaths, with CVE and MI risk in patients with LN. Results: Twenty-one records, encompassing 29,489 subjects, were included. The overall CVE incidence was 9% (95% confidence interval [CI]: 6–12%). Specifically, the incidence of MI (8 studies, n = 5,735), CVA (9 studies, n = 6,053), and mortality attributed to any cardiovascular or cerebrovascular disease (10 studies, n = 26,511) were 4% (95% CI: 2–7%), 4% (95% CI: 2–7%), and 5% (95% CI: 3–7%), respectively. Geographically, patients residing in Asia exhibited a lower incidence of CVE (2.3%, 95% CI: 1.6–3.3%) compared to those residing in North America (10.1%, 95% CI: 5.7–17.2%) and Europe (13.3%, 95% CI: 7.6–22.4%). Patients with LN had higher risk of CVE compared to SLE subjects (odds ratio: 1.18, 95% CI: 1.03–1.34, p = 0.014). Conclusion: CVE occurrence among individuals with LN is significant, and this disease entity increases CVE risk, highlighting the importance of implementing early therapeutic interventions to prevent poor outcomes.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".