Does Renin‐Angiotensin System Blockade Protect Lupus Nephritis Patients From Atherosclerotic Cardiovascular Events? A Case–Control Study
Bibliographic record
Abstract
OBJECTIVE: Angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs) are used as an adjuvant treatment in lupus nephritis (LN) patients with proteinuria. The primary aim of this study was to discover whether ACE inhibitors/ARBs have an atheroprotective effect similar to other, at-risk, populations. METHODS: A total of 144 patients (cases; mean ± SD age at onset 34 ± 12.1 years, followup 14.9 ± 8.6 years) with LN who were treated with ACE inhibitors/ARBs for at least 5 years were enrolled. The control group comprised 301 LN patients (mean ± SD age at onset 34.1 ± 13.2 years, followup 13.4 ± 7.9 years) with no such treatment. All patients were followed for the occurrence of atherosclerotic cardiovascular events (CVEs), consisting of transient ischemic attack and stroke, angina, myocardial infarction, percutaneous transluminal coronary angioplasty (PTCA), coronary artery bypass graft (CABG), and congestive heart failure. Patients with preexisting CVEs were excluded. RESULTS: There were no significant differences in the cumulative occurrence of CVEs (9.7% for treated versus 8.6% for nontreated patients; P = 0.708); however, hard events (stroke, myocardial infarction, CABG, and PTCA) were less frequent in treated patients (4.17% versus 5.32%). Cases were more frequently hypertensive (100% versus 52.8%; P < 0.001) and diabetic (10.4% versus 4.7%; P = 0.021), whereas controls more frequently had hypercholesterolemia (27.9% versus 18.1%; P = 0.024) and elevated triglycerides (14% versus 4.9%; P = 0.004); other variables did not differ significantly. Regression analysis failed to confirm ACE inhibitor/ARB nonuse as an important predictor of future CVEs. CONCLUSION: Our data do not support the hypothesis that ACE inhibitors/ARBs may be protective against atherosclerotic CVEs in LN patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".