Treatment patterns and control of hypertension in systemic lupus erythematosus (SLE): a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: Hypertension (HTN) is common in systemic lupus erythematosus (SLE), representing a key risk factor for cardiovascular and renal disease. We described HTN treatment patterns in SLE, evaluated uncontrolled HTN according to Canadian and American guidelines and identified factors associated with uncontrolled HTN. METHODS: We performed a cross-sectional study, identifying all McGill Lupus Clinic registry patients with an annual visit between January 2017 and May 2019 who were taking HTN medications. We excluded those taking medications only for another indication (eg, Raynaud's). We determined the frequency of uncontrolled HTN according to Canadian and American College of Cardiology/American Heart Association guidelines. Multivariate logistic regression (adjusted for age, sex and race/ethnicity) evaluated if uncontrolled HTN was more common with high body mass index (BMI), longer SLE duration, high disease activity, renal damage, multiple concomitant antihypertensives, prednisone and non-steroidal anti-inflammatory drugs. RESULTS: Of 442 patients with SLE, 108 were taking medications to treat HTN, and 38 took multiple medications concurrently. Angiotensin-receptor blockers were most common, followed by calcium channel blockers, diuretics, angiotensin-converting enzyme inhibitors and beta blockers. Among the 108 patients, 39.8% (n=43) had blood pressure (BP) >140/90 mm Hg, while 66.7% (n=72) had BP >130/80 mm Hg. In multivariate analyses, uncontrolled HTN (>130/80 mm Hg) was more likely in Caucasians (OR 2.72, 95% CI 1.12 to 6.78) and patients with higher BMI (OR 1.08, 95% CI 1.00 to 1.19). Patients with renal damage had better HTN control (OR 0.39, 95% CI 0.16 to 0.97). We could not draw definitive conclusions regarding other variables. CONCLUSION: Caucasians and patients with higher BMI had more uncontrolled HTN. The negative association with renal damage is reassuring, as controlled BP is key for renal protection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".