Fixed-Dose Combination Therapy for the Prevention of Cardiovascular Diseases in CKD
Bibliographic record
Abstract
BACKGROUND: Fixed-dose combination treatments reduce cardiovascular disease in primary prevention. We aim to explore whether those benefits differ in the presence of CKD. METHODS: We conducted an individual participant data meta-analysis in 18,162 participants on the efficacy and safety of treatment for the primary prevention of cardiovascular disease. Combination therapies consisted of at least two BP-lowering drugs and a statin, with or without aspirin versus placebo or minimal care. Here, we examine the differential effect of fixed-dose combination treatment on the risk of developing cardiovascular disease in participants with a low eGFR (<60 ml/min per 1.73 m 2 ; Chronic Kidney Disease Epidemiology Collaboration formula) compared with a normal eGFR (≥60 ml/min per 1.73 m 2 ). The primary composite outcome was time to first occurrence of a combination of cardiovascular death, myocardial infarction, stroke, or arterial revascularization. RESULTS: At baseline, the mean level of eGFR was 76 ml/min per 1.73 m 2 (SD 17). In total, 3315 (18%) participants had low eGFR at baseline. During a median follow-up of 5 years, among participants with normal eGFR, the primary outcome occurred in 232 (3%) participants in the treatment group compared with 339 (5%) in the control group (hazard ratio, 0.68; 95% confidence interval, 0.57 to 0.81; P < 0.001). In participants with low eGFR, the primary outcome occurred in 64 (4%) participants in the treatment group compared with 130 (8%) in the control group (hazard ratio, 0.49; 95% confidence interval, 0.36 to 0.66; P < 0.001; P for interaction 0.047). The relative risk reduction among participants with low eGFR was larger for combination strategies, including aspirin compared with treatments without aspirin. Apart from dizziness, other side effects did not differ between treatment and control groups, regardless of the stage of their kidney function. CONCLUSIONS: A fixed-dose combination treatment strategy is effective and safe at preventing cardiovascular disease, irrespective of eGFR, but relative and absolute risk reductions are larger in individuals with low eGFR. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_11_08_CJN0000000000000251.mp3.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".