Comparative peripheral edema for dihydropyridines calcium channel blockers treatment: A systematic review and network meta‐analysis
Bibliographic record
Abstract
Dihydropyridine calcium channel blockers (DHPCCBs) are widely used to treat hypertension and chronic coronary artery disease. One common adverse effect of DHPCCBs is peripheral edema, particularly of the lower limbs. The side effect could lead to dose reduction or discontinuation of the medication. The combination of DHPCCBs and renin-angiotensin system blockers has shown to reduce the risk of DHPCCBs-associated peripheral edema compared with DHPCCBs monotherapy. We performed the current systematic review and network meta-analysis of randomized controlled trials (RCTs) to estimate the rate of peripheral edema with DHPCCBs as a class and with individual DHPCCBs and the ranking of the reduction of peripheral edema. The effects of renin-angiotensin system blockers on DHPCCBs network meta-analysis were created to analyze the ranking of the reduction of peripheral edema. A total of 3312 publications were identified and 71 studies with 56,283 patients were included. Nifedipine ranked highest in inducing peripheral edema (SUCRA 81.8%) and lacidipine (SUCRA 12.8%) ranked the least. All DHPCCBs except lacidipine resulted in higher relative risk (RR) of peripheral edema compared with placebo. Nifedipine plus angiotensin receptor blocker (SUCRA: 92.3%) did not mitigate peripheral edema and amlodipine plus angiotensin-converting enzyme inhibitors (SUCRA: 16%) reduced peripheral edema the most. Nifedipine ranked the highest and lacidipine ranked the lowest amongst DHPCCBs for developing peripheral edema when used for cardiovascular indications. The second or higher generation of DHPCCBs combination with ACEIs or ARBs or diuretics lowered the chance of peripheral edema development compared to single DHPCCB treatment.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.043 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".