Efficacy and safety of ACE inhibitors versus calcium channel blockers in the management of hypertension in adults: a systematic review
Bibliographic record
Abstract
Hypertension (HTN) is a prevalent cardiovascular condition managed with various antihypertensive agents, including angiotensin-converting enzyme (ACE) inhibitors and calcium channel blockers (CCBs). This study aimed to compare the effectiveness and safety of these two classes of medications in reducing blood pressure (BP) and achieving target BP. A comprehensive literature search was conducted evaluating ACE inhibitors (e.g., Enalapril, Captopril, and Lisinopril) and CCBs (e.g., Amlodipine, Diltiazem, and Nifedipine) for HTN management. The inclusion criteria were randomized controlled trials, observational studies, and comparative studies reporting on BP reduction, achievement of target BP, and adverse events. Data were extracted on study design, interventions, participant demographics, and outcomes. Study quality was assessed using the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale. CCBs generally showed superior efficacy in BP reduction compared to ACE inhibitors. In terms of safety, ACE inhibitors were more frequently associated with cough and renal issues, while CCBs, particularly Amlodipine, were more prone to causing peripheral edema. CCBs, especially Amlodipine, generally offer greater BP reduction and a higher target achievement rate than ACE inhibitors. Despite the robust efficacy of ACE inhibitors like Enalapril, the overall safety profiles indicate that CCBs may provide a more effective approach for many patients. However, side effects such as peripheral edema need to be managed. This comparison underscores the need for individualized treatment strategies in HTN management, considering both the effectiveness and side effect profiles of antihypertensive medications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".