Association between intravitreal anti-vascular endothelial growth factor agents and hypertension: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To compare the risk of hypertension between intravitreal anti-vascular endothelial growth factor (VEGF) agents and sham injections, and between different anti-VEGF agents. METHODS: A systematic search was performed on OVID MEDLINE, EMBASE, and the Cochrane Controlled Register of Trials from January 2005 to August 2023. Inclusion criteria included published randomized clinical trials (RCTs) reporting on cases of hypertension as an adverse event for standard dose intravitreal anti-VEGF agents. Using random-effects modeling, risk ratios (RR) and 95% confidence intervals (CI) were used to estimate the frequency of hypertension between patients who received anti-VEGF agents versus sham injections. RESULTS: Twenty RCTs with 11 196 patients were included. There were 189/2873 (6.6%) new cases of hypertension in the ranibizumab 0.5-mg group, 61/1448 (4.2%) in the bevacizumab 1.25-mg group, 287/3450 (8.3%) in the aflibercept 2-mg group, 32/412 (7.8%) in the brolucizumab 6-mg group, 99/2063 (4.8%) in the faricimab 6-mg group, and 73/950 (7.7%) new cases of hypertension in patients receiving sham injections. There was no significant difference in the risk of developing hypertension between any of the included anti-VEGF agents. Compared to sham injection, ranibizumab (RR = 1.02, 95% CI: [0.74 to 1.40]; p = 0.92) and aflibercept (RR = 0.77, 95% CI: [0.54 to 1.11]; p = 0.17) did not significantly increase the risk of developing hypertension. CONCLUSION: This meta-analysis found no significant difference in hypertension risk between different intravitreal anti-VEGF agents or sham injection. These results reinforce the notion that these agents have similar and safe side effect profiles, and that the choice of agent should not be based on the risk of hypertension.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.042 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".