Extravasation, thrombosis, and infection with vasopressor infusion through peripheral intravenous catheters: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The safety of administering vasopressors through peripheral venous catheters (PVCs) remains controversial, primarily due to concerns regarding extravasation, thrombosis, and catheter-related infections. This study aimed to systematically summarize the prevalence of these complications through a meta-analysis. Methods: The PubMed, Excerpta Medical Database (Embase), Cochrane Library, Web of Science (WOS), China National Knowledge Infrastructure (CNKI), Wanfang (WF), Chinese Science and Technology Journal Database (VIP), and China Biology Medicine disc (CBMdisc) databases were systematically searched (from database establishment 16 August 2025) to retrieve pertinent articles, and study quality was rated via the Joanna Briggs Institute (JBI) scale and Newcastle-Ottawa Scale (NOS). The data analysis was conducted using the meta package in R, and random/fixed-effects models were applied to combine the complication rates based on heterogeneity. Sensitivity and subgroup analyses were also carried out. Results: =63%), respectively. The subgroup analysis peripherally inserted central catheters (PICCs) carried a higher risk of thrombosis, while midline catheters (MCs) had the lowest risk of extravasation. In relation to the catheter-related infection risks, PVCs showed the lowest incidence, whereas PICCs had the highest. Limited direct comparative evidence indicated no statistically significant differences between PVCs and central venous catheters (CVCs). Conclusions: Under standardized procedures, PVCs may be a viable option for vasopressor infusion, particularly MCs, which showed the lowest risk of extravasation. Caution is warranted with PICCs due to the potential risk of thrombosis, while traditional PVCs should be limited to short-term or emergency use. Future well-designed studies with standardized definitions are needed to strengthen the reliability and clinical applicability of the evidence.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".