Local anaesthetics in interventional radiology: a primer for radiologists on applications and management of complications
Bibliographic record
Abstract
Local anaesthetics (LAs) allow a range of procedures to be performed in interventional radiology (IR) through improving patient comfort and reducing pain. This review serves as a primer for interventional radiologists, providing an overview of commonly used LAs and practical tips for their implementation. With its quick onset time and moderate duration of action, the amide lidocaine is the most used and applicable to a variety of procedures such as biopsies and embolization. In contrast, bupivacaine and ropivacaine (both amides) have longer durations of action, and are therefore suitable for lengthy procedures and pain control post-procedurally. Procaine, an ester, may be used in cases of amide anaesthetic allergies. This review examines the clinical applications of LAs in radiology and management of their adverse effects including local anaesthetic systemic toxicity (LAST) and allergic reactions. It concludes with a discussion of LAST, emphasising techniques for early intervention and management. The role of lipid emulsion therapy and modifications to the advanced cardiac life support (ACLS) protocol are highlighted, including a discussion of other aspects such as airway management. By presenting the latest strategies to manage LAST and adverse effects, this research aims to help standardise anaesthetic management in radiology. It provides actionable steps for selecting and injecting anaesthetics, and management of complications that will be beneficial for interventional radiologists performing a diverse array of procedures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".