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Record W2163141724 · doi:10.1097/aln.0b013e31818dd8aa

Minimizing the Risk of Intravascular Injection during Ultrasound-guided Peripheral Nerve Blockade

2008· letter· en· W2163141724 on OpenAlexaffabout
Richard Brull, Anahi Perlas, Peter H. Cheng, Vincent Chan

Bibliographic record

VenueAnesthesiology · 2008
Typeletter
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineBlockadeIntravascular ultrasoundPeripheralPeripheral nerveAnesthesiaRadiologyInternal medicineAnatomy

Abstract

fetched live from OpenAlex

To the Editor: —We read with interest the recently published case reports by Loubert et al. 1and Zetlaoui et al. 2regarding suspected inadvertent intravascular injection of local anesthetic (LA) and LA toxicity during ultrasound-guided axillary brachial plexus blockade. While ultrasound likely reduces the risk of accidental vascular puncture compared with “blind” peripheral nerve stimulation,3these two case reports demonstrate that the risk of intravascular injection persists despite ultrasound guidance.1,2In 2006 and 2007 at the Toronto Western Hospital, we performed 1,797 ultrasound-guided brachial plexus blocks without any sign or symptom of LA toxicity. Good fortune notwithstanding, there are several important principles that merit thoughtful consideration to improve detection of accidental intravascular injection and possibly prevent LA toxicity during ultrasound-guided peripheral nerve blockade. We believe that the most reliable feature during real-time ultrasound imaging indicative of intravascular injection is the failure to visualize a hypoechoic fluid bolus on the ultrasound monitor during and/or after injection of as little as 1 ml of injectate. Indeed, we customarily initiate LA injection with a 1-ml bolus to exclude intravascular or intraneural4,5needle tip placement before proceeding with 5-ml increments of injectate. Visualization of the needle tip does not preclude intravascular injection per se ; it is the real-time observation of hypoechoic fluid causing tissue dispersion that most consistently excludes intravascular injection. The absence of a discernible extraneural hypoechoic fluid bolus on the ultrasound monitor means that either the needle tip is intravascular or the plane of imaging is inaccurate.We are also hesitant to recommend the use of ultrasound for perivascular block techniques as described by Loubert et al. 1Rather, we contend that perineural LA deposition is the safest application of ultrasound technology. There can be multiple veins traveling alongside landmark pulsatile arteries, especially in the axilla. Veins are exquisitely collapsible with even the slightest amount of pressure applied by the transducer to the skin, and can therefore vanish from sonographic view, fooling even the most experienced providers, as demonstrated by these two recent case reports.1,2We therefore use systematic scanning of the intended block site before needle insertion. Our systematic sonographic survey includes sliding the transducer distally and proximally to trace the target nerve along its expected course and examine the surrounding vasculature and tissues. Scanning is performed with varying degrees of pressure, with and without the use of color Doppler, to identify any hazards that may cross the planned trajectory of the needle. In addition, applying pulse wave Doppler over a nearby vessel during perineural injection may help to detect inadvertent intravascular injection by the characteristic high-pitch sound of turbulent flow associated with a sudden rush of fluid. Finally, however contentious,6we strongly believe that patients undergoing ultrasound-guided peripheral nerve blockade should remain awake with judicious sedation so that signs and symptoms of LA toxicity can be recognized, communicated, and treated immediately upon onset.*Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada. richard.brull@uhn.on.ca

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.479
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.232
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2008
Admission routes2
Has abstractyes

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