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Record W2921380624 · doi:10.1213/ane.0000000000002488

Optimal Dose of Perineural Dexamethasone to Prolong Analgesia After Brachial Plexus Blockade: A Systematic Review and Meta-analysis

2017· review· en· W2921380624 on OpenAlexaff
Kyle R. Kirkham, Alain Jacot-Guillarmod, Éric Albrecht

Bibliographic record

VenueAnesthesia & Analgesia · 2017
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineDexamethasoneAnesthesiaBrachial plexusMeta-analysisLocal anestheticInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Perineural dexamethasone has gained popularity in regional anesthesia to prolong analgesia duration. However, uncertainty remains regarding the optimal perineural dose. Clarification of this characteristic is of significant importance as the administration of dexamethasone may lead to dose-dependent complications. The objective of this meta-analysis was to define the optimal perineural dexamethasone dose to prolong analgesia after brachial plexus blockade for adult patients undergoing upper limb surgery. METHODS: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement guidelines and searched databases including MEDLINE, PubMed, and EMBASE until January 2017, without language restriction. Only trials comparing perineural dexamethasone and local anesthetics with local anesthetics alone for brachial plexus blocks were included in the present meta-analysis. The Cochrane Collaboration's Risk of Bias Tool was used to assess the methodological quality of each trial and meta-analyses were performed following a random effects model. The primary outcome was duration of analgesia for each type of local anesthetic (short-/intermediate-acting and long-acting local anesthetics). A meta-regression followed by a subgroup analysis were performed to assess the impact of different perineural dexamethasone doses on duration of analgesia; for the latter analysis, trials were grouped in low (1-4 mg) and moderate (5-10 mg) dexamethasone doses. Secondary outcomes included the rate of neurologic complication and resting pain scores and morphine consumption within the first 24 hours. RESULTS: Thirty-three controlled trials, including 2138 patients, were identified. The meta-regression revealed a ceiling effect with a perineural dexamethasone dose of 4 mg when combined with short-/intermediate-acting (8 trials; 366 participants) or long-acting local anesthetics (23 trials; 1869 participants). This finding was confirmed by subgroup analyses comparing low and moderate dexamethasone doses. With short-/intermediate-acting local anesthetics, the mean difference (95% confidence interval) of analgesia duration with low and moderate doses was 277 (234-322) minutes and 229 (161-297) minutes, respectively. With long-acting local anesthetics, the mean differences with low and moderate doses were 505 (342-669) minutes and 509 (443-575) minutes. Perineural dexamethasone did not increase the rate of neurologic complications (risk ratio [95% confidence interval], 1.40 [0.54-3.63]). The Grades of Recommendation, Assessment, Development, and Evaluation quality of evidence for the primary and secondary outcomes were very low, due mainly to limitations, inconsistency, indirectness, and publication bias. CONCLUSIONS: There is currently very low quality evidence that 4 mg of perineural dexamethasone represents a ceiling dose that prolongs analgesia duration by a mean period of 6 and 8 hours when combined with short-/intermediate- or long-acting local anesthetics, respectively. Additional data are needed to explore the threshold for this effect, particularly with doses below 4 mg. The risk of neurologic complications is probably not increased (very low evidence).

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.030
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0180.045
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.086
GPT teacher head0.361
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

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Citations143
Published2017
Admission routes1
Has abstractyes

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