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Record W4402023463 · doi:10.1111/aas.14470

Ketamine analgo‐sedation for mechanically ventilated critically ill adults: A rapid practice guideline from the Saudi Critical Care Society and the Scandinavian Society of Anesthesiology and Intensive Care Medicine

2024· article· en· W4402023463 on OpenAlexaff
Marwa Amer, Morten Hylander Møller, Mohammed Alshahrani, Yahya Shehabi, Yaseen M. Arabi, Fayez Alshamsi, Martin I. Sigurðsson, Marius Rehn, Michelle S. Chew, Maija‐Liisa Kalliomäki, Kimberley Lewis, Faisal Al-Suwaidan, Hasan M. Al‐Dorzi, Abdulrahman Al‐Fares, Naif Alsadoon, Carolyn M. Bell, Christine M. Groth, Rachael Parke, Sangeeta Mehta, Paul E. Wischmeyer, Awad Al‐Omari, Klaus T. Olkkola, Waleed Alhazzani

Bibliographic record

VenueActa Anaesthesiologica Scandinavica · 2024
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsMount Sinai HospitalMcMaster UniversityImpactSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMedicineSedationAnesthesiologyGuidelineCritically illIntensive careKetamineIntensive care medicinePain medicineAnesthesia

Abstract

fetched live from OpenAlex

BACKGROUND: This Rapid Practice Guideline (RPG) aimed to provide evidence-based recommendations for ketamine analgo-sedation (monotherapy and adjunct) versus non-ketamine sedatives or usual care in adult intensive care unit (ICU) patients on invasive mechanical ventilation (iMV) and to identify knowledge gaps for future research. METHODS: The RPG panel comprised 23 multinational multidisciplinary panelists, including a patient representative. An up-to-date systematic review and meta-analysis constituted the evidence base. The Grading Recommendations, Assessment, Development, and Evaluation approach, and the evidence-to-decision framework were used to assess the certainty of evidence and to move from evidence to decision/recommendation. The panel provided input on the balance of the desirable and undesirable effects, certainty of evidence, patients' values and preferences, costs, resources, equity, feasibility, acceptability, and research priorities. RESULTS: Data from 17 randomized clinical trials (n = 898) and nine observational studies (n = 1934) were included. There was considerable uncertainty about the desirable and undesirable effects of ketamine monotherapy for analgo-sedation. The evidence was very low certainty and downgraded for risk of bias, indirectness, and inconsistency. Uncertainty or variability in values and preferences were identified. Costs, resources, equity, and acceptability were considered varied. Adjunctive ketamine therapy had no effect on mortality (within 28 days) (relative risk [RR] 0.99; 95% confidence interval [CI] 0.76 to 1.27; low certainty), and may slightly reduce iMV duration (days) (mean difference [MD] -0.05 days; 95% CI -0.07 to -0.03; low certainty), and uncertain effect on the cumulative dose of opioids (mcg/kg/h morphine equivalent) (MD -11.6; 95% CI -20.4 to -2.7; very low certainty). Uncertain desirable effects (cumulative dose of sedatives and vasopressors) and undesirable effects (adverse event rate, delirium, arrhythmia, hepatotoxicity, hypersalivation, use of physical restraints) were also identified. A possibility of important uncertainty or variability in patient-important outcomes led to a balanced effect that favored neither the intervention nor the comparison. Cost, resources, and equity were considered varied. CONCLUSION: The RPG panel provided two conditional recommendations and suggested (1) against using ketamine as monotherapy analgo-sedation in critically ill adults on iMV when other analgo-sedatives are available; and (2) using ketamine as an adjunct to non-ketamine usual care sedatives (e.g., opioids, propofol, dexmedetomidine) or continuing with non-ketamine usual care sedatives alone. Large-scale trials should provide additional 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)0.032
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.052
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0070.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.003
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.018
GPT teacher head0.317
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreOther

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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Citations4
Published2024
Admission routes1
Has abstractyes

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