Ketamine Infusion as a Single Sedative Agent for Post-Intubation Management of Critically Ill Patients: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
Introduction: Combining multiple drugs for intubation raises concerns such as increased side effects, medication errors, nursing workload, and costs. Ketamine, with its anesthetic and analgesic properties, shows promise as a sedative agent for post-intubation care. This study aimed to evaluate the efficacy and safety of ketamine infusion as the sole sedative for critically ill intubated patients. Methods: Following PRISMA 2020 guidelines, we conducted a systematic review by searching Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and Google Scholar up to May 10, 2024. We included studies assessing ketamine use for post-intubation sedation in critically ill adults or children. Study quality was assessed using the Newcastle-Ottawa scale, and meta-analysis was performed using a random-effects model. Results: The systematic review included 7 studies, with 4 studies included in the meta-analysis. There was no significant difference in mortality (OR = 1.52; 95% CI: 0.49-4.70, p = 0.46; I2 = 83%) or length of hospital stay (MD = 6.42; 95% CI: -1.42-14.26, p = 0.11; I2 = 84%) between the ketamine only and other groups. The most common adverse events in the ketamine infusion group were atrial fibrillation and agitation. Conclusion: Single-agent ketamine infusion is effective and safe for critically ill intubated patients. No significant differences were found in mortality or hospital stay between ketamine only and other groups. Atrial fibrillation and agitation were the most common adverse effects.
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 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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.008 | 0.007 |
| 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".