Optimizing sedation in patients with acute brain injury
Bibliographic record
Abstract
Daily interruption of sedative therapy and limitation of deep sedation have been shown in several randomized trials to reduce the duration of mechanical ventilation and hospital length of stay, and to improve the outcome of critically ill patients. However, patients with severe acute brain injury (ABI; including subjects with coma after traumatic brain injury, ischaemic/haemorrhagic stroke, cardiac arrest, status epilepticus) were excluded from these studies. Therefore, whether the new paradigm of minimal sedation can be translated to the neuro-ICU (NICU) is unclear. In patients with ABI, sedation has 'general' indications (control of anxiety, pain, discomfort, agitation, facilitation of mechanical ventilation) and 'neuro-specific' indications (reduction of cerebral metabolic demand, improved brain tolerance to ischaemia). Sedation also is an essential therapeutic component of intracranial pressure therapy, targeted temperature management and seizure control. Given the lack of large trials which have evaluated clinically relevant endpoints, sedative selection depends on the effect of each agent on cerebral and systemic haemodynamics. Titration and withdrawal of sedation in the NICU setting has to be balanced between the risk that interrupting sedation might exacerbate brain injury (e.g. intracranial pressure elevation) and the potential benefits of enhanced neurological function and reduced complications. In this review, we provide a concise summary of cerebral physiologic effects of sedatives and analgesics, the advantages/disadvantages of each agent, the comparative effects of standard sedatives (propofol and midazolam) and the emerging role of alternative drugs (ketamine). We suggest a pragmatic approach for the use of sedation-analgesia in the NICU, focusing on some practical aspects, including optimal titration and management of sedation withdrawal according to ABI severity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".