Clinical Practice: Should we Radically Alter our Sedation of Critical Care Patients, Especially Given the COVID-19 Pandemics?
Bibliographic record
Abstract
The high number of patients infected with the SARS-CoV-2 virus requiring care for ARDS puts sedation in the critical care unit (CCU) to the edge. Depth of sedation has evolved over the last 40 years (no-sedation, deep sedation, daily emergence, minimal sedation, etc.). Most guidelines now recommend determining the depth of sedation and minimizing the use of benzodiazepines and opioids. The broader use of alpha-2 adrenergic agonists ('alpha-2 agonists') led to sedation regimens beginning at admission to the CCU that contrast with hypnotics+opioids ("conventional" sedation), with major consequences for cognition, ventilation and circulatory performance. The same doses of alpha-2 agonists used for 'cooperative' sedation (ataraxia, analgognosia) elicit no respiratory depression but modify the autonomic nervous system (cardiac parasympathetic activation, attenuation of excessive cardiac and vasomotor sympathetic activity). Alpha-2 agonists should be selected only in patients who benefit from their effects ('personalized' indications, as opposed to a 'one size fits all' approach). Then, titration to effect is required, especially in the setting of systemic hypotension and/or hypovolemia. Since no general guidelines exist for the use of alpha-2 agonists for CCU sedation, our clinical experience is summarized for the benefit of physicians in clinical situations in which a recommendation might never exist (refractory delirium tremens; unstable, hypovolemic, hypotensive patients, etc.). Because the physiology of alpha-2 receptors and the pharmacology of alpha-2 agonists lead to personalized indications, some details are offered. Since interactions between conventional sedatives and alpha-2 agonists have received little attention, these interactions are addressed. Within the existing guidelines for CCU sedation, this article could facilitate the use of alpha-2 agonists as effective and safe sedation while awaiting large, multicentre trials and more evidence-based medicine.
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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 | 0.014 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".