Understanding the use of sedation boluses in the intensive care unit: A mixed methods study
Bibliographic record
Abstract
BACKGROUND: To better understand the impact of sedations in the intensive care unit (ICU), an accurate understanding of the clinical practices surrounding sedation bolus use is essential. OBJECTIVES: Our primary objectives were to describe how sedation boluses are administered, and to compare observed and nurse-reported practices. METHODS: We conducted a mixed methods study comprising 150 quantitative observations in a large university-affiliated ICU and 10 semi-structured interviews with nurses recruited in the same ICU and from others in the Province of Quebec (Canada). RESULTS: During 150 observations, nurses administered 197 boluses. Nurses mostly administered boluses with a volumetric pump (76 %, 95 %CI 69-81 %). In interviews, all nurses expressed favoring volumetric pumps to administer boluses. Nurses documented bolus use in 58 % of observations (58 %, 95 %CI 50-66 %). Propofol and fentanyl were the most frequently used drugs, and all nurses reported that they were the "classic" bolus drugs. The median cumulative propofol and opioid bolus doses given by nurses were respectively 30 mg (95 %CI 25-30), and 50 µg in fentanyl-equivalent (95 %CI 50-50). We observed that nursing or medical interventions were the most common trigger for bolus use (63 %, 95 %CI 55-71 %), and these were among the main reason for bolus use reported in interviews (n = 9, 90 %). Increasing norepinephrine was observed (19 %, 95 %CI 13-26 %) and reported by all nurses as the most frequent interventions after boluses. CONCLUSIONS: Nurses favor volumetric pumps to administer boluses and propofol and fentanyl were the most used drugs. Documentation of boluses was suboptimal. Sedation boluses often led to norepinephrine titration. IMPLICATIONS FOR CLINICAL PRACTICE: Sedation boluses administered to patients with continuous infusions of sedations often led to norepinephrine titration, suggesting that they may have implications for patient safety and outcomes. Merging electronic health records entries and volumetric pump data logs or data feeds may be essential to properly capture the exposure of ICU patients to sedation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".