Current Perceptions and Practices Surrounding the Recognition and Treatment of Delirium in the Intensive Care Unit: A Survey of 250 Critical Care Pharmacists from Eight States
Bibliographic record
Abstract
BACKGROUND: Pharmacists are key members of the intensive care unit (ICU) team; however, few data exist regarding their clinical role, perceptions, and current practices in recognizing and managing delirium. OBJECTIVE: To describe current practices and perceptions of ICU pharmacists regarding delirium recognition and treatment relative to current recommendations. METHODS: A self-administered survey was distributed to 457 pharmacists in 8 states who are members of the Society of Critical Care Medicine or the American College of Clinical Pharmacy and who spend 25% or more of their time providing clinical ICU pharmacy services. RESULTS: A total of 250 (55%) pharmacists responded. A delirium screening tool was routinely used by few (7%) pharmacists. Lack of time (34%) and the belief that screening is a nursing role (24%) were key barriers to pharmacist screenings. Most (85%) said that delirium should be pharmacologically managed; 68% responded that 2 or more medications should be used. The treatments of first choice included haloperidol (76%), an atypical antipsychotic (14%), or a benzodiazepine (10%). Frequently used treatments were haloperidol (87%), quetiapine (59%), and lorazepam (47%). Haloperidol was perceived by many (42%) to have 1 or more randomized trials supporting its use for delirium and Food and Drug Administration approval for this indication (34%). Haloperidol was most often administered on a scheduled basis (62%), intravenously (92%), and at a daily dose of 5-10 mg (58%). While the QTc interval was frequently measured at least once per shift using an electrocardiogram strip (64%), it was not routinely measured in 20% of ICUs, and 60% continued haloperidol when the QTc exceeded 500 msec. CONCLUSIONS: Current practices and perceptions surrounding recognition and treatment of delirium in patients in the ICU by the critical care pharmacists surveyed are heterogeneous. Antipsychotics are frequently recommended by pharmacists for delirium treatment, despite a lack of rigorous evidence to support their use. While pharmacists are ideally suited to lead delirium recognition efforts and provide treatment recommendations in this area, these roles need further elucidation. The optimal pedagogical strategy to support these efforts remains unclear, and the potential impact of pharmacists' efforts on patients' outcomes is unknown.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".