Pharmacological Risk Factors for Delirium after Cardiac Surgery: A Review
Bibliographic record
Abstract
PURPOSE: The objective of this review is to evaluate the literature on medications associated with delirium after cardiac surgery and potential prophylactic agents for preventing it. SOURCE: Articles were searched in MEDLINE, Cumulative Index to Nursing and Allied Health, and EMBASE with the MeSH headings: delirium, cardiac surgical procedures, and risk factors, and the keywords: delirium, cardiac surgery, risk factors, and drugs. Principle inclusion criteria include having patient samples receiving cardiac procedures on cardiopulmonary bypass, and using DSM-IV-TR criteria or a standardized tool for the diagnosis of delirium. PRINCIPAL FINDINGS: Fifteen studies were reviewed. Two single drugs (intraoperative fentanyl and ketamine), and two classes of drugs (preoperative antipsychotics and postoperative inotropes) were identified in the literature as being independently associated with delirium after cardiac surgery. Another seven classes of drugs (preoperative antihypertensives, anticholinergics, antidepressants, benzodiazepines, opioids, and statins, and postoperative opioids) and three single drugs (intraoperative diazepam, and postoperative dexmedetomidine and rivastigmine) have mixed findings. One drug (risperidone) has been shown to prevent delirium when taken immediately upon awakening from cardiac surgery. None of these findings was replicated in the studies reviewed. CONCLUSION: These studies have shown that drugs taken perioperatively by cardiac surgery patients need to be considered in delirium risk management strategies. While medications with direct neurological actions are clearly important, this review has shown that specific cardiovascular drugs may also require attention. Future studies that are methodologically consistent are required to further validate these findings and improve their utility.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".