Delirium in the Postoperative Cardiac Patient: A Review
Bibliographic record
Abstract
UNLABELLED: BACKGROUND AND AIM OF REVIEW: Cardiac surgery is increasingly common and relatively safe, but there are frequent reports of neuropsychiatric sequelae occurring in the postoperative period. One of the most common neuropsychiatric presentations of cardiac surgery is delirium, also called postcardiotomy delirium (PCD). Despite the vast numbers of cardiac surgeries performed today, there is a paucity of data on risk factors and management options of PCD available to the clinician. This review aims to summarize available information, increase clinicians' awareness of PCD and suggest effective management of this illness. METHODS: Our literature search was completed using the databases Medline and CINAHL; it was limited to human and English language studies from 1964 to the present. Search terms included "delirium," "agitation," "postoperative," "cardiac," "neuropsychiatric," "neuroleptics," "psychosis," "surgery," "treatment," "postcardiotomy," and "pharmacotherapy." RESULTS: Our review of the literature revealed several risk factors for PCD, as well as various options for its pharmacological management. CONCLUSIONS: A multifactorial model should be applied when considering risk stratification for and prevention of delirium postoperatively. Pharmacologically, conventional antipsychotic agents, such as haloperidol, have long been used to manage delirium. In light of haloperidol's side effects, particularly those applicable to the cardiac patient, further research is required into the role of second generation antipsychotics. These agents are common in clinical use, and may be the preferred medications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".