The impact of preoperative alcohol use screening on postoperative delirium in cardiac surgery patients: a retrospective observational study
Bibliographic record
Abstract
Background: As cardiac surgery patients continue to age and present with more complex health conditions, the occurrence of a challenging postoperative complication known as "delirium" is becoming increasingly common. Postoperative delirium (PoD) is a complex neuropsychological disorder characterized by symptoms such as inattention, drowsiness, and agitation, which has many long-term negative health impacts. A patient-associated risk factor of PoD is presumed to be a preoperative alcohol use disorder (AUD). Objective: The primary purpose was to determine whether preoperative alcohol use, measured using the Alcohol Use Disorders Identification Test - Concise (AUDIT-C), is predictive of incident PoD in cardiac surgical patients. The secondary objective was to determine the risk factors associated with PoD available in the Manitoba Access Cardiac Surgery (MACS) database. Hypothesis: Primary: Higher AUDIT-C scores are associated with the incidence of PoD in adults who underwent cardiac surgery. Secondary: risk factors identified from the MACS database are correlated with PoD in adults who underwent cardiac surgery. Methods: This was a single-centre, retrospective observational cohort study. AUDIT-C score and PoD (as diagnosed using the Confusion Assessment Method (CAM) – ICU in the intensive care unit or CAM on the postoperative ward) were collected for all elective patients undergoing cardiac surgery between March 2015 – September 2020. Results: The overall incidence of delirium in this study was 14.2%. There was an association between preoperative alcohol use measured by AUDIT-C and PoD (0.559 OR, 0.515-0.607 95% CI, p = < 0.001) before controlling for covariates. After controlling for age, procedure category, MoCA score, CFS, LVEF category, renal insufficiency, previous CVA/TIA, recreational drug use and CPB time, there was no significant association between alcohol use and PoD (0.376 OR, 0.070-2.011 95% CI, p = 0.253). Previously known risk factors, including age, frailty, MoCA scores, renal insufficiency, previous CVA/TIA and CPB time, were found to be significant. Conclusion: Preoperative alcohol use measured by AUDIT-C is not a reliable predictor of PoD in cardiac surgery patients. Future research should explore the role of comprehensive measures of alcohol use on PoD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".