The incidence of post-operative delirium among elderly patients and its associated factors
Bibliographic record
Abstract
Background: Longer life span leads to an increase in elderly patients coming for surgery. The incidence of delirium is reported to be the highest in this population. This study was the first in Malaysia to investigate the incidence and predicting factors for post-operative delirium (POD) among local elderly patients undergoing various types of surgery. Methods: A prospective cohort study was conducted on surgical patients aged 65 years and above who underwent surgery at Hospital Universiti Sains Malaysia (HUSM). Cognitive and frailty statuses were assessed pre-operatively using the Malay version of Montreal Cognitive Assessment (MMoCA) and the Frailty Index for Elderly (FIFE), respectively. Postoperatively, patients were evaluated for the development of POD twice a day for up to 5 days or until discharged. Results: A total of 153 patients were recruited. The incidence of POD was 18.3% (95% CI: 12.5% 25.4%). Factors found to be significantly associated with POD on multivariate analysis were FIFE score (adjusted odds ratio (AOR): 1.568; 95% CI: 1.16 2.10; p = 0.003), METS < 4 (AOR: 3.228; 95% CI: 1.01 10.31; p = 0.048), and presence of intra operative hypotension (AOR: 7.687; 95% CI: 2.69 21.98; p = <0.001). New York Heart Association (NYHA) class, type of anaesthesia, duration of anaesthesia and surgery, estimated blood loss, need for intraoperative transfusion, and amount transfused were only significant on univariable analysis. All of our patients had complete resolution of POD, with a median duration of two days. Conclusion: The incidence of POD among elderly patients coming for both emergency and elective surgeries is high. Patients at higher risk for POD should be identified. This allows precautionary steps to be taken to prevent the development of POD and ensure primary teams are more vigilant post-operatively to detect and treat POD earlier.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".