Preoperative Visual Attention Performance Predicts Postoperative Delirium
Bibliographic record
Abstract
BACKGROUND: Preexisting cognitive impairment is a significant risk factor for post operative delirium (POD), and POD increases morbidity and mortality. Disturbances of attention (i.e., ability to direct, focus, sustain, and shift attention) and awareness (i.e., orientation to environment) are necessary for delirium diagnosis. This suggests dysfunction in frontoparietal networks which control visuospatial attention. However, preoperative visual attention has not been systematically investigated as a risk factor for delirium in at-risk adults. METHODS: In this prospective observational study, participants aged 65 years and older undergoing elective orthopedic surgery completed preoperative measures of visuospatial attention, including horizontal line bisections and a preparation of the Posner cueing task in vertical and horizontal orientations. Our primary outcome was maximum POD severity, as measured by the Confusion Assessment Method Severity (CAM-S) short form. Data were summarized (i.e., frequencies, mean (SD), and median [Q1, Q3]) and analyzed (i.e., Spearman correlational testing). RESULTS: The majority of the 28 participants were female (68%) and white (93%) with mean (SD) age of 75.0 (6.1) years of age and 15.6 (2.7) years of education. 75% were not delirious post-surgery, 11% were classified as subsyndromal (CAM-S > 0), and 14% were classified as having POD. The median [Q1, Q3] value for the delirium score was 0 [0, 1.5]. The median [Q1, Q3] values for cognitive measures were as follows: Montreal Cognitive Assessment (MoCA) was 24 [22,27], line bisection leftward deviation was 0.12 mm [-.40,0.11], incongruent stimulus reaction time was 550 ms [488,616]. Correlations between delirium and age, education, and MoCA score were not found to be significant. The correlation coefficient (p-value) between delirium and leftward deviation on line bisection was 0.39 (p = 0.038), and incongruent stimulus reaction time was 0.57 (p = 0.003). CONCLUSION: We found that preoperative measures of visual attention predicted postoperative delirium. Further studies can help to determine how to apply these measures to better predict and monitor the effects of delirium on cognition and neurodegeneration. Understanding this relationship also provides a basis for future research on strategies to prevent, mitigate or rehabilitate the effects of delirium, improving hospital outcomes and potentially delaying or preventing dementia. Funding AACSF-22-928731; K07AG066813.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".