Sleep disturbances in older surgical patients with and without suspected cognitive impairment: A multicenter cohort study
Bibliographic record
Abstract
AIMS: Early detection and management of sleep disturbances can improve postoperative outcomes given the high prevalence of sleep disturbances and unrecognized cognitive impairment in older surgical patients. There is an association between sleep disturbances and cognitive impairment in the general population. However, the relationship in older surgical patients has not been systematically investigated. The objective of this study was to assess the prevalence and trajectory of preoperative and postoperative sleep disturbances in older surgical participants with and without suspected cognitive impairment (sCI). METHODS: Two hundred and fifty-two participants aged ≥ 65 years undergoing non-cardiac surgery were recruited. The primary outcome was the prevalence and trajectory of sleep disturbances measured by the Pittsburgh Sleep Quality Index (PSQI) in participants with and without sCI preoperatively, 30, 90, and 180 days postoperatively. The main exposure, preoperative sCI, was operationalized as screening positive on one or more of the following cognitive screening tools: Centers for Disease Control and Prevention cognitive question (answered "yes"), Ascertain Dementia Eight-item Questionnaire (≥2), Telephone Montreal Cognitive Assessment (≤18), and Modified Telephone Interview for Cognitive Status (≤31). Sleep disturbances were defined as a PSQI score > 5. Mixed effects logistic regression models with random intercepts were used for the dichotomous outcome of sleep disturbances. RESULTS: One hundred and eight participants (43%) screened positive for preoperative sCI. The prevalence of preoperative sleep disturbances was higher in participants with sCI versus without (63% vs 47%, P = 0.02). Postoperatively in both groups, the prevalence of sleep disturbances was lower at 30, 90, and 180 days, compared to the preoperative assessment and overall trajectories did not differ significantly. Female sex and depression were associated with poorer postoperative sleep, regardless of cognitive status. CONCLUSION: Sleep disturbances and suspected cognitive impairment are highly prevalent in surgical cohorts. Targeting conditions such as depression which affect sleep, may improve postoperative outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".