<p>Physical Performance and Risk of Postoperative Delirium in Older Adults Undergoing Aortic Valve Replacement</p>
Bibliographic record
Abstract
BACKGROUND: Delirium is a major risk factor for poor recovery after surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR). It is unclear whether preoperative physical performance tests improve delirium prediction. OBJECTIVE: To examine whether physical performance tests can predict delirium after SAVR and TAVR, and adapt an existing delirium prediction rule for cardiac surgery, which includes Mini-Mental State Examination (MMSE), depression, prior stroke, and albumin level. DESIGN: Prospective cohort, 2014-2017. SETTING: Single academic center. SUBJECTS: A total of 187 patients undergoing SAVR (n=77) or TAVR (n=110). METHODS: The Short Physical Performance Battery (SPPB) score was calculated based on gait speed, balance, and chair stands (range: 0-12 points, lower scores indicate poor performance). Delirium was assessed using the Confusion Assessment Method. We fitted logistic regression to predict delirium using SPPB components and risk factors of delirium. RESULTS: Delirium occurred in 35.8% (50.7% in SAVR and 25.5% in TAVR). The risk of delirium increased for lower SPPB scores: 10-12 (28.2%), 7-9 (34.5%), 4-6 (37.5%) and 0-3 (44.1%) (p-for-trend=0.001). A model that included gait speed <0.46 meter/second (OR, 2.7; 95% CI, 1.2-6.4), chair stands time ≥11.2 seconds (OR, 3.5; 95% CI, 1.0-12.4), MMSE <24 points (OR, 2.9; 95% CI, 1.3-6.4), isolated SAVR (OR, 5.4; 95% CI, 2.1-13.8), and SAVR and coronary artery bypass grafting (OR, 15.8; 95% CI, 5.5-45.7) predicted delirium better than the existing prediction rule (C statistics: 0.71 vs 0.61; p=0.035). CONCLUSION: Assessing physical performance, in addition to cognitive function, can help identify high-risk patients for delirium after SAVR and TAVR.
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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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".