The Effects of Dexmedetomidine on Perioperative Neurocognitive Outcomes After Cardiac Surgery
Bibliographic record
Abstract
OBJECTIVE: The purpose of this systematic review and meta-analysis is to examine the effect of DEX on delayed dNCR (cognitive dysfunction ≥ 1 week postoperative) after cardiac surgery. BACKGROUND: DEX has salutary effects on cognitive outcomes following cardiac surgery, however, studies are limited by inconsistent assessment tools, timing, and definitions of dysfunction. It is imperative to identify accurate point estimates of effect of DEX on clinically relevant changes in cognitive function. METHODS: Randomized trials of adults undergoing cardiac surgery comparing perioperative DEX to placebo or alternate sedation and assessing cognitive function ≥ 1 week postoperative were included. Data was abstracted by three reviewers independently and in parallel according to PRISMA guidelines. The primary outcome is dNCR. To classify as dNCR, cognitive function must decrease by at least the minimal clinically important difference or accepted alternate measure (eg, Reliable Change Index ≥1.96). Bias was assessed with the Cochrane Collaboration tool. Data was pooled using a random effects model. RESULTS: Nine trials (942 participants) were included in qualitative analysis, of which seven were included in the meta-analysis of dNCR. DEX reduced the incidence of dNCR (OR 0.39, 95% CI 0.25-0.61, P < 0.0001) compared to placebo/no DEX. There was no difference in the incidence of delirium (OR 0.69, 95% CI 0.35-1.34, P = 0.27) or incidence of hemodynamic instability (OR 1.14, 95% CI 0.59-2.18, P = 0.70) associated with perioperative DEX. CONCLUSIONS: DEX reduced the incidence of dNCR 1 week after cardiac surgery. Although this meta-analysis demonstrates short term cognitive outcomes are improved after cardiac surgery with perioperative DEX, future trials examining long term cognitive outcomes, using robust cognitive assessments, and new perioperative neurocognitive disorders nomenclature with objective diagnostic criteria are necessary.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".