Systematic review and network meta-analysis of various nadir temperature strategies for hypothermic circulatory arrest for aortic arch surgery
Bibliographic record
Abstract
BACKGROUND: The optimal nadir temperature for hypothermic circulatory arrest during aortic arch surgery remains unclear. We aimed to assess and compare clinical outcomes of all three temperature strategies (deep, moderate, and mild hypothermia) using a network meta-analysis. METHODS: After literature search with MEDLINE and EMBASE through December 2021, studies comparing clinical outcomes with deep (<20°C), moderate (20-28°C), or mild (>28°C) hypothermic circulatory arrest were included. The outcomes of interest were perioperative mortality, stroke, transient ischemia attack (TIA), acute kidney injury (AKI), postoperative bleeding, operative time, and length of hospital stay. RESULTS: Twenty-four comparative studies were identified, including 6018 patients undergoing aortic arch surgery using hypothermic circulatory arrest (deep: 2,978, moderate: 2,525, and mild: 515). Compared to deep hypothermia, mild and moderate hypothermia were associated with lower mortality (mild vs. deep: odds ratio [OR] 0.50; 95% confidence interval (CI) 0.29-0.87, moderate vs. deep: OR 0.68; 95% CI 0.54-0.86). In addition, mild hypothermia was associated with lower stroke (OR 0.50; 95% CI 0.28-0.89), AKI (OR 0.36; 95% CI 0.15-0.88) and postoperative bleeding (OR 0.55; 95% CI 0.31-0.97) compared to deep hypothermia. There was no significant difference between mild and moderate hypothermia in mortality, AKI or bleeding occurrence, while mild hypothermia was associated with shorter operative time and hospital stay. There was no significant difference in TIA rate among three groups. CONCLUSIONS: Mild hypothermia was associated with overall more favorable clinical outcomes with comparable neurological complications compared to deep hypothermia. Furthermore, considering the shorter operative time and hospital stay compared with moderate hypothermia, mild hypothermia may be warranted when appropriate adjunctive cerebral perfusion is employed.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".