Impact of cardiopulmonary bypass on cerebrovascular autoregulation assessed by ultrafast ultrasound imaging
Bibliographic record
Abstract
Abstract Newborns with congenital heart disease undergoing cardiac surgery are at risk of neurodevelopmental impairment with limited understanding of the impact of intra‐operative cardiopulmonary bypass (CPB), deep hypothermia and selective cerebral perfusion on the brain. We hypothesized that a novel ultrasound technique, ultrafast power Doppler (UPD), can assess variations of cerebral blood volume (CBV) in neonates undergoing cardiac surgery requiring CPB. UPD was performed before, during and after surgery in newborns with hypoplastic left heart syndrome undergoing a Norwood operation. We found that global CBV was not significantly different between patients and controls (P = 0.98) and between pre‐ and post‐surgery (P = 0.62). UPD was able to monitor changes in CBV throughout surgery, revealing regional differences in CBV during hypothermia during which CBV correlated with CPB flow rate (R2 = 0.52, P = 0.021). Brain injury on post‐operative magnetic resonance imaging was observed in patients with higher maximum variation in CBV. Our findings suggest that UPD can quantify global and regional brain perfusion variation during neonatal cardiac surgery with this first intra‐operative application demonstrating an association between CBV and CPB flow rate, suggesting loss of autoregulation. Therefore, the measurement of CBV by UPD could enable optimization of cerebral perfusion during cardiac surgery in neonates. image Key points The impact of cardiopulmonary bypass (CPB) on the neonatal brain undergoing cardiac surgery is poorly understood. Ultrafast power Doppler (UPD) quantifies cerebral blood volume (CBV), a surrogate of brain perfusion. CBV varies throughout CPB surgery and is associated with variation of the bypass pump flow rate during deep hypothermia. Association between CBV and bypass pump flow rate suggests loss of cerebrovascular autoregulatory processes. Quantitative monitoring of cerebral perfusion by UPD could provide a direct parameter to optimize CPB flow rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".