Effects of blood pressure on brain metabolism during cardiopulmonary bypass
Bibliographic record
Abstract
Cardiac surgery with cardiopulmonary bypass (CPB) is associated with postoperative neurological complications. Targeted mean arterial blood pressure (MAP) during cardiac surgery is used as one method of maintaining adequate cerebral blood flow (CBF) and perfusion pressure. However, an MAP target of 60 mmHg after transitioning on CPB, which is used in many centers, does not account for the reported broad range of lower autoregulatory limits (50-90 mmHg) [1]. In an effort to maintain cerebral perfusion, near-infrared spectroscopy (NIRS) is used to monitor tissue oxygen saturation (StO2); however, StO2 is not a direct marker of CBF or tissue oxygen demand. As an alternative, possible effects on cerebral energy metabolism could be monitored by using hyperspectral NIRS (hsNIRS) to measure changes in the redox state of cytochrome c oxidase (ΔoxCCO), which are linked to ATP production. In this study, an in-house built hsNIRS/diffuse correlation spectroscopy (DCS) was used to monitor ΔoxCCO, CBF and StO2 in patients during cardiac surgery with CPB. Fourteen patients were retrospectively grouped according to the level of their MAP when transitioning onto CPB: high (70-90 mmHg), target (57-69 mmHg), and low MAP (40-56 mmHg). The aim was to evaluate the potential effects of MAP on ΔoxCCO during the transition onto CPB. Results demonstrated that the smallest changes in oxCCO (-0.08 ± 0.24 μM) were observed in the high MAP group and significantly larger changes (-0.73 ± 0.25 μM) in the low MAP group. The results highlight the potential of ΔoxCCO monitoring for real-time assessment of MAP management during CPB with the ultimate aim of mitigating adverse cerebral events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".