Optical monitoring of cerebral perfusion and metabolism in adults during cardiac surgery with cardiopulmonary bypass
Bibliographic record
Abstract
During cardiac surgery with cardiopulmonary bypass (CPB), adequate maintenance of cerebral blood flow (CBF) is vital in preventing postoperative neurological injury - i.e. stroke, delirium, cognitive impairment. Reductions in CBF large enough to impact cerebral energy metabolism can lead to tissue damage and subsequent brain injury. Current methods for neuromonitoring during surgery are limited. This study presents the clinical translation of a hybrid optical neuromonitor for continuous intraoperative monitoring of cerebral perfusion and metabolism in ten patients undergoing non-emergent cardiac surgery with non-pulsatile CPB. The optical system combines broadband near-infrared spectroscopy (B-NIRS) to measure changes in the oxidation state of cytochrome c oxidase (oxCCO) - a direct marker of cellular energy metabolism - and diffuse correlation spectroscopy (DCS) to provide an index of cerebral blood flow (CBFi). As the heart was arrested and the CPB-pump started, increases in CBFi (88.5 ± 125.7%) and significant decreases in oxCCO (-0.5 ± 0.2 µM) were observed; no changes were noted during transitions off CPB. Fifteen hypoperfusion events, defined as large and sustained reductions in CPB-pump flow rate, were identified across all patients and resulted in significant decreases in perfusion and metabolism when mean arterial pressure dropped to 30 mmHg or below. The maximum reduction in cerebral blood flow preceded the corresponding metabolic reduction by 18.2 ± 15.0 s. Optical neuromonitoring provides a safe and non-invasive approach for assessing intraoperative perfusion and metabolism and has potential in guiding patient management to prevent adverse clinical outcomes.
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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) | 0.000 | 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".