Optimal mean arterial pressure and its objective statistical associations with clinical outcomes and multimodal monitoring cerebral physiology: A systematic scoping review
Bibliographic record
Abstract
Optimal mean arterial pressure (MAPopt), also known as optimal arterial blood pressure (ABPopt), represents a patient-specific blood pressure range at which cerebral autoregulation is most intact. To date, literature on this personalized physiological target remains heterogeneous, scattered, and difficult to follow. This scoping review, following PRISMA-ScR guidelines, examined studies that explored objective statistical associations between MAPopt and clinical outcomes and multimodal monitoring (MMM) cerebral physiology. Fifteen articles met the inclusion criteria for studies investigating the relationship between MAPopt and outcome, including seven neonatal studies, five cardiac arrest/surgery studies, two general ICU (Intensive Care Unit) studies, and one neurological ICU study. Fourteen of the fifteen studies found that maintaining blood pressure above or within MAPopt was linked to improved neurological outcomes, while pressures below MAPopt correlated with worse outcomes. In neonates with hypoxic-ischemic encephalopathy, deviations below MAPopt were associated with more severe brain injury. Similarly, cardiac arrest patients spending more time below MAPopt-5 mmHg had higher mortality. Only one study evaluated MAPopt in relation to MMM data, identifying a nonlinear relationship between brain oxygenation and MAP deviation. This review underscores the need for further standardized research on MAPopt, particularly its interaction with MMM, to support its application in personalized critical care.
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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 | 0.015 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".