Validation of Intracranial Pressure-Derived Cerebrovascular Reactivity Indices against the Lower Limit of Autoregulation, Part II: Experimental Model of Arterial Hypotension
Bibliographic record
Abstract
The aim of this work was to explore the relationship between intracranial pressure (ICP)-derived indices of cerebrovascular reactivity and the lower limit of autoregulation (LLA) during arterial hypotension. We retrospectively reviewed recorded physiological data from piglets that underwent controlled hypotension. Hypotension was induced by inflation of a balloon catheter in the inferior vena cava. ICP, cortical laser Doppler flowmetry (LDF), and arterial blood pressure (ABP) monitoring was conducted. ICP-derived indices were calculated: pressure reactivity index (PRx; correlation between ICP and mean arterial pressure [MAP]); pulse amplitude index (PAx; correlation between pulse amplitude of ICP [AMP] and MAP); and RAC (correlation between AMP and cerebral perfusion pressure [CPP]). LLA was estimated by piece-wise linear regression of CPP versus LDF. We produced error bar plots for PRx, PAx, and RAC against 5-mm Hg bins of CPP, displaying the relationship with the LLA. We compared CPP values at clinically relevant thresholds of PRx, PAx, and RAC to CPP measured at the LLA. Receiver operating curve (ROC) analysis was performed for each index across the LLA using 5-mm Hg bins for CPP. Mean LLA was 36.2 ± 10.5 mm Hg. Error bar plots demonstrated that PRx, PAx, and RAC increased, with CPP decreasing below the LLA. CPP at clinically relevant thresholds for PRx, PAx, and RAC displayed weak associations with the LLA, indicating that thresholds defined in TBI may not apply to a model of arterial hypotension. ROC analysis indicated that PRx, PAx, and RAC predicted the LLA, with AUCs of: 0.806 (95% confidence interval [CI], 0.750-0.863; p < 0.0001), 0.726 (95% CI, 0.664-0.789; p < 0.0001), and 0.710 (95% CI, 0.646-0.775; p < 0.0001), respectively. Three ICP-derived continuous indices of cerebrovascular reactivity, PRx, PAx, and RAC, were validated against the LLA within this experimental model of arterial hypotension, with PRx being superior.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".