Near-Infrared Spectroscopy to Assess Cerebral Autoregulation and Optimal Mean Arterial Pressure in Patients With Hypoxic-Ischemic Brain Injury: A Prospective Multicenter Feasibility Study
Bibliographic record
Abstract
We provide preliminary multicenter data to suggest that recruitment and collection of physiologic data necessary to quantify cerebral autoregulation and individualized blood pressure targets are feasible in postcardiac arrest patients. We evaluated the feasibility of a multicenter protocol to enroll patients across centers, as well as collect continuous recording (≥ 80% of monitoring time) of regional cerebral oxygenation and mean arterial pressure, which is required to quantify cerebral autoregulation, using the cerebral oximetry index, and individualized optimal mean arterial pressure thresholds. Additionally, we conducted an exploratory analysis to assess if an increased percentage of monitoring time where mean arterial pressure was greater than or equal to 5 mm Hg below optimal mean arterial pressure, percentage of monitoring time with dysfunctional cerebral autoregulation (i.e., cerebral oximetry index ≥ 0.3), and time to return of spontaneous circulation were associated with an unfavorable neurologic outcome (i.e., 6-mo Cerebral Performance Category score ≥ 3). DESIGN SETTING AND PATIENTS: A prospective multicenter cohort study was conducted in ICUs in three teaching hospitals across Canada. Patients (≥ 16 yr old) were included if their cardiac arrest occurred within the previous 36 hours, they had greater than or equal to 20 consecutive minutes of spontaneous circulation following resuscitation, and they had a post-resuscitation Glasgow Coma Scale of less than or equal to 8. MEASUREMENTS AND MAIN RESULTS: Recruitment rates were calculated across sites, and patients underwent continuous regional cerebral oxygenation monitoring using near-infrared spectroscopy, as well as invasive blood pressure monitoring. Exploratory multivariable logistic regression was performed. Although it was feasible to recruit patients across multiple centers, there was variability in the recruitment rates. Physiologic data were captured in 86.2% of the total monitoring time and the median monitoring time was 47.5 hours (interquartile interval, 29.4-65.0 hr) across 59 patients. Specifically, 88% of mean arterial pressure and 96% of bilateral frontal regional cerebral oxygenation data were acquired, and 90% of cerebral oximetry index and 70% of optimal mean arterial pressure values were quantified. However, there was substantial variation in the amount of data captured among individuals. Time to return of spontaneous circulation was associated with an increased odds of an unfavorable neurologic outcome. CONCLUSIONS AND RELEVANCE: We demonstrated feasibility to recruit and collect high frequency physiologic data in patients after cardiac arrest. Future investigations will need to systematically document the reasons for data attrition, as well as how these methodological complications were resolved. Due to underpowered analyses and the inability to control for potential confounds, further studies are needed to explore the association between cerebral autoregulatory capacity and individualized mean arterial pressure thresholds with neurologic 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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".