Near-Infrared Spectroscopy to Assess Cerebral Autoregulation and Optimal Mean Arterial Pressure in Patients With Hypoxic-Ischemic Brain Injury: A Prospective Multicenter Feasibility Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».