Cerebral multimodality monitoring in adult neurocritical care patients with acute brain injury: A narrative review
Bibliographic record
Abstract
Cerebral multimodality monitoring (MMM) is, even with a general lack of Class I evidence, increasingly recognized as a tool to support clinical decision-making in the neuroscience intensive care unit (NICU). However, literature and guidelines have focused on unimodal signals in a specific form of acute brain injury. Integrating unimodal signals in multiple signal monitoring is the next step for clinical studies and patient care. As such, we aimed to investigate the recent application of MMM in studies of adult patients with traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), intracerebral hemorrhage (ICH), acute ischemic stroke (AIS), and hypoxic ischemic brain injury following cardiac arrest (HIBI). We identified continuous or daily updated monitoring modalities and summarized the monitoring setting, study setting, and clinical characteristics. In addition, we discussed clinical outcome in intervention studies. We identified 112 MMM studies, including 11 modalities, over the last 7 years (2015–2022). Fifty-eight studies (52%) applied only two modalities. Most frequently combined were ICP monitoring (92 studies (82%)) together with PbtO 2 (63 studies (56%). Most studies included patients with TBI (59 studies) or SAH (53 studies). The enrollment period of 34 studies (30%) took more than 5 years, whereas the median sample size was only 36 patients (q1- q3, 20–74). We classified studies as either observational (68 studies) or interventional (44 studies). The interventions were subclassified as systemic (24 studies), cerebral (10 studies), and interventions guided by MMM (11 studies). We identified 20 different systemic or cerebral interventions. Nine (9/11, 82%) of the MMM-guided studies included clinical outcome as an endpoint. In 78% (7/9) of these MMM-guided intervention studies, a significant improvement in outcome was demonstrated in favor of interventions guided by MMM. Clinical outcome may be improved with interventions guided by MMM. This strengthens the belief in this application, but further interdisciplinary collaborations are needed to overcome the heterogeneity, as illustrated in the present review. Future research should focus on increasing sample sizes, improved data collection, refining definitions of secondary injuries, and standardized interventions. Only then can we proceed with complex outcome studies with MMM-guided treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".