ArticleIEEE transactions on bio-medical engineering2025
Personalizing the Pressure Reactivity Index for Quantifying Cerebral Autoregulation in Neurocritical Care.
Article in IEEE transactions on bio-medical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Early inhaled isoflurane sedation in neurocritical patients with invasive intracranial pressure monitoring: the NEURO-CONDA randomized pilot trial.Critical care (London, England) · 2026Trial
- Toward comprehensive multimodal neuromonitoring of surrogates of cerebral autoregulation in traumatic brain injury: insights from a hyperventilation protocol.Neurophotonics · 2026Article
- Towards a physics informed digital twin to predict cerebral blood flow and cerebral vascular regulation.NPJ digital medicine · 2026Article
- Intracranial pressure management in severe intraventricular hemorrhage: A minireview.World journal of critical care medicine · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveThe Pressure Reactivity Index (PRx) is a common metric for assessing cerebral autoregulation in neurocritical care. This study aimed to enhance the clinical utility of PRx by developing a personalized PRx algorithm (pPRx) and identifying ideal hyperparameters.
methodsAlgorithmic errors were quantified using simulated data and multimodal monitoring data from traumatic brain injury patients from the Track-TBI dataset. Using linear regression, heart rate was identified as a potential cause of PRx error. The pPRx method was developed by reparameterizing PRx averaging to heartbeats. Ideal hyperparameters for the standard PRx algorithm were identified that minimized algorithmic errors.
resultsPRx was sensitive to hyperparameters and patient variability. Errors were related to patient heart rates. By parameterizing PRx to heartbeats, the pPRx methodology significantly reduced noise and sensitivity to both patient variability and hyperparameter selection. In the standard PRx algorithm, averaging windows of 10 seconds and correlation windows of 40 samples resulted in the lowest overall error.
conclusionPersonalized PRx enhances the robustness and accuracy of cerebral autoregulation estimation by addressing patient- and hyperparameter-sensitivity. This improvement is crucial for reliable clinical decision-making in neurocritical care. SIGNIFICANCE: Robust estimation of cerebral autoregulation would be beneficial for identifying precision medicine targets and improving outcomes for neurocritical care patients. We systematically increased the robustness of PRx to make it more consistent across patient populations.
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