Personalizing the Pressure Reactivity Index for Quantifying Cerebral Autoregulation in Neurocritical Care.

OBJECTIVE

The 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.

METHODS

Algorithmic 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.

RESULTS

PRx 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.

CONCLUSION

Personalized 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.