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An open source autoregulation-based neuromonitoring algorithm shows PRx and optimal CPP association with pediatric
Eris van Twist1, Tahisa B Robles2, Bart Formsma2
1Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands. e.vantwist@erasmusmc.nl.
Insights
An open-source algorithm for pressure-reactivity index (PRx) aids in monitoring cerebral autoregulation (CA) in pediatric severe traumatic brain injury (sTBI). This tool helps identify optimal cerebral perfusion pressure (CPPopt) targets, improving long-term outcomes in children.
Area of Science:
- Neuroscience
- Pediatric Critical Care
- Biomedical Engineering
Background:
- Pediatric severe traumatic brain injury (sTBI) poses significant challenges in neuromonitoring.
- Cerebral autoregulation (CA) is crucial for maintaining adequate cerebral blood flow in pediatric patients.
- Current methods for monitoring CA and optimizing cerebral perfusion pressure (CPP) in children have limitations.
Purpose of the Study:
- To develop and validate an open-source algorithm for calculating the pressure-reactivity index (PRx).
- To assess the utility of the derived optimal cerebral perfusion pressure (CPPopt) in relation to real-time CPP and long-term outcomes in pediatric sTBI.
- To provide a novel, accessible tool for pediatric neuromonitoring.
Main Methods:
- Retrospective analysis of intracranial pressure (ICP) and mean arterial pressure data from pediatric patients (<18 years) with sTBI.
- Calculation of PRx using Pearson correlation between ICP and mean arterial pressure.
- Derivation of CPPopt as a weighted average of CPP-PRx over time.
- Correlation of PRx and CPPopt with one-year outcomes using logistic regression and mixed-effect models.
Main Results:
- The developed algorithm successfully derived CPPopt for a significant portion of monitoring time (75.4%).
- Elevated PRx and suboptimal CPPopt were significantly associated with unfavorable long-term outcomes (PCPC 4-6).
- Specific PRx thresholds demonstrated a clear correlation with adverse outcomes, indicating their predictive value.
Conclusions:
- The open-source PRx algorithm provides a valuable tool for monitoring CA in pediatric sTBI.
- Derived CPPopt targets are associated with long-term neurological outcomes in this population.
- This algorithm offers a promising approach for optimizing neuromonitoring and improving patient management in pediatric neurocritical care.
Abstract:
This study aimed to develop an open-source algorithm for the pressure-reactivity index (PRx) to monitor cerebral autoregulation (CA) in pediatric severe traumatic brain injury (sTBI) and compared derived optimal cerebral perfusion pressure (CPPopt) with real-time CPP in relation to long-term outcome. Retrospective study in children (< 18 years) with sTBI admitted to the pediatric intensive care unit (PICU) for intracranial pressure (ICP) monitoring between 2016 and 2023. ICP was analyzed on an insult basis and correlated with outcome. PRx was calculated as Pearson correlation coefficient between ICP and mean arterial pressure. CPPopt was derived as weighted average of CPP-PRx over time. Outcome was determined via Pediatric Cerebral Performance Category (PCPC) scale at one year post-injury. Logistic regression and mixed effect models were developed to associate PRx and CPPopt with outcome. In total 50 children were included, 35 with favorable (PCPC 1-3) and 15 with unfavorable outcome (PCPC 4-6). ICP insults correlated with unfavorable outcome at 20 mmHg for 7 min duration. Mean CPPopt yield was 75.4% of monitoring time. Mean and median PRx and CPPopt yield associated with unfavorable outcome, with odds ratio (OR) 2.49 (1.38-4.50), 1.38 (1.08-1.76) and 0.95 (0.92-0.97) (p < 0.001). PRx thresholds 0.0, 0.20, 0.25 and 0.30 resulted in OR 1.01 (1.00-1.02) (p < 0.006). CPP in optimal range associated with unfavorable outcome on day one (0.018, p = 0.029) and four (-0.026, p = 0.025). Our algorithm can obtain optimal targets for pediatric neuromonitoring that showed association with long-term outcome, and is now available open source.
