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Stroh JN, Foreman B, Bennett TD, Briggs JK, Park S, Albers DJ. Intracranial pressure-flow relationships in traumatic brain injury patients expose gaps in the tenets of models and pressure-oriented management. Front Physiol 2024; 15:1381127. [PMID: 39189028 PMCID: PMC11345185 DOI: 10.3389/fphys.2024.1381127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 06/28/2024] [Indexed: 08/28/2024] Open
Abstract
Background: The protocols and therapeutic guidance established for treating traumatic brain injury (TBI) in neurointensive care focus on managing cerebral blood flow (CBF) and brain tissue oxygenation based on pressure signals. The decision support process relies on assumed relationships between cerebral perfusion pressure (CPP) and blood flow, pressure-flow relationships (PFRs), and shares this framework of assumptions with mathematical intracranial hemodynamics models. These foundational assumptions are difficult to verify, and their violation can impact clinical decision-making and model validity. Methods: A hypothesis- and model-driven method for verifying and understanding the foundational intracranial hemodynamic PFRs is developed and applied to a novel multi-modality monitoring dataset. Results: Model analysis of joint observations of CPP and CBF validates the standard PFR when autoregulatory processes are impaired as well as unmodelable cases dominated by autoregulation. However, it also identifies a dynamical regime -or behavior pattern-where the PFR assumptions are wrong in a precise, data-inferable way due to negative CPP-CBF coordination over long timescales. This regime is of both clinical and research interest: its dynamics are modelable under modified assumptions while its causal direction and mechanistic pathway remain unclear. Conclusion: Motivated by the understanding of mathematical physiology, the validity of the standard PFR can be assessed a) directly by analyzing pressure reactivity and mean flow indices (PRx and Mx) or b) indirectly through the relationship between CBF and other clinical observables. This approach could potentially help to personalize TBI care by considering intracranial pressure and CPP in relation to other data, particularly CBF. The analysis suggests a threshold using clinical indices of autoregulation jointly generalizes independently set indicators to assess CA functionality. These results support the use of increasingly data-rich environments to develop more robust hybrid physiological-machine learning models.
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Affiliation(s)
- J. N. Stroh
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Department of Bioengineering, University of Colorado Denver |Anschutz Medical Campus, Denver, CO, United States
| | - Brandon Foreman
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati, Cincinnati, OH, United States
- Gardner Neuroscience Institute, University of Cincinnati, Cincinnati, OH, United States
| | - Tellen D. Bennett
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Pediatric Intensive Care, Children’s Hospital of Colorado, Aurora, CO, United States
| | - Jennifer K. Briggs
- Department of Bioengineering, University of Colorado Denver |Anschutz Medical Campus, Denver, CO, United States
| | - Soojin Park
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
- Department of Neurology, New York Presbyterian/Columbia University Irving Medical Center, New York, NY, United States
| | - David J. Albers
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Department of Bioengineering, University of Colorado Denver |Anschutz Medical Campus, Denver, CO, United States
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
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Stroh JN, Foreman B, Bennett TD, Briggs JK, Park S, Albers DJ. Intracranial pressure-flow relationships in traumatic brain injury patients expose gaps in the tenets of models and pressure-oriented management. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.17.24301445. [PMID: 38293069 PMCID: PMC10827274 DOI: 10.1101/2024.01.17.24301445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Background The protocols and therapeutic guidance established for treating traumatic brain injuries (TBI) in neurointensive care focus on managing cerebral blood flow (CBF) and brain tissue oxygenation based on pressure signals. The decision support process relies on assumed relationships between cerebral perfusion pressure (CPP) and blood flow, pressure-flow relationships (PFRs), and shares this framework of assumptions with mathematical intracranial hemodynamic models. These foundational assumptions are difficult to verify, and their violation can impact clinical decision-making and model validity. Method A hypothesis- and model-driven method for verifying and understanding the foundational intracranial hemodynamic PFRs is developed and applied to a novel multi-modality monitoring dataset. Results Model analysis of joint observations of CPP and CBF validates the standard PFR when autoregulatory processes are impaired as well as unmodelable cases dominated by autoregulation. However, it also identifies a dynamical regime -or behavior pattern- where the PFR assumptions are wrong in a precise, data-inferable way due to negative CPP-CBF coordination over long timescales. This regime is of both clinical and research interest: its dynamics are modelable under modified assumptions while its causal direction and mechanistic pathway remain unclear. Conclusions Motivated by the understanding of mathematical physiology, the validity of the standard PFR can be assessed a) directly by analyzing pressure reactivity and mean flow indices (PRx and Mx) or b) indirectly through the relationship between CBF and other clinical observables. This approach could potentially help personalize TBI care by considering intracranial pressure and CPP in relation to other data, particularly CBF. The analysis suggests a threshold using clinical indices of autoregulation jointly generalizes independently set indicators to assess CA functionality. These results support the use of increasingly data-rich environments to develop more robust hybrid physiological-machine learning models.
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Affiliation(s)
- J N Stroh
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Bioengineering, University of Colorado Denver |Anschutz Medical Campus, Denver, CO, USA
| | - Brandon Foreman
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati, Cincinnati, OH, USA
- Gardner Neuroscience Institute, University of Cincinnati, Cincinnati, OH, USA
| | - Tellen D Bennett
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Pediatric Intensive Care, Children's Hospital of Colorado, Aurora, CO, USA
| | - Jennifer K Briggs
- Department of Bioengineering, University of Colorado Denver |Anschutz Medical Campus, Denver, CO, USA
| | - Soojin Park
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
- Department of Neurology, New York Presbyterian/Columbia University Irving Medical Center, New York, NY, USA
| | - David J Albers
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Bioengineering, University of Colorado Denver |Anschutz Medical Campus, Denver, CO, USA
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
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Editor's Choice Articles for November. Pediatr Crit Care Med 2021; 22:933-934. [PMID: 34734893 DOI: 10.1097/pcc.0000000000002853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Editor's Choice Articles for February. Pediatr Crit Care Med 2021; 22:133-134. [PMID: 33528195 DOI: 10.1097/pcc.0000000000002651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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