Cerebral compliance assessment from intracranial pressure waveform analysis: Is a positional shift-related increase in intracranial pressure predictable?
Real-time monitoring of intracranial pressure (ICP) is a routine part of neurocritical care in the management of brain injury. While mainly used to detect episodes of intracranial hypertension, the ICP signal is also indicative of the volume-pressure relationship within the cerebrospinal system, oft...
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Public Library of Science (PLoS)
2024-01-01
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| Online Access: | https://doi.org/10.1371/journal.pone.0316167 |
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| author | Donatien Legé Pierre-Henri Murgat Russell Chabanne Kevin Lagarde Clément Magand Jean-François Payen Marion Prud'homme Yoann Launey Laurent Gergelé |
| author_facet | Donatien Legé Pierre-Henri Murgat Russell Chabanne Kevin Lagarde Clément Magand Jean-François Payen Marion Prud'homme Yoann Launey Laurent Gergelé |
| author_sort | Donatien Legé |
| collection | DOAJ |
| description | Real-time monitoring of intracranial pressure (ICP) is a routine part of neurocritical care in the management of brain injury. While mainly used to detect episodes of intracranial hypertension, the ICP signal is also indicative of the volume-pressure relationship within the cerebrospinal system, often referred to as intracranial compliance (ICC). Several ICP signal descriptors have been proposed in the literature as surrogates of ICC, but the possibilities of combining these are still unexplored. In the present study, a rapid ICC assessment consisting of a 30-degree postural shift was performed on a cohort of 54 brain-injured patients. 73 ICP signal features were calculated over the 20 minutes prior to the ICC test. After a selection step, different combinations of these features were provided as inputs to classification models. The goal was to predict the level of induced ICP elevation, which was categorized into three classes: less than 7 mmHg ("good ICC"), between 7 and 10 mmHg ("medium ICC"), and more than 10 mmHg ("poor ICC"). A logistic regression model fed with a combination of 5 ICP signal features discriminated the "poor ICC" class with an area under the receiving operator curve (AUROC) of 0.80 (95%-CI: [0.73-0.87]). The overall one-versus-one classification task was achieved with an averaged AUROC of 0.72 (95%-CI: [0.61-0.83]). Adding more features to the input set and/or using nonlinear machine learning algorithms did not significantly improve classification performance. This study highlights the potential value of analyzing the ICP signal independently to extract information about ICC status. At the patient's bedside, such univariate signal analysis could be implemented without dependence on a specific setup. |
| format | Article |
| id | doaj-art-458c49dce3074efb95cb3d15752a264f |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-458c49dce3074efb95cb3d15752a264f2025-01-17T05:31:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031616710.1371/journal.pone.0316167Cerebral compliance assessment from intracranial pressure waveform analysis: Is a positional shift-related increase in intracranial pressure predictable?Donatien LegéPierre-Henri MurgatRussell ChabanneKevin LagardeClément MagandJean-François PayenMarion Prud'hommeYoann LauneyLaurent GergeléReal-time monitoring of intracranial pressure (ICP) is a routine part of neurocritical care in the management of brain injury. While mainly used to detect episodes of intracranial hypertension, the ICP signal is also indicative of the volume-pressure relationship within the cerebrospinal system, often referred to as intracranial compliance (ICC). Several ICP signal descriptors have been proposed in the literature as surrogates of ICC, but the possibilities of combining these are still unexplored. In the present study, a rapid ICC assessment consisting of a 30-degree postural shift was performed on a cohort of 54 brain-injured patients. 73 ICP signal features were calculated over the 20 minutes prior to the ICC test. After a selection step, different combinations of these features were provided as inputs to classification models. The goal was to predict the level of induced ICP elevation, which was categorized into three classes: less than 7 mmHg ("good ICC"), between 7 and 10 mmHg ("medium ICC"), and more than 10 mmHg ("poor ICC"). A logistic regression model fed with a combination of 5 ICP signal features discriminated the "poor ICC" class with an area under the receiving operator curve (AUROC) of 0.80 (95%-CI: [0.73-0.87]). The overall one-versus-one classification task was achieved with an averaged AUROC of 0.72 (95%-CI: [0.61-0.83]). Adding more features to the input set and/or using nonlinear machine learning algorithms did not significantly improve classification performance. This study highlights the potential value of analyzing the ICP signal independently to extract information about ICC status. At the patient's bedside, such univariate signal analysis could be implemented without dependence on a specific setup.https://doi.org/10.1371/journal.pone.0316167 |
| spellingShingle | Donatien Legé Pierre-Henri Murgat Russell Chabanne Kevin Lagarde Clément Magand Jean-François Payen Marion Prud'homme Yoann Launey Laurent Gergelé Cerebral compliance assessment from intracranial pressure waveform analysis: Is a positional shift-related increase in intracranial pressure predictable? PLoS ONE |
| title | Cerebral compliance assessment from intracranial pressure waveform analysis: Is a positional shift-related increase in intracranial pressure predictable? |
| title_full | Cerebral compliance assessment from intracranial pressure waveform analysis: Is a positional shift-related increase in intracranial pressure predictable? |
| title_fullStr | Cerebral compliance assessment from intracranial pressure waveform analysis: Is a positional shift-related increase in intracranial pressure predictable? |
| title_full_unstemmed | Cerebral compliance assessment from intracranial pressure waveform analysis: Is a positional shift-related increase in intracranial pressure predictable? |
| title_short | Cerebral compliance assessment from intracranial pressure waveform analysis: Is a positional shift-related increase in intracranial pressure predictable? |
| title_sort | cerebral compliance assessment from intracranial pressure waveform analysis is a positional shift related increase in intracranial pressure predictable |
| url | https://doi.org/10.1371/journal.pone.0316167 |
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