Probabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injury
Abstract Traumatic brain injury (TBI) causes alteration in brain functions. Generally, at intensive care units (ICU), intracranial pressure (ICP) is monitored and treated to avoid increases in ICP with associated poor clinical outcome. The aim was to develop a model which could predict future ICP le...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-06-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-13732-x |
| _version_ | 1818207541725757440 |
|---|---|
| author | Priyantha Wijayatunga Lars-Owe D. Koskinen Nina Sundström |
| author_facet | Priyantha Wijayatunga Lars-Owe D. Koskinen Nina Sundström |
| author_sort | Priyantha Wijayatunga |
| collection | DOAJ |
| description | Abstract Traumatic brain injury (TBI) causes alteration in brain functions. Generally, at intensive care units (ICU), intracranial pressure (ICP) is monitored and treated to avoid increases in ICP with associated poor clinical outcome. The aim was to develop a model which could predict future ICP levels of individual patients in the ICU, to warn treating clinicians before secondary injuries occur. A simple and explainable, probabilistic Markov model was developed for the prediction task ICP ≥ 20 mmHg. Predictions were made for 10-min intervals during 60 min, based on preceding hour of ICP. A prediction enhancement method was developed to compensate for data imbalance. The model was evaluated on 29 patients with severe TBI. With random data selection from all patients (80/20% training/testing) the specificity of the model was high (0.94–0.95) and the sensitivity good to high (0.73–0.87). Performance was similar (0.90–0.95 and 0.73–0.89 respectively) when the leave-one-out cross-validation was applied. The new model could predict increased levels of ICP in a reliable manner and the enhancement method further improved the predictions. Further advantages are the straightforward expandability of the model, enabling inclusion of other time series data and/or static parameters. Next step is evaluation on more patients and inclusion of parameters other than ICP. |
| first_indexed | 2024-12-12T04:30:34Z |
| format | Article |
| id | doaj.art-8c850ec4ce6f4371be5c9879d859af72 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-12T04:30:34Z |
| publishDate | 2022-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8c850ec4ce6f4371be5c9879d859af722022-12-22T00:38:05ZengNature PortfolioScientific Reports2045-23222022-06-011211910.1038/s41598-022-13732-xProbabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injuryPriyantha Wijayatunga0Lars-Owe D. Koskinen1Nina Sundström2Department of Statistics, Umeå UniversityDepartment of Clinical Science – Neurosciences, Umeå UniversityDepartment of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå UniversityAbstract Traumatic brain injury (TBI) causes alteration in brain functions. Generally, at intensive care units (ICU), intracranial pressure (ICP) is monitored and treated to avoid increases in ICP with associated poor clinical outcome. The aim was to develop a model which could predict future ICP levels of individual patients in the ICU, to warn treating clinicians before secondary injuries occur. A simple and explainable, probabilistic Markov model was developed for the prediction task ICP ≥ 20 mmHg. Predictions were made for 10-min intervals during 60 min, based on preceding hour of ICP. A prediction enhancement method was developed to compensate for data imbalance. The model was evaluated on 29 patients with severe TBI. With random data selection from all patients (80/20% training/testing) the specificity of the model was high (0.94–0.95) and the sensitivity good to high (0.73–0.87). Performance was similar (0.90–0.95 and 0.73–0.89 respectively) when the leave-one-out cross-validation was applied. The new model could predict increased levels of ICP in a reliable manner and the enhancement method further improved the predictions. Further advantages are the straightforward expandability of the model, enabling inclusion of other time series data and/or static parameters. Next step is evaluation on more patients and inclusion of parameters other than ICP.https://doi.org/10.1038/s41598-022-13732-x |
| spellingShingle | Priyantha Wijayatunga Lars-Owe D. Koskinen Nina Sundström Probabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injury Scientific Reports |
| title | Probabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injury |
| title_full | Probabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injury |
| title_fullStr | Probabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injury |
| title_full_unstemmed | Probabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injury |
| title_short | Probabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injury |
| title_sort | probabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injury |
| url | https://doi.org/10.1038/s41598-022-13732-x |
| work_keys_str_mv | AT priyanthawijayatunga probabilisticpredictionofincreasedintracranialpressureinpatientswithseveretraumaticbraininjury AT larsowedkoskinen probabilisticpredictionofincreasedintracranialpressureinpatientswithseveretraumaticbraininjury AT ninasundstrom probabilisticpredictionofincreasedintracranialpressureinpatientswithseveretraumaticbraininjury |