Time Series Analysis and Prediction of Intracranial Pressure Using Time-Varying Dynamic Linear Models
| Autor: | I. R. Piper, Maya Kommer, Christopher Hawthorne, Laura Moss, Martin Shaw, Roddy O'Kane |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
business.industry Linear model 030218 nuclear medicine & medical imaging Age and gender 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Blood pressure Dynamic linear model Approximation error medicine Time series business Icp monitoring 030217 neurology & neurosurgery Intracranial pressure |
| Zdroj: | Acta Neurochirurgica Supplement ISBN: 9783030594350 |
| DOI: | 10.1007/978-3-030-59436-7_43 |
| Popis: | Intracranial pressure (ICP) monitoring is a key clinical tool in the assessment and treatment of patients in a neuro-intensive care unit (neuro-ICU). As such, a deeper understanding of how an individual patient's ICP can be influenced by therapeutic interventions could improve clinical decision-making. A pilot application of a time-varying dynamic linear model was conducted using the BrainIT dataset, a multi-centre European dataset containing temporaneous treatment and vital-sign recordings. The study included 106 patients with a minimum of 27 h of ICP monitoring. The model was trained on the first 24 h of each patient's ICU stay, and then the next 2 h of ICP was forecast. The algorithm enabled switching between three interventional states: analgesia, osmotic therapy and paralysis, with the inclusion of arterial blood pressure, age and gender as exogenous regressors. The overall median absolute error was 2.98 (2.41-5.24) mmHg calculated using all 106 2-h forecasts. This is a novel technique which shows some promise for forecasting ICP with an adequate accuracy of approximately 3 mmHg. Further optimisation is required for the algorithm to become a usable clinical tool. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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