Hyperosmolar therapy response in traumatic brain injury: Explainable artificial intelligence based long-term time series forecasting approach

Abstract

In the neurological intensive care setting implementing tier-based therapy, osmotic therapy serves as an intermediate approach for reducing intracranial pressure (ICP). However, discussions regarding quantitative and specific criteria for the maintenance and transition of treatment remain limited. This study proposes an explainable neural network forecasting of physiological responses after osmotic therapy administration for the purpose of treatment guidance. Recordings of arterial blood pressure and intracranial pressure were obtained from 107 patients with traumatic brain injury. To maintain adequate intracranial pressure, arterial blood pressure, and cerebral perfusion pressure, 20 % mannitol (100 ml; no. infusion, 3,571) and 11.3 % hypertonic saline (40 ml; no. infusion, 574) were administered. The LTSF-NLinear model was used to predict the target signal response for the subsequent 1 h. Six prediction models were derived by combining two agents (mannitol or hypertonic saline) and three target signals (mean arterial blood pressure, mean intracranial pressure and cerebral perfusion pressure). To validate the model's robustness while considering patient heterogeneity, group (5)-fold cross-validation was implemented. To assess the predictive ability of the proposed model for adverse clinical events, binary classification of hypotension, intracranial hypertension, and cerebral hypoperfusion was performed. The overall function of the model was elucidated using linear weight visualization. Additionally, the DeepSHAP application allows for a detailed investigation of the prediction process for a single case. The relationship between response to osmotic therapy and patient outcomes was statistically significant. The proposed models achieved reasonable performance, with R2 score > 0.8 for physiological signal predictions and accuracy > 0.9 for event predictions. Through global explanation, the recent values of mean arterial blood pressure, mean intracranial pressure, and cerebral perfusion pressure of input signals significantly influenced physiological responses after osmotic treatment. In the context of local explanation (a single case), it was possible to determine how physiological state influenced the response. The proposed model is expected to provide objective and quantitative information on osmotherapy in neurological intensive care environments, enabling guidance for alternative and aggressive treatments. Thus, it can potentially aid in enhancing the prognosis of patients with traumatic brain injury and improving the clinical workflow. Moreover, the global and local explanations of the proposed model can provide valuable insights for future researchers.