Author:

Keywords:

Science & Technology, Life Sciences & Biomedicine, Critical Care Medicine, Clinical Neurology, Neurosciences, General & Internal Medicine, Neurosciences & Neurology, intracranial pressure, intracranial pressure dose, machine learning, prediction, traumatic brain injury, SELECTION, Adult, Humans, Brain Injuries, Brain Injuries, Traumatic, Intracranial Hypertension, Intracranial Pressure, Machine Learning, Computer Simulation, Arterial Pressure, Monitoring, Physiologic, Clinical Decision Rules, CENTER-TBI High-Resolution ICU (HR ICU) Sub-Study Participants and Investigators, ADULT BRAIN INJURY, INTRACRANIAL PRESSURE, Other, SECONDARY INSULT, TRAUMATIC BRAIN INJURY, 1103 Clinical Sciences, 1109 Neurosciences, Neurology & Neurosurgery, 3202 Clinical sciences, 3209 Neurosciences, 5202 Biological psychology

Abstract:

Treatment and prevention of elevated intracranial pressure (ICP) is crucial in patients with severe traumatic brain injury (TBI). Elevated ICP is associated with secondary brain injury, and both intensity and duration of an episode of intracranial hypertension, often referred to as "ICP dose," are associated with worse outcomes. Prediction of such harmful episodes of ICP dose could allow for a more proactive and preventive management of TBI, with potential implications on patients' outcomes. The goal of this study was to develop and validate a machine-learning (ML) model to predict potentially harmful ICP doses in patients with severe TBI. The prediction target was defined based on previous studies and included a broad range of doses of elevated ICP that have been associated with poor long-term neurological outcomes. The ML models were used, with minute-by-minute ICP and mean arterial blood pressure signals as inputs. Harmful ICP episodes were predicted with a 30 min forewarning. Models were developed in a multi-center dataset of 290 adult patients with severe TBI and externally validated on 264 patients from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) dataset. The external validation of the prediction model on the CENTER-TBI dataset demonstrated good discrimination and calibration (area under the curve: 0.94, accuracy: 0.89, precision: 0.87, sensitivity: 0.78, specificity: 0.94, calibration-in-the-large: 0.03, calibration slope: 0.93). The proposed prediction model provides accurate and timely predictions of harmful doses of ICP on the development and external validation dataset. A future interventional study is needed to assess whether early intervention on the basis of ICP dose predictions will result in improved outcomes.