title: Prognostic significance of advanced neuromonitoring after traumatic brain injury using neural networks.
abstract: While the therapeutic impact of tissue oxygenation (PtiO2) supplementing ICP-monitoring is proven by several clinical studies, its prognostic value is not well studied. In the following study artificial neural networks (ANN) were used to analyze the accuracy of outcome prediction after traumatic brain injury (TBI) for different combinations of clinical data and parameters derived from neuromonitoring. The total group included 95 patients suffering from TBI. For all patients clinical data (age, GCS, pupillary response etc.) were recorded and outcome was classified using Glasgow outcome scale after 6 months. In a first step a subgroup of 60 patients was chosen to train a neural network to predict outcome based only on clinical data. In a second step the resting 35 patients all having continuous neuromonitoring with automatic data storage of ICP and PtiO2 were chosen. Different network models were composed using the former clinical model plus up to three additional input units for the following parameters: (a) relative number of ICP > 40 mmHg, (b) relative number of PtiO2 < 5 mmHg and (c) relative number of ICP > 30 mmHg with simultaneous PtiO2 < 10 mmHg. For each model the following time periods were analyzed: day 1-2, day 1-3 and day 1-4 after trauma and additionally day 1-4 after trauma plus last day of neuromonitoring. Pure clinical data allowed to predict outcome with 74.3% accuracy. A combination of clinical data with ICP (a) significantly increased the confidence levels of outcome prediction in all time periods (p < 0.05) with accuracy rates rising up to 82.9% for the longer time periods. The combination of clinical data and ICP & PtiO2 (c) lead to comparable results. In contrast, no significant increases were observed in the early time periods when combining clinical data with PtiO2 (b) while accuracy rates rose up to 80% for extended time periods after trauma. A combination of all parameters lead to results lying between the above results. The results indicate that prediction of outcome can be improved by combining clinical and neuromonitoring data. The prognostic value of ICP might be superior to that of PtiO2.
authors.affiliation: Department of Neurosurgery, University of Würzburg, Germany. meix@nch.uni-wuerzburg.de
authors.forename: A
authors.initials: A
authors.lastname: Väth
authors.forename: J
authors.initials: J
authors.lastname: Meixensberger
authors.forename: J
authors.initials: J
authors.lastname: Dings
authors.forename: M
authors.initials: M
authors.lastname: Meinhardt
authors.forename: K
authors.initials: K
authors.lastname: Roosen
mesh_terms: D001930:Brain Injuries / Q000145:classification / Q000503:physiopathology* / Q000628:therapy*; D005500:Follow-Up Studies; D007839:Functional Laterality; D015600:Glasgow Coma Scale; D006801:Humans; D008991:Monitoring, Physiologic; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D013997:Time Factors