Intracranial pressure monitoring is standard of care after severe traumatic brain injury. Episodes of increased intracranial pressure are secondary injuries associated with poor outcome. We developed a model to predict increased intracranial pressure episodes 30 mins in advance, by using the dynamic characteristics of continuous intracranial pressure and mean arterial pressure monitoring. In addition, we hypothesized that performance of current models to predict long-term neurologic outcome could be substantially improved by adding dynamic characteristics of continuous intracranial pressure and mean arterial pressure monitoring during the first 24 hrs in the ICU.
Prognostic modeling. Noninterventional, observational, retrospective study.
The Brain Monitoring with Information Technology dataset consisted of 264 traumatic brain injury patients admitted to 22 neuro-ICUs from 11 European countries.
Predictive models were built with multivariate logistic regression and Gaussian processes, a machine learning technique. Predictive attributes were Corticosteroid Randomisation After Significant Head Injury-basic and International Mission for Prognosis and Clinical Trial design in TBI-core predictors, together with time-series summary statistics of minute-by-minute mean arterial pressure and intracranial pressure.
Increased intracranial pressure episodes could be predicted 30 mins ahead with good calibration (Hosmer-Lemeshow p value 0.12, calibration slope 1.02, calibration-in-the-large −0.02) and discrimination (area under the receiver operating curve = 0.87) on an external validation dataset. Models for prediction of poor neurologic outcome at six months (Glasgow Outcome Score 1–2) based only on static admission data had 0.72 area under the receiver operating curve; adding dynamic information of intracranial pressure and mean arterial pressure during the first 24 hrs increased performance to 0.90. Similarly, prediction of Glasgow Outcome Score 1–3 was improved from 0.68 to 0.87 when including dynamic information.
The dynamic information in continuous mean arterial pressure and intracranial pressure monitoring allows to accurately predict increased intracranial pressure in the neuro-ICU. Adding information of the first 24 hrs of intracranial pressure and mean arterial pressure monitoring to known baseline risk factors allows very accurate prediction of long-term neurologic outcome at 6 months.
1 Department of Intensive Care Medicine, KU Leuven, Leuven, Belgium.
2 Department of Neurosurgery, KU Leuven, Leuven, Belgium.
3 Department of Clinical Physics, Southern General Hospital, Glasgow, Scotland, United Kingdom.
*See also p. 688.
Supported by The European-Framework-Programme (FP5-QLRI-2000-00454, QLGT-2002-00160 and FP7-IST-2007-217049); by the Foundation-for-Scientific-Research (in part) Flanders (FWO), Belgium (research project G. 0904.11); and a senior clinical investigator postdoctoral fellowship to Professor Meyfroidt); and the Flemish government Methusalem-program (to G.V.d.B.).
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The authors have not disclosed any potential conflicts of interest.
Drs. Meyfroidt and Güiza conceived and designed the study. Dr. Piper programmed the Brain-IT database, exported and validated the data. Drs. Piper and Depreitere are members of the Brain-IT steering group. Dr. Güiza programmed the machine learning algorithms and performed the statistic analysis. Drs. Meyfroidt and Güiza drafted the manuscript. Drs. Van den Berghe, Depreitere, and Piper participated in the design of the study and proofread the manuscript. All authors read and approved the final manuscript.
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