title: Novel methods to predict increased intracranial pressure during intensive care and long-term neurologic outcome after traumatic brain injury: development and validation in a multicenter dataset.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  Prognostic modeling. Noninterventional, observational, retrospective study.
  
  
  METHODS
  The Brain Monitoring with Information Technology dataset consisted of 264 traumatic brain injury patients admitted to 22 neuro-ICUs from 11 European countries.
  
  
  METHODS
  None.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Intensive Care Medicine, KU Leuven, Leuven, Belgium. fabian.guiza@med.kuleuven.be
authors.forename: Fabian
authors.initials: F
authors.lastname: Güiza
authors.forename: Bart
authors.initials: B
authors.lastname: Depreitere
authors.forename: Ian
authors.initials: I
authors.lastname: Piper
authors.forename: Greet
authors.initials: G
authors.lastname: Van den Berghe
authors.forename: Geert
authors.initials: G
authors.lastname: Meyfroidt
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D001794:Blood Pressure / Q000502:physiology*; D001930:Brain Injuries / Q000503:physiopathology*; D003422:Critical Care; D016208:Databases, Factual; D005260:Female; D023261:Glasgow Outcome Scale; D006801:Humans; D007362:Intensive Care Units; D019586:Intracranial Hypertension / Q000175:diagnosis* / Q000503:physiopathology; D007427:Intracranial Pressure / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D008991:Monitoring, Physiologic; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care; D011379:Prognosis; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D055815:Young Adult