@article{ref1,
title="External validation and recalibration of risk prediction models for acute traumatic brain injury among critically ill adult patients in the United Kingdom",
journal="Journal of neurotrauma",
year="2015",
author="Harrison, David A. and Griggs, Kathryn A. and Prabhu, Gita and Gomes, Manuel and Lecky, Fiona and Hutchinson, Peter John and Menon, David and Rowan, Kathryn M.",
volume="32",
number="19",
pages="1522-1537",
abstract="This study validates risk prediction models for acute traumatic brain injury (TBI) in critical care units in the United Kingdom (UK) and recalibrates the models to this population. The Risk Adjustment In Neurocritical care (RAIN) Study was a prospective, observational cohort study in 67 adult critical care units. Adult patients admitted to critical care following acute TBI with a last pre-sedation Glasgow Coma Score (GCS) less than 15 were recruited. The primary outcomes were mortality and unfavourable outcome (death or severe disability, assessed using the Extended Glasgow Outcome Scale) at six months following TBI. Of 3,626 critical care unit admissions, 2,975 were analysed. Following imputation of missing outcomes, mortality at six months was 25.7% and unfavourable outcome 57.4%. Ten risk prediction models were validated from Hukkelhoven et al, the MRC CRASH Trial Collaborators and the IMPACT group. The model with the best discrimination was the IMPACT 'Lab' model (c-index 0.779 for mortality, 0.713 for unfavourable outcome). This model was well calibrated for mortality at six months but substantially under-predicted the risk of unfavourable outcome. Recalibration of the models resulted in small improvements in discrimination and excellent calibration for all models. The risk prediction models demonstrated sufficient statistical performance to support their use in research and audit but fell below the level required to guide individual patient decision-making. The published models for unfavourable outcome at six months had poor calibration in the UK critical care setting and the models recalibrated to this setting should be used in future research.<p /> <p>Language: en</p>",
language="en",
issn="0897-7151",
doi="10.1089/neu.2014.3628",
url="http://dx.doi.org/10.1089/neu.2014.3628"
}