Predicting Outcome after Traumatic Brain Injury: Development and International Validation of Prognostic Scores Based on Admission Characteristics

By Ewout W. Steyerberg, N. Mushkudiani, Pablo Perel, I. Butcher, Juan Lu, G. McHugh, G. Murray, A. Marmarou, I. Roberts, D. Habbema and 1 more. PLoS Medicine, 2008

1,333 citations recorded by the source corpus.

Background Traumatic brain injury (TBI) is a leading cause of death and disability. A reliable prediction of outcome on admission is of great clinical relevance. We aimed to develop prognostic models with readily available traditional and novel predictors. Methods and Findings Prospectively collected individual patient data were analyzed from 11 studies. We considered predictors available at admission in logistic regression models to predict mortality and unfavorable outcome according to the Glasgow Outcome Scale at 6 mo after injury. Prognostic models were developed in 8,509 patients with severe or moderate TBI, with cross-validation by omission of each of the 11 studies in turn. External validation was on 6,681 patients from the recent Medical Research Council Corticosteroid Randomisation after Significant Head Injury (MRC CRASH) trial. We found that the strongest predictors of outcome were age, motor score, pupillary reactivity, and CT characteristics, including the presence of traumatic subarachnoid hemorrhage. A prognostic model that combined age, motor score, and pupillary reactivity had an area under the receiver operating characteristic curve (AUC) between 0.66 and 0.84 at cross-validation. This performance could be improved (AUC increased by approximately 0.05) by considering CT characteristics, secondary insults (hypotension and hypoxia), and laboratory parameters (glucose and hemoglobin). External validation confirmed that the discriminative ability of the model was adequate (AUC 0.80). Outcomes were systematically worse than predicted, but less so in 1,588 patients who were from high-income countries in the CRASH trial. Conclusions Prognostic models using baseline characteristics provide adequate discrimination between patients with good and poor 6 mo outcomes after TBI, especially if CT and laboratory findings are considered in addition to traditional predictors. The model predictions may support clinical practice and research, including the design and analysis of randomized controlled trials.

Subfields: Emergency Medicine, Epidemiology, Neurology

View Paper: https://www.semanticscholar.org/paper/5fe1f680fbe9efd347a492d7f6317aa905c95720

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Citations in the corpus

Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research (2013, background): “For example, a prognostic model developed to help identify patients with traumatic brain injury who are likely to have an unfavourable six month outcome [16] involves the prognostic factors of age, motor score, pupillary reactivity, computed tomographic characteristics, and laboratory parameters.”
External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges (2016, background): “33 Such big, combined datasets heralds an exciting opportunity to improve the uptake of external validation research.”
Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research (2013, methodology): “CRASH/IMPACT 6 month outcome after traumatic brain injury (n = 10 008 for CRASH, n = 8530 for IMPACT) [43] Cross-validation of CRASH on IMPACT and vice versa [43] Cited as source of prognostic risk estimation [7].”
Prediction models need appropriate internal, internal-external, and external validation (2015, positive): “In the context of individual patient data meta-analysis (IPDMA), internaleexternal cross-validation has been used to show external validity of a prediction model [12,13].”