By Eleanor McDermid, Senior medwireNews Reporter
A model based on quantitative diffusion tensor imaging (DTI) findings predicts outcomes after severe traumatic brain injury (TBI) better than current methods, shows a multicenter study.
The extent of white matter damage in 20 preselected tracts in patients with acute severe TBI was highly predictive for poor outcomes 1 year later, report the French researchers.
"Based on this observation, we have developed a prognostic model that integrates quantitative diffusion variables into a composite DTI score for predicting outcome," they write in Anesthesiology.
Although based purely on DTI findings, the score gave a more accurate prognosis than the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) score, which includes clinical and computed tomography findings.
The IMPACT score was 64% accurate for predicting which of the 105 patients in the study would have unfavorable outcomes 1 year after injury, whereas the DTI-based model was 84%. The sensitivity of the DTI score was 64% and the specificity was 95%.
The DTI score was based on magnetic resonance imaging findings an average of 21 days after injury. One year later, 38% of the patients had an unfavorable Glasgow Outcome Scale score, with 21 dead, five in a vegetative state, and 14 minimally conscious.
"The task of predicting long-term outcome in severe TBI is challenging," say Damien Galanaud (Pitié Salpêtrière Hospital, Paris) and team. "Patients with similar clinical and radiologic characteristics in the acute phase may have markedly different outcomes ranging from death to complete recovery, with intermediate states of impaired consciousness or neuropsychological dysfunction."
Before constructing the DTI score, the researchers normalized the patients' readings against findings from 99 healthy volunteers, because of significant between-center differences in these "normal" values. "The variance was significant enough to seriously undermine the generalizability of our results and led us to implement a normalization step via control subjects at each center," says the team.
A more usual practice would be to image the same volunteers on all scanners, but this was not practical in their study, which involved 10 centers. "In addition, use of the same control subjects does not lend itself to the development of a broadly applicable outcome prediction algorithm that any center can implement using its own set of control subjects," say Galanaud et al.
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