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ObjectivesThe determination of the volume of brain lesions after trauma is challenging. Manual delineation is observer-dependent and time-consuming which inhibits the practice in clinical routine. We propose and evaluate an automated atlas-based quantification procedure (AQP) based on the detection of abnormal mean diffusivity (MD) values computed from diffusion-weighted MR images.MethodsWe measured the performance of AQP versus manual delineation consensus by independent raters in two series of experiments: i) realistic trauma phantoms (n=5) where abnormal MD values were assigned to healthy brain images according to the intensity, form and location of lesion observed in real TBI cases; ii) severe TBI patients (n=12 patients) who underwent MR imaging within 10 days after injury.ResultsIn realistic trauma phantoms, no statistical difference in Dice similarity coefficient, precision and brain lesion volumes was found between AQP, the rater consensus and the ground truth lesion delineations. Similar findings were obtained when comparing AQP and manual annotations for TBI patients. The intra-class correlation coefficient between AQP and manual delineation was 0.70 in realistic phantoms and 0.92 in TBI patients. The volume of brain lesions detected in TBI patients was 59 ml (19-84 ml) (median; 25-75th centiles).Conclusionsour results indicate that an automatic quantification procedure could accurately determine with accuracy the volume of brain lesions after trauma. This presents an opportunity to support the individualized management of severe TBI patients.Key pointsThe management of patients with severe traumatic brain injury is complex, and access to objective quantitative information lesion volumes can support clinical decision-making.An automated delineation procedure was developed to determine the nature and volume of brain lesions post-trauma.This procedure was based on diffusion weighted MR-imaging to quantify the volume of vasogenic and cellular edema from realistic phantoms and patients with severe traumatic brain injury.Nature and quantification of the brain lesions volume compared favorably with manual delineation of brain lesions by a panel of experts.

Background The purpose of this study was to test the diagnostic performance of clinical judgment for the prediction of a significant injury with whole-body computed tomography scanning after high-energy trauma. Methods The authors conducted an observational prospective study in a single level-I trauma center. Adult patients were included if they had an isolated high-energy injury. Senior trauma leaders were asked to make a clinical judgment regarding the likelihood of a significant injury before performance of a whole-body computed tomography scan. Clinical judgments were recorded using a probability diagnosis scale. The primary endpoint was the diagnosis of a serious-to-critical lesion on the whole-body computed tomography scan. Diagnostic performance was assessed using receiver operating characteristic analysis. Results Of the 354 included patients, 127 patients (36%) had at least one injury classified as abbreviated injury score greater than or equal to 3. The area under the receiver operating characteristic curve of the clinical judgment to predict a serious-to-critical lesion was 0.70 (95% CI, 0.64 to 0.75%). The sensitivity of the clinical judgment was 82% (95% CI, 74 to 88%), and the specificity was 49% (95% CI, 42 to 55%). No patient with a strict negative clinical examination had a severe lesion (n = 19 patients). The sensitivity of the clinical examination was 100% (95% CI, 97 to 100%) and its specificity was 8% (95% CI, 5 to 13%). Conclusions Clinical judgment alone is not sufficient to reduce whole-body computed tomography scan use. In patients with a strictly normal physical examination, whole-body computed tomography scanning might be avoided, but this result deserves additional study in larger and more diverse populations of trauma patients.

International audience; We report the evaluation of an automated method for quantification of brain tissue damage, caused by a severe traumatic brain injury, using mean diffusivity computed from MR diffusion images. Our automatic results obtained on realistic phantoms and real patient images 10 days post-event provided by nine different centers were coherent with four expert manually identified lesions. For realistic phantoms automated method scores were equal to 0.77, 0.77 and 0.83 for Dice, Precision and Sensibility respectively compared to 0.78, 0.72 and 0.86 for the experts. The inter correlation class (ICC) was 0.79. For 7/9 real cases 0.57, 0.50 and 0.70 were respectively obtained for automated method compared to 0.60, 0.52 and 0.78 for experts with ICC=0.71. Additionally, we detail the quality control module used to pool data from various image provider centers. This study clearly demonstrates the validity of the proposed automated method to eventually compute in a multi-centre project, the lesional load following brain trauma based on MD changes.

Trauma-induced coagulopathy (TIC) is a common feature after severe trauma. Detection of TIC is based upon classic coagulation tests including international normalized ratio (INR) value. Point-of-care (POC) devices have been developed to rapidly measure INR at the bedside on whole blood. The aim of the study was to test the precision of the Coagucheck® XS Pro device for INR measurement at hospital admission after severe trauma. We conducted a prospective observational study in a French level I trauma center. From January 2015 to May 2016, 98 patients with a suspicion of a post-traumatic acute hemorrhage had POC-INR measurement on whole blood concomitantly to classic laboratory INR determination (lab-INR) on plasma at hospital admission. The agreement between the two methods in sorting three predefined categories of INR (normal coagulation, moderate TIC and severe TIC) was evaluated using the Cohen’s kappa test with a quadratic weighting. The correlation between POC-INR and lab-INR was measured using the Pearson’s coefficient. We also performed a Bland and Altman analysis. The agreement between the lab-INR and the POC-INR was moderate (Kappa = 0.45 [95% CI 0.36–0.50]) and the correlation between the two measurements was also weak (Pearson’s coefficient = 0.44 [95% CI 0.27–0.59]). Using a Bland and Altman analysis, the mean difference (bias) for INR was 0.22 [95% CI 0.02–0.42], and the standard deviation (precision) of the difference was 1.01. POC Coagucheck® XS Pro device is not reliable to measure bedside INR. Its moderate agreement with lab-INR weakens the usefulness of such device after severe trauma. NCT02869737 . Registered 9 August 2016.