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Decision tree analysis to predict the risk of intracranial haemorrhage after mild traumatic brain injury in patients taking DOACs.
Turcato, G, Zaboli, A, Pfeifer, N, Maccagnani, A, Tenci, A, Giudiceandrea, A, Zannoni, M, Ricci, G, Bonora, A, Brigo, F
The American journal of emergency medicine. 2021;:388-393
Abstract
BACKGROUND Although the preliminary evidence seems to confirm a lower incidence of post-traumatic bleeding in patients treated with direct oral anticoagulants (DOACs) compared to those on vitamin K antagonists (VKAs), the recommended management of mild traumatic brain injury (MTBI) in patients on DOACs is the same as those on the older VKAs, risking excessive use of CT in the emergency department (ED). AIM: To determine which easily identifiable clinical risk factors at the first medical evaluation in the ED may indicate an increased risk of post-traumatic intracranial haemorrhage (ICH) in patients on DOACs with MTBI. METHODS Patients on DOACs who were evaluated in the ED for an MTBI from 2016 to 2020 at four centres in Northern Italy were considered. A decision tree analysis using the chi-square automatic interaction detection (CHAID) method was conducted to assess the risk of post-traumatic ICH after an MTBI. Known pre- and post-traumatic clinical risk factors that are easily identifiable at the first medical evaluation in the ED were used as input predictor variables. RESULTS Among the 1146 patients on DOACs in this study, post-traumatic ICH was present in 6.5% (75/1146). Decision tree analysis using the CHAID method found post-traumatic TLOC, post-traumatic amnesia, major trauma dynamic, previous neurosurgery and evidence of trauma above the clavicles to be the strongest predictors associated with the presence of post-traumatic ICH in patients on DOACs. The absence of a concussion seems to indicate subgroups at very low risk of requiring neurosurgery. CONCLUSIONS The machine-based CHAID model identified distinct prognostic groups of patients with distinct outcomes based on clinical factors. Decision trees can be useful as guides for patient selection and risk stratification.
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Utility of Serum Biomarkers in the Diagnosis and Stratification of Mild Traumatic Brain Injury.
Lewis, LM, Schloemann, DT, Papa, L, Fucetola, RP, Bazarian, J, Lindburg, M, Welch, RD
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine. 2017;(6):710-720
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Abstract
OBJECTIVE The objective was to compare test characteristics of a single serum concentration of glial fibrillary acidic protein (GFAP), S-100β, and ubiquitin carboxyl terminal hydrolase L1 (UCH-L1), obtained within 6 hours of head injury, to diagnose mild traumatic brain injury (mTBI) in head-injured subjects. METHODS Adults aged 18 to 80 years who presented to one of seven EDs with a blunt closed head injury underwent head CT within 4 hours of injury and had blood drawn for biomarker analysis within 6 hours of injury were eligible. Subjects were considered to have mTBI if they had an initial Glasgow Coma Scale (GCS) > 13 and met one or more of the following criteria: loss of consciousness (LOC), posttraumatic amnesia, or confusion. Subjects with mTBI and an abnormal head computed tomography (CT) scan were categorized as complicated mTBI; those with a normal head CT were categorized as uncomplicated mTBI; and subjects with a GCS = 15, no LOC, no posttraumatic amnesia, and no confusion were considered to not have a mTBI. Biomarker concentration measurements for GFAP and UCH-L1 were performed using an enzyme-linked immunosorbent assay. S-100β concentration was determined using an electrochemiluminescence immunoassay. Median biomarker concentration for each group was compared using the Kruskal-Wallis test. Logistic regression was used to determine area under the receiver operating curve (AUC) for each of the three biomarkers. Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and negative and positive likelihood ratios (LRs) for the three biomarkers to differentiate between complicated mTBI, uncomplicated mTBI, and no mTBI were calculated. RESULTS A total of 247 subjects were enrolled and had adequate clinical and biomarker information for analysis. A total of 188 met criteria for mTBI, with 34 (18.1%) having an acute abnormality on CT (complicated mTBI). The mean (±SD) age of the study population was 45.8 (±17.3) years, and 59.9% were male. Median serum concentrations for all biomarkers were significantly different between groups, lowest in the no mTBI group, and progressively increasing in the uncomplicated and complicated mTBI groups (p < 0.0001). All three biomarkers were significant classifiers of mTBI versus no mTBI, with the following AUCs: GFAP, 0.70; S-100β, 0.69; and UCH-L1, 0.65 (p = 0.17). Sensitivity for mTBI was highest for S-100β (96.5%). NPVs ranged from 31% for UCH-L1 to 35% for GFAP. PPVs ranged from 75.5% for S-100β to 96.5% for GFAP. Negative LR ranged from 0.59 for GFAP to 0.71 for UCH-L1, with positive LR ranging from 1.0 for both UCH-L1 and S-100β to 8.7 for GFAP. CONCLUSION A single serum concentration of GFAP, UCH-L1, or S-100β within 6 hours of head injury may be useful in identifying and stratifying the severity of brain injury in emergency department patients with head trauma, but cannot reliably exclude a diagnosis of concussion. A positive GFAP was associated with the presence of concussion.