Blood-Based CNS-Injury and Inflammation Biomarkers Sampled at Acute, Subacute, and Chronic phases After Mild TBI Demonstrate Diagnostic Utility For Patients With and Without Intracranial Injuries on Acute CT and MRI

Blood-Based CNS-Injury and Inflammation Biomarkers Sampled at Acute, Subacute, and Chronic phases After Mild TBI Demonstrate Diagnostic Utility For Patients With and Without Intracranial Injuries on Acute CT and MRI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Blood-Based CNS-Injury and Inflammation Biomarkers Sampled at Acute, Subacute, and Chronic phases After Mild TBI Demonstrate Diagnostic Utility For Patients With and Without Intracranial Injuries on Acute CT and MRI Gerard Janez Brett Clarke, Toril Skandsen, Henrik Zetterberg, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3959215/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Identifying individuals with intracranial injuries following mild traumatic brain injury (mTBI), i.e. complicated mTBI cases, is important for follow-up and prognostication. The aim of the current study was to identify the ability of single and multi-panel blood biomarkers of CNS injury and inflammation, from the acute to chronic phase after injury, to classify people with complicated mTBI on computer tomography (CT) and/or magnetic resonance imaging (MRI) acquired within 72 hours. Methods Patients with mTBI (n = 207, 16–60 years), i.e., Glasgow Coma Scale (GCS) score between 13 and 15, loss of consciousness (LOC) < 30 min and post-traumatic amnesia (PTA) < 24 hours, were included. Complicated mTBI was present in 8% (n = 16) based on CT (CT+) and 12% (n = 25) based on MRI (MRI+). Blood biomarkers were sampled at four timepoints following injury: admission (within 72 hours), 2 weeks (± 3 days), 3 months (± 2 weeks) and 12 months (± 1 month). CNS biomarkers included were GFAP, NFL and tau, along with a panel of 12 inflammation markers. Predictive models were generated with both single and multi-panel biomarkers and assessed using area under the curve analyses (AUCs). Results The most discriminative single biomarkers were GFAP at admission (CT+: AUC = 0.78; MRI+: AUC = 0.82) and NFL at 2 weeks (CT+: AUC = 0.81; MRI+: AUC = 0.89) and 3 months (MRI+: AUC = 0.86). MIP-1β and IP-10 concentrations were significantly lower at almost all timepoints in patients who were CT + and MRI+. Eotaxin and IL-9 were significantly lower in patients who were MRI + only. FGF-basic concentrations increased over time in patients who were MRI- and were significantly higher than patients MRI + at 3- and 12 months. Multi-biomarker panels improved discriminability at all timepoints (AUCs ≈ 0.90 of admission and 2-week models for CT + and AUC > 0.90 of admission, 2-week and 3-month models for MRI+). Conclusions The CNS biomarkers GFAP and NFL were useful diagnostic biomarkers of complicated mTBI in acute, subacute and chronic phases after mTBI. Several inflammation markers were significantly lower in patients with complicated mTBI, at all timepoints, and could discriminate between CT + and MRI + even after 12 months. Multi-biomarker panels improved diagnostic accuracy at all timepoints. concussion mixed-mechanism mild TBI prediction predictive modelling cytokines growth factors neuroimaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Mild traumatic brain injury (mTBI) is the most common type of brain injury, representing up to 90% of all head injury cases. 1,2 mTBI encompasses a wide range of injury severities, from blows to the head with limited symptoms and rapid recovery, to injuries involving intracranial abnormalities detectable with neuroimaging techniques. Individuals with mTBI concurrent with traumatic intracranial findings determined by computed tomography (CT) or magnetic resonance imaging (MRI) are considered to have experienced a complicated mTBI. These individuals are at increased risk for cognitive sequalae and persistent post-concussive symptoms. 3–6 Given this, low-cost and reliable identification of patients with potential intracranial injury in the emergency department (ED), such as through the utilization of blood-based biomarkers, could drastically improve patient triage protocols. Further, since most intracranial injuries following mTBI resolve over time, blood biomarkers able to reliably identify patients with potential complicated mTBI at later timepoints could be of great clinical utility. This could prove helpful also for those individuals with mTBI involved in litigation. CT is currently the mainstay imaging technique used in the acute care of patients with TBI, 6–8 due in part to its speed and cost-effectiveness compared to MRI. Two biomarkers: UCH-L1 and GFAP are currently approved by the Food and Drug Administration in the US to assess the likelihood of mTBI-related intracranial injury in the acute phase. 9 In Scandinavia, blood S100B is recommended for triaging patients with mTBI to CT scanning during the first 24 hours after injury. 10 However, these biomarkers are only approved for patients presenting acutely, and it is unclear whether other biomarkers, or a combination of biomarkers could improve diagnostic accuracy in the acute phase as well as later. Furthermore, MRI is known to be more sensitive to certain brain injuries than CT, particularly in identifying traumatic axonal injury (TAI), including microbleeds 8,11,12 that are difficult to observe on CT. 3–5 There are currently no recognized guidelines regarding which patients should be referred for clinical MRI examination instead of CT, 6–8 although emerging evidence suggests MRI findings improve functional outcome prediction, 11 and many patients with no observable lesions on CT have findings on MRI. 13 Blood-based biomarker diagnostics in mTBI are currently being used in the acute phase, though there is currently no approved diagnostic biomarker at later timepoints. NFL – considered a surrogate marker for axonal injury 14 – is a promising candidate in this regard, given its late peak (~ 10 days after injury), 15 and protracted course of elevation for at least 3 months in mTBI 16 and up to 5 years following more severe TBI. 17 Recent studies have suggested acutely measured NFL is able to discriminate intracranial abnormalities in patients with mTBI on both CT and MRI, 18–20 though its diagnostic utility for this at later timepoints is yet to be determined. TBI is also known to trigger increased inflammatory activity. 21,22 So far, clinical research on TBI and inflammation has primarily been performed in moderate-severe TBI cohorts. 21,22 Recent work on mild TBI cohorts has evidenced associations between certain inflammation markers and complicated mTBI (determined by both CT/MRI). 20,23–25 However, many markers of inflammation in the context of mTBI diagnosis remain unexplored, as studies often pre-selected a small number of inflammation markers for analysis. Moreover, the focus has thus far been limited to acutely measured peripheral inflammation markers, highlighting the need for studies investigating chronic or late-phase inflammation with regard to mTBI diagnostics. Our study aims to predict complicated mTBI on CT/MRI taken during the acute phase using a large array of candidate biomarkers related to CNS damage/inflammation obtained at admission (within 72 hours), 2 weeks (± 3 days), 3 months (± 2 weeks) and 12 months (± 1 month). Predictive models were generated with both single and multi-panel biomarkers and assessed using area under the curve analyses (AUCs). Moreover, we aimed to elucidate whether there were any biomarker profiles uniquely associated with findings on MRI vs. CT. In this manner, we aim to uncover accurate diagnostic biomarkers from the acute to chronic phase of mTBI, with the goal of informing more personalized treatment protocols, in line with current goals of developing precision-based medicine approaches. 26 Materials and Methods Participants and Recruitment The Trondheim mTBI study is a large-scale prospective cohort study with follow up for 12 months in patients with mTBI between 16–60 years of age. Patients with mTBI (n = 378) were included from April 1st 2014 to December 15th 2015. They were recruited from two emergency departments (EDs): St. Olavs hospital (Trondheim University Hospital), a regional level 1 trauma center in Trondheim, Norway, and Trondheim Municipal Emergency clinic, a general practitioner-run, 24-hour/7-day out-patient clinic. Inclusion criteria were having sustained a mild TBI according to World Health Organization criteria, 27 i.e. Glasgow Coma Scale (GCS) score of 13–15, < 30 minutes loss of consciousness (LOC), and < 24 hours post-traumatic amnesia (PTA). Exclusion criteria were: ( 1 ) non-fluency in the Norwegian language, ( 2 ) pre-existing neurological, psychiatric, somatic, or substance use disorder; determined to be severe enough to interfere with follow-up and outcome assessment, ( 3 ) a prior history of a complicated mild (i.e. having trauma-related intracranial findings on CT or MRI), moderate or severe TBI, ( 4 ) other major trauma that could interfere with follow-up or outcome assessment, or ( 5 ) presentation > 48 hours after the initial trauma. The sub-cohort selected for this investigation were all patients with mTBI (see Skandsen et al. 28 and Einarsen et al. 13 for more details regarding patient enrolment and clinical ratings) who had blood data collected. Clinical Information Clinical information was obtained from patient interviews and medical records. LOC was rated as present only if observed. Duration of PTA was recorded as time after injury for which the patient had no continuous memory (> 0 min and < 1 hour, or 1–24 hours). GCS score was assessed in the ED or inferred from records. 29 Presence of injuries to parts of the body other than the head (e.g. dislocations, fractures, soft tissue injuries in need of treatment) was recorded based on self-report and ED/hospital records. Skin abrasions and contusions were not included in this rating. CT imaging Non-contrast CT was performed on a Siemens Somatom Sensation 64 row scanner as part of the initial clinical assessment (within 24 hours of injury), according to the Scandinavian Guidelines for Head Injury Management. 10 The intracranial traumatic findings were classified by an experienced neuroradiologist into contusion, epidural hematoma (EDH), traumatic sub-arachnoid hemorrhage (tSAH) and subdural hematoma (SDH). The CT scans from patients with intracranial traumatic findings on MRI were later reviewed by an experienced neuroradiologist and a consultant in physical medicine and rehabilitation. MRI imaging Subjects underwent a standardized brain MRI scan within 72 hours of injury. 30 All MRI scans were acquired with the same protocol on the same 3.0 Tesla Siemens Skyra MRI scanner with a 32-channel head coil. The protocol included 3D volumes with T1-weighted (Magnetization Prepared Rapid Acquisition Gradient Echo), T2-weighted, Fluid-attenuated inversion recovery (FLAIR), and susceptibility-weighted (SWI) scans. The clinical scans were read by neuroradiologists according to standard criteria, and the inter-rater reliability was moderate to good. 13 TAI was diagnosed and graded as described previously. 31 Two patients with a positive CT scan were unable to undergo MRI at inclusion, hence the reading of the CT scan was used to describe TBI-related intracranial findings in place of MRI. More detailed patient MRI results and their development over time are presented in Einarsen et al. 13 Blood Samples Time of blood sampling was measured as time from injury. Participants had their blood drawn at admission (within 72 h post-injury), then at 2 weeks (± 3 days), 3 months (± 2 weeks), and 12 months (± 1 month). Plasma samples were obtained with EDTA gel tubes which were immediately put on ice and centrifuged for 10 min at 4°C on 2,000 g within 30 min of acquisition and aliquoted into eight 0.5 mL Nunc tubes which were immediately frozen at -80°C. The tubes remained stored at -80°C until two tubes were retrieved and transported in the frozen condition to the labs that analyzed the CNS injury and the inflammation makers, respectively. No freeze thaw cycles were necessary. Plasma GFAP, NFL and tau concentrations were measured using the validated and commercially available Human Neurology 4-Plex A assay (N4PA) on an HD-1 Single molecule array (Simoa) instrument, according to instructions from the manufacturer (Quanterix, Billerica, MA). The measurements were performed in one round of experiments using one batch of reagents by board-certified laboratory technicians blinded to the clinical data. See Clarke et al. 32 for further details. For inflammation markers, the plasma samples were analyzed using a commercial fluorescence magnetic bead-based immunoassay, with high-sensitivity detection range and precision (Bio-Plex Human Cytokine 27-Plex, Bio-Rad Laboratories, Inc., Hercules, CA, USA). 27 cytokines were analyzed in total (see Chaban et al. 33 for full list). Plasma samples were diluted 1:4 in Sample Diluent (Bio-Rad Laboratories, Inc.). A lower detection limit for the cytokines in the low picogram/milliliter range (< 20 pg/mL for all cytokines) was determined automatically by the software based on the standard curve for each inflammation marker. Only markers that were present in methodologically and clinically meaningful amounts, according to our previous experience, 34 in more than 75% of all samples during the observation period, were selected for further study. These were: IL-1 receptor antagonist (IL-1ra), IL-8, IL-9, IL-17A, eotaxin-1 (CCL11), basic fibroblast growth factor (FGF-basic), interferon gamma (IFN-γ), IFN-γ-inducing protein 10 (IP-10/CXCL10), monocyte chemoattractant protein 1 (MCP-1/CCL2), macrophage inflammatory protein-1-beta (MIP-1β/CCL4), platelet-derived growth factor-BB (PDGF-BB), tumor necrosis factor (TNF). Statistical Analysis Demographic and clinical variables for the total number of patients included in this study are summarized by frequencies and percentages or means and standard deviations, as appropriate. Descriptive statistics (mean, standard deviation, median, interquartile range and range) for biomarkers are presented per timepoint in supplementary tables 1 and 2. Linear mixed model (LMM) analyses were conducted with groups separated by CT+/CT- and MRI+/MRI-. LMM assesses the time course of multiple groups, allowing comparisons both across timepoints within a certain group, and group comparisons at a given timepoint. Time, group, and a time-by-group interaction were entered as fixed effects. The interaction coefficient was retained in all models regardless of statistical significance. To account for within-subject correlations, a covariance structure for the total residuals was selected among a set of candidate models: ( 1 ) a model with an unstructured correlation matrix and homogeneous residual variance (UC-model), ( 2 ) a random intercept only model (RI-model), and ( 3 ) a random intercept model with heterogenous residual variances (HV-model). A fully unstructured covariance structure, including heterogeneous variances, was ruled out due to lack of convergence of the fitting algorithm. Model fit was assessed using a pragmatic combination of Aikake information criterion (AIC) and log-likelihood ratio (LR) tests, aimed at selecting the most parsimonious model with an acceptable model fit (without considering a specific threshold of significance). For biomarkers showing a significant group effect or a significant time-by-group interaction (main effects presented in supplementary table 3 ), group differences at each timepoint were assessed using post-hoc contrasts adjusted by Tukey’s honest significant difference (HSD). Within-group changes across time were assessed only if there was a significant time-by-group interaction. Significant effects of time with no effect of group were not of interest, as these have been previously reported on. 16 To determine the best combination of candidate biomarkers for predicting patients who were CT + and MRI+, elastic net regression was performed using all candidate biomarkers at each timepoint as possible predictors. Elastic net models are generalized linear models fit with a hybrid of lasso and ridge penalty functions. 35 Ridge regression penalizes the square of the regression coefficients for the predictors, shrinking coefficients for the least important predictors toward zero. Lasso imposes a penalty on the absolute value of the coefficients, shrinking them by a constant factor, thereby selecting a subset of predictors by shrinking the coefficients of the least predictive predictors to zero. Whereas ridge retains all predictors, adjusting for relative predictive importance, lasso tends to select only one predictor from a group of correlated predictors. Elastic net is a useful combination of both, performing shrinkage selection while enabling the inclusion of collinear predictors in the final model. This means all variables that have a meaningful effect on the outcome can be selected by the procedure, even if they are strongly correlated, while predictors unrelated to outcome will be set to 0. To determine the optimal penalization parameters and internally validate models, 5-fold cross-validation (CV) was used, testing over a grid of α and λ sequences and selecting the combination yielding the maximal AUC value. Uncertainty in variable selection was assessed by repeating the penalized regression procedure for each model in 1000 bootstrap samples. The uncertainty for each of the variables was assessed as the proportion of the 1000 bootstrap samples when the variable’s coefficient was not set to 0, i.e. the number of times the procedure determined the variable had a meaningful effect on outcome (see supplementary table 4 & supplementary Fig. 1). For the subset of biomarkers selected via elastic net, we refit ordinary logistic regression models to obtain unpenalized parameter estimates. A complete-case per timepoint approach was used. Unpenalized regression coefficients are standardized for comparability between biomarkers. The ability of each biomarker – at the four timepoints – to discriminate patients with intracranial findings on CT or MRI from those without, was assessed with receiver operating curves (ROCs) and area under the curves (AUCs). The optimal pair of sensitivity and specificity was defined as the one corresponding to the Youden’s J statistic. 36 AUCs were calculated based on the unpenalized multivariable models. AUCs were classified based on the following system: 0.90-1.00 = excellent, 0.80–0.90 = very good, 0.70–0.80 = moderate, 0.60–0.70 = poor, < 60 = negligible. 37 To provide some protection against false positives due to multiple comparisons, the significance level was set to α = 0.01. P-values for unpenalized regression models are not provided, as p-values after variable selection tend to be underestimated. A small number of outliers (n = 5) were determined and removed based on a pragmatic assessment of leverage values from LMMs and visual inspection of the data. Statistical analyses were performed using R version 4.2.2. 38 Linear mixed models were conducted using the nlme package. 39 Post-hoc linear mixed model contrasts were conducted using the emmeans package. 40 Elastic net regression was conducted using the glmnet package. 41 Unpenalized logistic regressions were conducted in base R. AUC and ROC curves were computed using the pROC package. 42 Results The flow chart in Fig. 1 summarizes sample numbers for each timepoint and reasons for drop out/data loss. 207 had blood data at one or more timepoints. At 2 weeks there were 177 with blood data available, at 3 months 172, and by 12 months, 159 patients remained in the study, giving a long-term retention rate of 77%. Table 1 provides a detailed summary of the demographic and clinical characteristics of the patients with mTBI included. Most were men (63.3%), with GCS scores of 15 in 76.3%. LOC was observed in 47.3%, and PTA between 1 hour and 24 hours in 30.9%, while 36.7% experienced concurrent extracranial injuries. A total of 8% of patients were CT+ (16% were not triaged to CT) and 12% were MRI+. 40.0% of patients had the same intracranial injuries on CT and MRI, 24.0% had additional or different findings on MRI compared to CT and 36.0% had findings on MRI but none on CT. Table 1 Demographic and injury characteristics of total patients with mild TBI included in the study. Patients with mTBI N = 207 Sex (%) Male M 131 (63.3) Females 76 (36.7) Age at Injury Mean age, y (SD) 32.4 (13.2) Age range, y 16–60 GCS score (%) 13 5 (2.4) 14 33 (16.0) 15 158 (76.3) Not recorded 11 (5.3) LOC (%) Unobserved LOC 109 (52.7) Observed LOC 98 (47.3) PTA duration (%) PTA < 1 hour 143 (69.1) PTA between 1–24 hours 64 (30.9) Injury Mechanism mTBI (%) Fall 79 (38.1) Traffic Accident 57 (27.5) Sports Accident 26 (12.6) Violence 31 (15.0) Hit Object & Other 14 (6.8) Extracranial Injuries ‡ No 131 (63.3) Yes 76 (36.7) Intracranial Finding on CT (%) Contusion only 4 (1.9) Intracranial hematoma only* 9 (4.4) Contusion and hematoma* 3 (1.4) No findings 158 (76.3) Not triaged to CT 33 (16.0) Intracranial Finding on MRI (%) TAI only 6 (2.9) Contusion only 3 (1.4) Intracranial hematoma only* 5 (1.4) TAI and contusion 5 (2.4) Contusion and hematoma* 6 (2.9) No findings 182 (88.0) Intracranial Finding on CT vs. MRI (%) Contusion on CT and MRI 2 (1.0) Intracranial hematoma* on CT and MRI 5 (2.4) Contusion and hematoma* on CT and MRI 3 (1.4) Contusion on CT and contusion and TAI on MRI 2 (1.0) Hematoma* on CT and contusion and hematoma* on MRI 3 (1.4) Hematoma* on CT and contusion on MRI* 1 (0.5) TAI and contusion on MRI, no CT findings 3 (1.4) TAI findings on MRI, no CT findings 5 (2.4) TAI findings on MRI, CT not performed 1 (0.5) No findings on either modality incl. not triaged to CT 182 (88.0) Abbreviations: mTBI = mild traumatic brain injury; GCS = Glasgow Coma Score; LOC = Loss of Consciousness; PTA = Post-Traumatic Amnesia; MRI = Magnetic Resonance Imaging ‡ Extracranial injuries refer to the presence of concurrent injuries to parts of the body other than the head (e.g. bone fracture). * Intracranial hematoma includes epidural hematomas, subdural hematomas, and traumatic subarachnoid hemorrhaging. [INSERT Table 1 HERE] Longitudinal Evolution of Biomarkers Based on Presence of Intracranial Findings Figures 2 and 3 depict the temporal profiles of the biomarkers based on CT + versus CT- and MRI + versus MRI-, respectively. GFAP concentrations were significantly elevated in both CT + and MRI + patients compared to CT- and MRI- at admission and 2 weeks, while NFL was significantly elevated at admission, 2 weeks and 3 months (see Table 2 ). MIP-1β was significantly lower in CT + and MRI + patients at all timepoints, while IP-10 was significantly lower in CT + and MRI + patients at admission, 3 months and 12 months. Biomarkers uniquely associated with MRI + were eotaxin, IL-9 and FGF-basic. Eotaxin was significantly lower in the MRI + group at all timepoints, IL-9 was significantly lower at admission, 3 months and 12 months, and FGF-basic was significantly lower at 3 months and 12 months only. No biomarker was uniquely associated with CT+. Table 2 Group contrasts of blood biomarker concentrations per timepoint between patients who were CT+/CT- and MRI+/MRI-. Admission Estimate [95% CI] p -value 2 weeks Estimate [95% CI] p -value 3 months Estimate [95% CI] p -value 12 months Estimate [95% CI] p -value CT Imaging: GFAP † 0.60 [0.30–0.90] p < 0.001 0.27 [0.10–0.45] p = 0.002 0.17 [0.03–0.30] p = 0.014 0.08 [-0.07–0.23] p = 0.306 NFL † 0.23 [0.03–0.44] p = 0.027 0.67 [0.45–0.90] p < 0.001 0.44 [0.23–0.66] p < 0.001 -0.07 [-0.29–0.14] p = 0.511 MIP-1β -24.26 [-38.28 – -10.23] p < 0.001 -26.54 [-40.05 – -13.02] p < 0.001 -25.98 [-41.01 – -10.95] p < 0.001 -29.27 [-44.50 – -14.03] p < 0.001 IP-10 † -0.25 [-0.39 – -0.11] p < 0.001 -0.17 [-0.31 – -0.02] p = 0.025 -0.23 [-0.36 – -0.11] p < 0.001 -0.24 [-0.38 – -0.09] p = 0.002 MRI Imaging : GFAP † 0.79 [0.51–1.07] p < 0.001 0.25 [0.12–0.39] p < 0.001 0.06 [-0.04–0.16] p = 0.264 -0.04 [-0.16–0.07] p = 0.463 NFL † 0.21 [0.09–0.33] p < 0.001 0.85 [0.65–1.05] p < 0.001 0.55 [0.39–0.72] p < 0.001 -0.03 [-0.13–0.08] p = 0.631 Eotaxin -0.23 [-0.36 – -0.10] p < 0.001 -0.20 [-0.33 – -0.07] p = 0.002 -0.19 [-0.32 – -0.07] p = 0.002 -0.18 [-0.30 – -0.05] p = 0.006 MIP-1β -21.97 [-33.06 – -10.88] p < 0.001 -21.66 [-32.64 – -10.68] p < 0.001 -28.64 [-40.17 – -17.12] p < 0.001 -30.33 [-43.22 – -17.44] p < 0.001 IP-10 -0.26 [-0.37 – -0.14] p < 0.001 -0.14 [-0.26 – -0.02] p = 0.021 -0.20 [-0.32 – -0.08] p = 0.001 -0.19 [-0.31 – -0.07] p = 0.003 IL-9 -16.13 [-28.03 – -4.24] p = 0.008 -14.28 [-25.86 – -2.70] p = 0.016 -20.11 [-31.99 – -8.22] p = 0.001 -21.15 [-33.29 – -9.008] p < 0.001 FGF-basic -2.15 [-11.14–6.84] p = 0.638 -7.90 [-17.13–1.34] p = 0.093 -11.51 [-20.26 – -2.77] p = 0.010 -11.92 [-20.73 – -3.11] p = 0.008 † Indicates base-10 log transformed data. Significant p-values are bolded (α = 0.01, adjusted using Tukey’s HSD). Presented biomarkers are those that exhibited a significant group by time interaction or a significant main effect of group. Estimate refers to mean group differences as estimated by the mixed model; 95% CI is the 95% confidence interval of the estimated group difference. mTBI, mild traumatic brain injury; GFAP, Glial fibrillary acidic protein; NFL, Neurofilament light. [INSERT Table 2 HERE] Contrasts comparing biomarker levels over time in patients with mTBI (Table 3 ) revealed a large, significant decrease in GFAP across all subgroups between admission and 2 weeks. There was also a significant decrease in GFAP between 2 weeks and 3 months in MRI+, MRI- and CT- groups. The difference between admission and 12-month GFAP levels was large and significant in all subgroups. There was a significant increase in NFL concentrations from admission to 2 weeks in all subgroups, followed by a significant decrease in NFL concentrations from 2 weeks and 3 months and also from 3 months to 12 months in all subgroups. The difference between 2-week and 12-month NFL levels was large and significant in all subgroups. Though there was no significant increase in FGF-basic in the MRI- group between successive timepoints, a steadily growing difference at every timepoint between admission and 12 months is evident, culminating in a statistically significant increase in FGF-basic concentrations at 12 months compared to admission in the MRI- group. There are no differences in FGF-basic in the MRI + group, nor based on CT findings. Table 3 Differences in blood biomarker concentrations between timepoints in patients who were CT+/CT- and/or MRI+/MRI-. Admission – 2 weeks Estimate [95% CI] p -value 2 weeks – 3 months Estimate [95% CI] p -value 3 months – 12 months Estimate [95% CI] p -value Admission – 12 months Estimate [95% CI] p -value 2 weeks – 12 months Estimate [95% CI] p -value CT Imaging: GFAP † CT + Findings -0.71 [-1.10 – -0.31] p < 0.001 -0.20 [-0.38 – -0.02] p = 0.026 -0.10 [-0.23–0.03] p = 0.206 -1.003 [-1.38 – -0.02] p < 0.001 CT- Findings -0.38 [-0.50 – -0.27] p < 0.001 -0.09 [-0.13 – -0.05] p < 0.001 -0.01 [-0.05–0.02] p = 0.742 -0.49 [-0.60 – -0.37] p < 0.001 NFL † CT + Findings 0.75 [0.46–1.04] p < 0.001 -0.46 [-0.68 – -0.24] p < 0.001 -0.67 [-0.86 – -0.48] p < 0.001 -1.13 [-1.46 – -0.79] p < 0.001 CT- Findings 0.31 [0.23–0.39] p < 0.001 -0.23 [-0.29 – -0.17] p < 0.001 -0.15 [-0.21 – -0.10] p < 0.001 -0.38 [-0.48 – -0.29] p < 0.001 MRI Imaging : GFAP † MRI + Findings -0.85 [-1.21 – -0.48] p < 0.001 -0.26 [-0.40 – -0.12] p < 0.001 -0.10 [-0.20 – -0.01] p = 0.018 -1.21 [-1.56 – -0.12] p < 0.001 MRI- Findings -0.31 [-0.40 – -0.22] p < 0.001 -0.06 [-0.09 – -0.03] p < 0.001 -0.006 [-0.04–0.02] p = 0.950 -0.38 [-0.47 – -0.29] p < 0.001 NFL † MRI + Findings 0.89 [0.64–1.15] p < 0.001 -0.50 [-0.78 – -0.21] p < 0.001 -0.68 [-0.87 – -0.50] p < 0.001 -1.18 [-1.42 – -0.95] p < 0.001 MRI- Findings 0.25 [0.18–0.32] p < 0.001 -0.20 [-0.28 – -0.12] p < 0.001 -0.10 [-0.15 – -0.06] p < 0.001 -0.30 [-0.38 – -0.23] p < 0.001 FGF-basic MRI + Findings -1.88 [-9.25–5.49] p = 0.913 0.78 [-5.49–7.04] p = 0.989 1.71 [-2.49–5.91] p = 0.720 0.61 [-5.24–7.04] p = 0.993 MRI- Findings 3.87 [-0.33–8.07] p = 0.084 4.39 [0.22–8.57] p = 0.035 2.12 [-2.63–6.86] p = 0.659 10.38 [5.59–15.17] p < 0.001 † Indicates base-10 log transformed data. Significant p-values are bolded (α = 0.01, adjusted using Tukey’s HSD). Presented biomarkers are those that exhibited a significant group by time interaction. Estimate refers to mean timepoint differences as estimated by the mixed model; 95% CI is the 95% confidence interval of the estimated timepoint difference. mTBI, mild traumatic brain injury; GFAP, Glial fibrillary acidic protein; NFL, Neurofilament light. [INSERT Table 3 HERE] Biomarkers Able to Discriminate CT+ & MRI+ Table 4 presents the unpenalized odds ratios of the biomarkers selected by elastic net as important predictors of CT + and MRI+. The algorithm determined a combination of GFAP, NFL, MIP-1β, IP-10 and eotaxin to be predictive of both CT + and MRI + at admission and 2 weeks, while IL-1ra was uniquely predictive of intracranial findings on CT at those timepoints. At 3 months, NFL, MIP-1β and IP-10 were selected as predictors for CT + and MRI+. At 12 months, MIP-1β was predictive of findings in both modalities, while IP-10 uniquely predicted CT+, and IL-9 uniquely predicted MRI+. GFAP and NFL were positively predictive of intracranial findings (i.e. elevated in CT+/MRI + groups) while for all inflammation markers, except IL-1ra, associations were negative (significantly lower concentrations in CT+/MRI + groups). Table 4 Unpenalized odds ratios of the algorithmically-selected blood biomarkers predicting patients who were CT + and/or MRI+. Admission 2 weeks 3 months 12 months Odds Ratio [95% CI] Odds Ratio [95% CI] Odds Ratio [95% CI] Odds Ratio [95% CI] CT Imaging : GFAP † 1.26 [0.62–2.60] 1.10 [0.41–3.22] NFL † 1.82 [0.91–3.84] 1.81 [0.69–4.81] 1.77 [1.02–3.20] MIP-1β 0.46 [0.21–0.94] 0.45 [0.17–1.15] 0.56 [0.25–1.21] 0.42 [0.13–1.20] IP-10 † 0.63 [0.29–1.26] 0.66 [0.23–1.60] 0.43 [0.14–1.14] 0.57 [0.19–1.45] Eotaxin † 0.64 [0.26–1.21] 0.54 [0.14–1.09] IL-1ra 2.30 [1.07–6.68] 2.04 [0.73–12.71] MRI Imaging : GFAP † 2.59 [1.44–4.93] 1.02 [0.45–2.23] NFL † 1.47 [0.82–2.71] 4.52 [2.14–11.13] 3.37 [1.99–6.30] MIP-1β 0.56 [0.29–1.07] 0.78 [0.38–1.68] 0.49 [0.25–0.90] 0.36 [0.13–0.88] IP-10 † 0.57 [0.29–1.05] 0.45 [0.16–1.10] 0.55 [0.25–1.14] Eotaxin † 0.77 [0.40–1.27] 0.44 [0.14–0.87] IL-9 0.62 [0.28–1.36] † Indicates base-10 log transformed data. The blood biomarkers were algorithmically selected via elastic net. Each marker of inflammation or CNS injury was included in the model as a possible predictor, at their respective timepoints. The biomarkers selected for each model were fed into an ordinary logistic regression, from which the above odds ratios, with 95% CIs (confidence intervals) were calculated. 95% CI = 95% confidence interval; GFAP = Glial fibrillary acidic protein; NFL = neurofilament light; MIP = Macrophage Inflammatory Protein; IP = IFNγ-induced Protein; IL = Interleukin Figure 4 illustrates the ROC curves of the selected combination of biomarkers at each timepoint for classifying CT+/MRI + and AUC values are presented in Table 5 . The multivariable predictions yielded AUCs above 0.80 at all timepoints for both modalities, with AUCs > .90 for discriminating CT + from CT- at 2 weeks, and MRI + from MRI- at admission, 2 weeks and 3 months. Supplementary Figs. 2 and 3 present the ROC curves discriminating CT+/MRI + vs. CT-/MRI- for individual candidate biomarkers at each timepoint and supplementary tables 4 and 5 provide the corresponding AUC values, sensitivities, and specificities. Some notable single biomarkers for classifying CT + were: admission GFAP (sensitivity = 1.00, specificity = 0.58, AUC = 0.78); 2-week NFL (sensitivity = 1.00, specificity = 0.54, AUC = 0.81); 2-week eotaxin (sensitivity = 1.00, specificity = 0.51, AUC = 0.76); MIP-1β at all timepoints (AUC = 0.79 at admission, 2 weeks and 3 months and AUC = 0.81 at 12 months). Notable biomarkers for discriminating MRI + were: admission GFAP (sensitivity = 0.92, specificity = 0.63, AUC = 0.82); NFL at 2 weeks (sensitivity = 0.74, specificity = 0.90, AUC = 0.89) and 3 months (sensitivity = 0.68, specificity = 0.92, AUC = 0.86); and MIP-1β at 12 months (AUC = 0.81). Table 5 Classification accuracy of unpenalized models with algorithmically-selected biomarkers for discriminating patients who were CT + and/or MRI +. Sensitivity Specificity AUC [95% CI] Included Biomarkers CT Imaging: Admission 0.93 0.82 0.89 [0.82–0.96] GFAp, NFL, MIP-1β, IP-10, Eotaxin, IL-ra 2 weeks 0.91 0.83 0.90 [0.83–0.98] GFAp, NFL, MIP-1β, IP-10, Eotaxin, IL-ra 3 months 0.80 0.85 0.85 [0.74–0.97] NFL, MIP-1β, IP-10 12 months 0.80 0.81 0.82 [0.64–0.99] MIP-1β, IP-10 MRI Imaging : Admission 0.91 0.76 0.90 [0.85–0.96] GFAp, NFL, MIP-1β, IP-10, Eotaxin 2 weeks 0.89 0.90 0.94 [0.88–0.99] GFAp, NFL, MIP-1β, IP-10, Eotaxin 3 months 0.89 0.85 0.91 [0.84–0.98] NFL, MIP-1β, IP-10 12 months 0.78 0.84 0.82 [0.69–0.94] MIP-1β, IL-9 Area under the curve (AUCs), sensitivities and specificities from unpenalized models with the selected combination of blood biomarkers – selected via elastic net – for discriminating patients who were CT + and/or MRI + are presented. Biomarker coefficients set to 0 in all models: Tau, IFNγ, IL-8, MCP-1, IL-17A, TNF, FGF-basic, PDGF. mTBI, mild traumatic brain injury; ROC, Receiver Operating Characteristic; AUC, Area Under the Curve; GFAP = Glial fibrillary acidic protein; NFL = Neurofilament light; IFNγ = Interferon Gamma; IL = Interleukin; MIP = Macrophage Inflammatory Protein; MCP = Monocyte Chemoattractant Protein; IP = IFNγ-induced Protein; TNF = Tumor Necrotic Factor; FGF-basic = Basic Fibroblast Growth Factor; PDGF = Platelet-derived Growth Factor Discussion In this longitudinal study based on a representative sample of mixed-mechanism mild TBI patients with follow-up over one year, we present novel findings regarding the diagnostic utility of several blood-based biomarkers for predicting and discriminating intracranial injuries determined by acute-phase CT and MRI. We observed a shift in the diagnostic utility of blood biomarkers discriminating those with and without complicated mTBI based on timepoint of sampling after injury, representing a novel finding. In the acute to subacute phase, higher levels of CNS-injury biomarkers provided superior discrimination while lower levels of some inflammatory makers across the entire first year best identified those with intracranial injury in the acute phase. Biomarkers of CNS Injury Demonstrating High Diagnostic Utility for Intracranial Findings at Acute, Subacute and Chronic Phases The CNS-injury markers NFL and GFAP were highly discriminative biomarkers for both CT + and MRI+. We present novel findings regarding the ability of NFL measured at 2 weeks and 3 months to classify patients with mTBI who were CT + or MRI+, given previous studies 18–20 did not study NFL sampled during these subacute and chronic phases. Single-biomarker classification accuracy of CT+/MRI + is highest using NFL sampled at 2 weeks, while 3-month NFL also demonstrates high discriminability. Based on our results, we recommend NFL as a diagnostic biomarker for intracranial injury at 2 weeks especially, and potentially for up to 3 months after injury. GFAP’s diagnostic utility at admission is in line with previous research and current clinical recommendations. 9 GFAP in our sample demonstrated perfect sensitivity for discriminating CT + patients at admission and high sensitivity for discriminating MRI + patients, adding to the already solid evidence for its early diagnostic utility across imaging modalities. 9,19,20,43,44 Additionally, we demonstrated good diagnostic accuracy of GFAP sampled at 2 weeks, constituting a new finding that could guide futureclinical recommendations. Inflammation Markers Associated with Intracranial Findings on Both CT & MRI We demonstrated primarily negative associations of inflammation markers with intracranial findings at all timepoints, on both imaging modalities, i.e. inflammation markers were significantly reduced in CT+/MRI + groups compared to CT-/MRI-. This indicates that low levels or lack of an inflammatory response may be important diagnostically, which is counter to expectations. Specifically, the inflammation markers MIP-1β and IP-10 demonstrated significant reductions in both patients who were CT + and MRI + at almost all timepoints. Both markers are chemokines best known for their proinflammatory and chemotactic effects. 45,46 MIP-1β is a key player in many inflammatory conditions, but also appears to be critical for wound healing and has the ability to promote homeostasis, 45 while IP-10 plays an important role in CNS inflammation in a number of diseases, such as multiple sclerosis and Alzheimer’s disease. 46,47 Studies have previously evidenced MIP-1β and IP-10 upregulation post-injury in both animal models of TBI 48–50 and human TBI studies. 51–53 We have also confirmed that both biomarkers are elevated compared to controls in a previous analysis on this sample. 33 However, despite studies reporting associations between poorer TBI outcome and both chemokines, 54,55 a recent study comparing the chronic phase of mTBI in rats and humans reported a positive correlation between MIP-1β and IP-10 concentrations and fractional anisotropy in several brain regions, 51 interpreted as better white matter integrity as a function of higher concentrations of both chemokines. A similar relationship – higher levels of IP-10 and MIP-1β in those without intracranial findings – is reported here and speaks to the complex nature of inflammatory processes. Inflammation Markers Uniquely Associated with Intracranial Findings on MRI The growth factor FGF-basic, IL-9 and eotaxin were uniquely associated with MRI findings, perhaps due to biological mechanisms associated with TAI (TAI is the primary difference between MRI and CT groups, see Table 1 ). IL-9 and eotaxin were significantly reduced in patients who were MRI+ (but not CT+) at almost all timepoints. Eotaxin is a chemokine that has long been associated with cognitive decline during aging in both humans and rodent models. 56 Its elevations have recently been detected in a number of neurodegenerative and psychiatric disorders, 56 and it is particularly associated with memory deficits in Alzheimer’s disease. 57 In both animal models and human studies of more severe TBI, eotaxin has exhibited elevation in response to injury. 54,55,58 It is therefore unclear why no TAI would result in greater concentrations of eotaxin. We have previously shown that patients with mTBI without other injuries (e.g. skin contusions, abrasions, bone fractures etc.) also have lower eotaxin levels. 33 Taken together, eotaxin appears to be most elevated in those with the “mildest” form of mTBI. These contradictory effects warrant replication and further investigation into their underlying mechanisms in both human and animal studies. IL-9 is a pleiotropic cytokine primarily activated by Th9 cells. 59 Its major functions remain relatively underinvestigated, though it has been associated with a number of inflammatory diseases, specifically with regard to promoting immunotolerance. 59,60 Though some rodent studies have evidenced IL-9 elevation following mTBI, 61,62 its association with diagnostic and prognostic factors in mTBI is not yet investigated. Given IL-9 was algorithmically selected for the 12-month model predicting MRI findings, but not the corresponding CT model, it appears to demonstrate diagnostic specificity for MRI + above and beyond other biomarkers in the late/chronic phase of mTBI. The results presented here, coupled with our previous findings of IL-9 elevations in patients with PPCS, 32 warrant further investigation into the neurobiological mechanisms of both high and low IL-9 levels in mTBI. In our sample, FGF-basic showed steady increases in the MRI- group, culminating in statistically significantly greater concentrations in those who were MRI- compared to MRI + at 3 and 12 months. FGF-basic is a growth factor believed to broadly promote angio- and neurogenesis, to reduce pathogenic disruption of the blood-brain barrier and to increase neuronal survival. 63–65 Following experimental TBI, it has been shown in human cell cultures to reduce apoptosis of human brain endothelial cells 63 and to upregulate neuronal survival in the adult hippocampus of a TBI mouse model, 64 along with alleviating neurological deficits. Based on these findings, FGFs were recently proposed as a therapeutic treatment for stroke, which could have relevance also for patients with TBI. 65 Given FGFs’ evidenced neuroprotective effects, our results could indicate that MRI- patients (presumably, those without TAI) begin to naturally produce this beneficial growth factor given time, while the more severely injured MRI + patients are unable to do so within the first year following injury. Patients with mTBI who are MRI + may therefore represent a clinical target who would especially benefit from FGF therapies. Multi-Biomarker Panels Improve Discriminability Over Single Biomarkers at All Timepoints Single-biomarker discriminability for intracranial findings (CT+/MRI+) was poor to moderate for all inflammation markers and very good for GFAP sampled at admission and NFL sampled at 2 weeks and 3 months. Using a multi-biomarker panel with biomarkers selected for the model via elastic net regression improved discriminability at all timepoints, with excellent discriminability for CT + at admission and 2 weeks (≈ 0.90) and for MRI + at admission, 2 weeks and 3 months (AUC > 0.90). Though IL-ra did not exhibit significant group differences in the LMMs, it was included in the model predicting CT + status at admission and 2 weeks and was the only inflammation marker to show a positive association with intracranial findings. IL-ra is an endogenous receptor antagonist of IL-1r 66 that is under investigation as a therapeutic target for TBI. 67 Its elevation in CT + could therefore reflect endogenous repair mechanisms, although given it was only included in some CT models (but not MRI models), its clinical/biological relevance remains unclear. Put into context, our results show that a biomarker panel can identify with high accuracy patients with intracranial findings on CT/MRI, although similar discriminability can be achieved using only admission GFAP or 2-week NFL. Moreover, a greater number of biomarkers are discriminable of intracranial findings at early timepoints, though good discriminability is also achieved with a small selection of biomarkers at later timepoints. A validated panel of biomarkers for diagnosing intracranial injury late could help guide treatment plans in cases where CT/MRI were not performed acutely following injury. Taken together with our previous published works on this cohort (see 16,32,33 ), we conclude that the biomarkers of CNS injury GFAP and NFL show high diagnostic utility for both intracranial findings on CT/MRI and for discriminating patients with mTBI from controls. Inflammation markers on the other hand show greater prognostic relevance for PPCS and remain elevated in patients with mTBI compared to controls for at least one year after injury. They showed diagnostic relevance also for intracranial findings on CT/MRI, but at levels lower than their CNS-injury counterparts, and in a biologically/clinically unexpected direction that warrants further investigation. Limitations Our study acknowledges several limitations. Firstly, the sample is comprised of those who were willing to participate in comprehensive data collection, meaning it may not be generalizable to all patients with mTBI. Secondly, the small number of CT+/MRI + cases compared to non-cases increases the likelihood of statistical overfitting, as is reflected in relatively large confidence intervals. As such, we do not present our models with the goal of generating accurate predictive models, but rather in the hopes that our findings can inform the selection of biomarkers for analysis in future, large-scale studies (such as CENTER-TBI and TRACK-TBI). However, we consider the small number of intracranial findings a consequence of recruiting a more representative sample from both the ED and ambulatory clinics. Furthermore, our upper-age limit of 60 years was designed to reduce the burden of age-related findings on MRI scans, however this means that known age-related effects of TBI were not investigated in this study. Regarding biomarkers, we sampled total tau, although previous literature suggests phosphorylated tau, or the ratio of phosphorylated tau:total tau may be more relevant for TBI diagnostics, 68,69 and a new method for isolating brain-derived tau 70 may prove diagnostically superior in future studies. Similarly, IL-6 and IL-10 are two of the most studied inflammatory biomarkers in mTBI, 71 however they were not expressed in sufficient quantities in our multiplex assay, de facto implying no effects of mTBI on their levels. Studies have also identified other potential diagnostic inflammation markers 72 that we unfortunately did not assess here. Lastly, due to technical constraints, our admission timepoint includes all blood drawn within 72 hours from injury, although it is known that many biomarkers show greater discriminability within 24 hours. 16,43,71 All-in-all, the limitations of our paper highlight the need for rigorous meta-analyses and pooling of data across labs. Conclusions This study provides novel evidence regarding the diagnostic utility of a large array of CNS-injury and inflammation biomarkers for triaging mTBI patients likely to have complicated mTBI in the acute phase as well as in the chronic phase (up to 12 months). We demonstrated NFL’s significant diagnostic utility at subacute (2 weeks) and chronic (3 months) timepoints and confirmed GFAP’s acute diagnostic utility, along with evidencing its potential subacute (2-week) diagnostic utility. We also shed light on interesting mechanisms of peripheral inflammation in complicated mTBI, whereby there appears to be lower inflammation in patients with intracranial findings than those without from the acute phase throughout the first year of injury. This highlights important differences between inflammatory profiles in mild versus moderate-severe TBI cohorts. Lastly, our study provides promising evidence regarding the diagnostic utility of a panel of biomarkers, rather than single blood biomarkers, encouraging further research with the goal of fine-tuning and validating predictive diagnostic models based on biomarkers in mTBI. Declarations Ethical Approval and Consent to Participate The study was approved by the Regional Committee for Medical Research Ethics (2013/754), and participants provided written informed consent. Consent for Publication Not applicable Availability of Data and Materials Data, including biofluids, from the Trondheim mTBI study used in this manuscript can be accessed by contacting the last author ( [email protected] ) or Professor Toril Skandsen ( [email protected] ) by e-mail. Note that data will only be shared with qualified investigators in connection with planned investigations which have undergone scientific and ethical review and are in compliance with the European Union General Data Protection Regulations (GDPR), Norwegian laws and regulations, and NTNU regulations. The completion of a material transfer agreement (MTA) signed by an institutional official will be required. Analytic code used to conduct the analyses presented in this study are not available in a public repository; they may be available by emailing the first author ( [email protected] ). Competing Interests Henrik Zetterberg is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2022-01018 and #2019-02397), the European Union’s Horizon Europe research and innovation programme under grant agreement No 101053962, and Swedish State Support for Clinical Research (#ALFGBG-71320). He has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). Kaj Blennow holds the Torsten Söderberg Professorship in Medicine at the Royal Swedish Academy of Sciences, and was supported by the Swedish Research Council (#2017-00915), the Swedish Alzheimer Foundation (#AF651 742881), Hjärnfonden, Sweden (#FO2017-0243), and a grant (#ALFGBG-715986) from the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement. He has served as a consultant or at advisory boards for Alzheon, CogRx, Biogen, Lilly, Novartis and Roche Diagnostics, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB, a GU Venture-based platform company at the University of Gothenburg. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential competing interests Funding Statement The Trondheim mild TBI study was funded by the Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology (NTNU). Furthermore, the MRI image study was partly funded by the National Norwegian Advisory Unit for functional MRI and Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital. Additional, general financial support was obtained from, The Odd Fellow Foundation, and The Simon Fougner Hartmann Family Fund. First author Gerard Clarke is supported by the Centre for Innovative Ultrasound Solutions (CIUS) funded by the Research Council of Norway, grant number: 237887. Author Contributions Gerard Clarke organized blood data results, performed all statistical analyses, and drafted the manuscript for intellectual content. Toril Skandsen (PI of Trondheim mild TBI study), Anne Vik, and Asta Håberg designed the study, oversaw all data collection, contributed to analysis, planned, and revised the manuscript. Henrik Zetterberg and Kaj Blennow selected and oversaw CNS injury biomarker analyses, quality assessed data, and revised the manuscript. Turid Follestad is the statistician who approved all statistical methods and presentation of results in writing and figures/tables. Anne Vik provided neurosurgical expertise and revised the manuscript. Asta Håberg is the principal supervisor of this manuscript. All authors carefully revised the manuscript and approved the submitted version. Acknowledgements The authors would like to thank the staff at the Trondheim Municipal Emergency Department, the Department of Neurosurgery and the Department of Anaesthesiology, and Intensive Care Medicine for their cooperation during patient recruitment. Thanks to Stine Bjøralt for study coordination and to Jonas Stenberg, Simen Berg Saksvik and Migle Karaliute for recruitment of trauma controls and help with the blood samples. Thank you to Cathrine Einarsen for recruitment of participants, collection and organization of demographic, clinical and blood sample data. Thank you to Søren Erik Pischke and Tom Eirik Mollnes who oversaw inflammation biomarker analyses and quality assessment of that data. Thank you to Biobank 1 for the storage of our blood samples and thank you to the laboratory technicians of the Clinical Neurochemistry Laboratory at the Sahlgrenska University Hospital. 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Roles of Fibroblast Growth Factors and Their Therapeutic Potential in Treatment of Ischemic Stroke. Front Pharmacol. 2021;12:671131. 10.3389/fphar.2021.671131 . Helmy A, Guilfoyle MR, Carpenter KL, et al. Recombinant Human Interleukin-1 Receptor Antagonist in Severe Traumatic Brain Injury: A Phase II Randomized Control Trial. J Cereb Blood Flow Metab. 2014;34(5):845–51. 10.1038/jcbfm.2014.23 . Thome JG, Reeder EL, Collins SM et al. Contributions of Interleukin-1 Receptor Signaling in Traumatic Brain Injury. Front Behav Neurosci 2020;13. Rubenstein R, Chang B, Yue JK, et al. Comparing Plasma Phospho Tau, Total Tau, and Phospho Tau-Total Tau Ratio as Acute and Chronic Traumatic Brain Injury Biomarkers. JAMA Neurol. 2017;74(9):1063–72. 10.1001/jamaneurol.2017.0655 . Gardner RC, Rubenstein R, Wang KKW, et al. Age-Related Differences in Diagnostic Accuracy of Plasma Glial Fibrillary Acidic Protein and Tau for Identifying Acute Intracranial Trauma on Computed Tomography: A TRACK-TBI Study. J Neurotrauma. 2018;35(20):2341–50. 10.1089/neu.2018.5694 . Gonzalez-Ortiz F, Turton M, Kac PR, et al. Brain-derived tau: a novel blood-based biomarker for Alzheimer’s disease-type neurodegeneration. Brain. 2023;146(3):1152–65. 10.1093/brain/awac407 . Malik S, Alnaji O, Malik M et al. Inflammatory cytokines associated with mild traumatic brain injury and clinical outcomes: a systematic review and meta-analysis. Front Neurol 2023;14. Sharma R, Rosenberg A, Bennett ER, et al. A blood-based biomarker panel to risk-stratify mild traumatic brain injury. PLoS ONE. 2017;12(3):e0173798. 10.1371/journal.pone.0173798 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.tif Supplementary Figure 1: Penalized regression coefficients and Internal Validation Metrics of Elastic Net Regression Models Percentages describe the proportion of times the variable was included (i.e. not set to 0) in the 1000 bootstrapped re-samples, meaning higher percentages reflect greater certainty that the variable has a true relationship with the outcome (this is an analogue of the confidence interval for penalized variable selection methods). GFAP = Glial fibrillary acidic protein; NFL = Neurofilament light; IFNg = Interferon Gamma; IL = Interleukin; MIP = Macrophage Inflammatory Protein; MCP = Monocyte Chemoattractant Protein ; IP = IFNg-induced Protein; TNF = Tumor Necrotic Factor; FGF-basic = Basic Fibroblast Growth Factor; PDGF = Platelet-derived Growth Factor SupplementaryFigure2.eps Supplementary Figure 2: ROC curves for classifying patients who were CT+ using single biomarkers at each timepoint. ROC curves and AUC values indicate the diagnostic accuracy of each biomarker at each timepoint. ROC = Receiver Operating Characteristic; AUC = Area Under the Curve; GFAP = Glial Fibrillary Acidic Protein; NFL = Neurofilament Light; IFNg = Interferon Gamma; IL = Interleukin; MIP = Macrophage Inflammatory Protein; MCP = Monocyte Chemoattractant Protein; IP = IFNg-induced Protein; TNF = Tumor Necrotic Factor; FGF-basic = Basic Fibroblast Growth Factor; PDGF = Platelet-derived Growth Factor SupplementaryFigure3.eps Supplementary Figure 3: ROC curves for classifying patients who were MRI+ using single biomarkers at each timepoint.. ROC curves and AUC values indicate the diagnostic accuracy of each biomarker at each timepoint. ROC = Receiver Operating Characteristic; AUC = Area Under the Curve; GFAP = Glial Fibrillary Acidic Protein; NFL = Neurofilament Light; IFNg = Interferon Gamma; IL = Interleukin; MIP = Macrophage Inflammatory Protein; MCP = Monocyte Chemoattractant Protein; IP = IFNg-induced Protein; TNF = Tumor Necrotic Factor; FGF-basic = Basic Fibroblast Growth Factor; PDGF = Platelet-derived Growth Factor SupplementaryTables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 01 Mar, 2024 Reviewers agreed at journal 26 Feb, 2024 Reviewers invited by journal 26 Feb, 2024 Editor assigned by journal 19 Feb, 2024 Submission checks completed at journal 19 Feb, 2024 First submitted to journal 15 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3959215","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273687049,"identity":"bfd748ee-66c3-4efe-9545-ab9621e11a4e","order_by":0,"name":"Gerard Janez Brett Clarke","email":"","orcid":"","institution":"Norwegian University of Science and Technology (NTNU)","correspondingAuthor":false,"prefix":"","firstName":"Gerard","middleName":"Janez Brett","lastName":"Clarke","suffix":""},{"id":273687050,"identity":"00bf82a1-2b35-43cc-807b-c4315b094e43","order_by":1,"name":"Toril Skandsen","email":"","orcid":"","institution":"Trondheim University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Toril","middleName":"","lastName":"Skandsen","suffix":""},{"id":273687051,"identity":"e428506c-656e-422f-bb26-4b2209d3af19","order_by":2,"name":"Henrik Zetterberg","email":"","orcid":"","institution":"University of Gothenburg","correspondingAuthor":false,"prefix":"","firstName":"Henrik","middleName":"","lastName":"Zetterberg","suffix":""},{"id":273687052,"identity":"5f109e65-a580-40f4-8edd-54f718be4e1b","order_by":3,"name":"Turid Follestad","email":"","orcid":"","institution":"Norwegian University of Science and Technology (NTNU)","correspondingAuthor":false,"prefix":"","firstName":"Turid","middleName":"","lastName":"Follestad","suffix":""},{"id":273687053,"identity":"032f24fb-e43e-4262-8305-04c03f547744","order_by":4,"name":"Anne Vik","email":"","orcid":"","institution":"Trondheim University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"","lastName":"Vik","suffix":""},{"id":273687055,"identity":"a6e68537-23df-4dc1-8960-39d811d8e3ad","order_by":5,"name":"Alexander Olsen","email":"","orcid":"","institution":"Norwegian University of Science and Technology (NTNU)","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Olsen","suffix":""},{"id":273687057,"identity":"b8cf4bb9-e901-42f6-ad57-66a90906b60c","order_by":6,"name":"Kaj Blennow","email":"","orcid":"","institution":"University of Gothenburg","correspondingAuthor":false,"prefix":"","firstName":"Kaj","middleName":"","lastName":"Blennow","suffix":""},{"id":273687058,"identity":"98ca9163-51e8-4447-a321-bc0b107563ab","order_by":7,"name":"Asta Kristine Håberg","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYLACxgaGBBD1gEj1zHAtzAZgihQtbBJEaeHvP3/wA+MOuzx+6ePXqgt/3ElskG4+gFeLxI1kZgnGM8nFkn05ZbdnJDxLbJA5RsCuG8wMEoxtzIkbzvCk3eZJOJzYIJFjgFeH/PnDzD8Y2+oT9wO1FBOlxeBAMhvQlsOJG3jYjzETpcXwRrKZRWLb8WKJMzzM0jxph43bJNLw+0Xu/MHHNz62Vefx97A//Mxjc1i2XyL5AF4tYAAxlQfiHjbC6uGA/QEJikfBKBgFo2AkAQAtdUYIPLihwAAAAABJRU5ErkJggg==","orcid":"","institution":"Norwegian University of Science and Technology (NTNU)","correspondingAuthor":true,"prefix":"","firstName":"Asta","middleName":"Kristine","lastName":"Håberg","suffix":""}],"badges":[],"createdAt":"2024-02-15 16:50:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3959215/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3959215/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51446400,"identity":"41eaba3e-f54d-404e-af92-c18537a60ea9","added_by":"auto","created_at":"2024-02-21 18:14:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":152701,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart depicting enrolment and follow-up of patients with mTBI from admission to 12 months.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLoss to follow up and loss of data between timepoints due to technical issues are described.\u003c/p\u003e\n\u003cp\u003emTBI, mild traumatic brain injury\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3959215/v1/9b32471a380b708f677f4860.png"},{"id":51446399,"identity":"a1a7e8be-e658-4e66-a588-97e0c76f4af1","added_by":"auto","created_at":"2024-02-21 18:14:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":271410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiomarker concentrations over time in CT+ and CT- patients with mTBI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcentrations of candidate biomarkers are presented as box plots with median as the midline, box borders representing the 25th and 75th percentile and whiskers calculated as the 25th and 75th percentile + 1.5 * interquartile range. Points above and below the whiskers represent outliers. Individual data points are presented within the box-plots.\u003c/p\u003e\n\u003cp\u003e** = p \u0026lt; .01; *** = p \u0026lt; .001\u003c/p\u003e\n\u003cp\u003eGFAP = Glial Fibrillary Acidic Protein; NFL = Neurofilament Light; Tau; IFNg = Interferon Gamma; IL-8 = Interleukin-8; Eotaxin; MIP-1b = Macrophage Inflammatory Protein-1b; MCP-1 = Monocyte Chemoattractant Protein-1; IP-10 = IFNg-induced Protein-10; IL-17A = Interleukin-17A; IL-9 = Interleukin-9; TNF = Tumor Necrotic Factor;\u003cstrong\u003e \u003c/strong\u003eFGF-basic = Basic Fibroblast Growth Factor; PDGF = Platelet-derived Growth Factor IL-1ra = Interleukin-1ra\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3959215/v1/ccdd04cf9a9a6cb68c10ff95.png"},{"id":51446404,"identity":"78eedf95-2941-43d4-ad41-ddc6ef7a1ecf","added_by":"auto","created_at":"2024-02-21 18:14:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":229551,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiomarker concentrations over time in MRI+ and MRI- patients with mTBI.\u003c/strong\u003eConcentrations of candidate biomarkers are presented as box plots with median as the midline, box borders representing the 25th and 75th percentile and whiskers calculated as the 25th and 75th percentile + 1.5 * interquartile range. Points above and below the whiskers represent outliers. Individual data points are presented within the box-plots.\u003c/p\u003e\n\u003cp\u003e** = p \u0026lt; .01; *** = p \u0026lt; .001\u003c/p\u003e\n\u003cp\u003eGFAP = Glial Fibrillary Acidic Protein; NFL = Neurofilament Light; Tau; IFNg = Interferon Gamma; IL-8 = Interleukin-8; Eotaxin; MIP-1b = Macrophage Inflammatory Protein-1b; MCP-1 = Monocyte Chemoattractant Protein-1; IP-10 = IFNg-induced Protein-10; IL-17A = Interleukin-17A; IL-9 = Interleukin-9; TNF = Tumor Necrotic Factor;\u003cstrong\u003e \u003c/strong\u003eFGF-basic = Basic Fibroblast Growth Factor; PDGF = Platelet-derived Growth Factor IL-1ra = Interleukin-1ra\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3959215/v1/d58f67e12655d92839760aee.png"},{"id":51447161,"identity":"1406fe14-e204-44f8-8eb7-295f684e6d82","added_by":"auto","created_at":"2024-02-21 18:22:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96872,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves indicating diagnostic accuracy for the algorithmically-selected biomarker combinations discriminating CT+ and MRI+ patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curves are based on the optimal combination of biomarkers for predicting CT+ and MRI+ patients at each timepoint. AUC values for each timepoint are indicated in the plot.\u003c/p\u003e\n\u003cp\u003eBiomarkers included for findings on CT: \u003cstrong\u003eAdmission: \u003c/strong\u003eGFAp, NFL, MIP-1b, IP-10, Eotaxin, IL-ra; \u003cstrong\u003e2 weeks: \u003c/strong\u003eGFAp, NFL, MIP-1b, IP-10, Eotaxin, IL-ra \u003cstrong\u003e3 months: \u003c/strong\u003eNFL, MIP-1b, IP-10\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e12 months: \u003c/strong\u003eMIP-1b, IP-10\u003c/p\u003e\n\u003cp\u003eBiomarkers included for findings on MRI: \u003cstrong\u003eAdmission: \u003c/strong\u003eGFAp, NFL, MIP-1b, IP-10, Eotaxin; \u003cstrong\u003e2 weeks: \u003c/strong\u003eGFAp, NFL, MIP-1b, IP-10, Eotaxin \u003cstrong\u003e3 months: \u003c/strong\u003eNFL, MIP-1b, IP-10\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e12 months: \u003c/strong\u003eMIP-1b, IL-9\u003c/p\u003e\n\u003cp\u003eROC, Receiver Operating Characteristic; AUC, Area Under the Curve; GFAP = Glial fibrillary acidic protein; NFL = Neurofilament light; IFNg = Interferon Gamma; IL = Interleukin; MIP = Macrophage Inflammatory Protein; MCP = Monocyte Chemoattractant Protein; IP = IFNg-induced Protein; TNF = Tumor Necrotic Factor; FGF-basic = Basic Fibroblast Growth Factor; PDGF = Platelet-derived Growth Factor\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3959215/v1/562b2228c3d3ec01792987be.png"},{"id":51504848,"identity":"97fa31f6-68ad-4e5f-bf76-6446f0a18496","added_by":"auto","created_at":"2024-02-22 18:39:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1595588,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3959215/v1/d6849572-e49d-4b33-8b33-40f8f13db618.pdf"},{"id":51446401,"identity":"d7198904-25ca-4c7d-b6d7-557e715e4437","added_by":"auto","created_at":"2024-02-21 18:14:30","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":799078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1:\u003c/strong\u003e \u003cstrong\u003ePenalized regression coefficients and Internal Validation Metrics of Elastic Net Regression Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePercentages describe the proportion of times the variable was included (i.e. not set to 0) in the 1000 bootstrapped re-samples, meaning higher percentages reflect greater certainty that the variable has a true relationship with the outcome (this is an analogue of the confidence interval for penalized variable selection methods).\u003c/p\u003e\n\u003cp\u003eGFAP = Glial fibrillary acidic protein; NFL = Neurofilament light; IFNg = Interferon Gamma; IL = Interleukin; MIP = Macrophage Inflammatory Protein; MCP = Monocyte Chemoattractant Protein ; IP = IFNg-induced Protein; TNF = Tumor Necrotic Factor; FGF-basic = Basic Fibroblast Growth Factor; PDGF = Platelet-derived Growth Factor\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3959215/v1/b74c508ee92fd70b1eff66ee.tif"},{"id":51446405,"identity":"98258ab0-1805-421a-a24f-c24e4edc89e5","added_by":"auto","created_at":"2024-02-21 18:14:30","extension":"eps","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":171065,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2: ROC curves for classifying patients who were CT+ using single biomarkers at each timepoint.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curves and AUC values indicate the diagnostic accuracy of each biomarker at each timepoint.\u003c/p\u003e\n\u003cp\u003eROC = Receiver Operating Characteristic; AUC = Area Under the Curve; GFAP = Glial Fibrillary Acidic Protein; NFL = Neurofilament Light; IFNg = Interferon Gamma; IL = Interleukin; MIP = Macrophage Inflammatory Protein; MCP = Monocyte Chemoattractant Protein; IP = IFNg-induced Protein; TNF = Tumor Necrotic Factor; FGF-basic = Basic Fibroblast Growth Factor; PDGF = Platelet-derived Growth Factor\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.eps","url":"https://assets-eu.researchsquare.com/files/rs-3959215/v1/c52a3bead1dfd8d999b156e0.eps"},{"id":51446403,"identity":"3876aab0-c5cb-4c47-bb5e-b432c08f64fd","added_by":"auto","created_at":"2024-02-21 18:14:30","extension":"eps","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":183332,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3: ROC curves for classifying patients who were MRI+ using single biomarkers at each timepoint..\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curves and AUC values indicate the diagnostic accuracy of each biomarker at each timepoint.\u003c/p\u003e\n\u003cp\u003eROC = Receiver Operating Characteristic; AUC = Area Under the Curve; GFAP = Glial Fibrillary Acidic Protein; NFL = Neurofilament Light; IFNg = Interferon Gamma; IL = Interleukin; MIP = Macrophage Inflammatory Protein; MCP = Monocyte Chemoattractant Protein; IP = IFNg-induced Protein; TNF = Tumor Necrotic Factor; FGF-basic = Basic Fibroblast Growth Factor; PDGF = Platelet-derived Growth Factor\u003c/p\u003e","description":"","filename":"SupplementaryFigure3.eps","url":"https://assets-eu.researchsquare.com/files/rs-3959215/v1/066cb52ee0848fd95385a79d.eps"},{"id":51446406,"identity":"c92b11f6-c5a3-4904-9888-c25470b58457","added_by":"auto","created_at":"2024-02-21 18:14:30","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":90049,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-3959215/v1/5f62494d8270c6719a66e6b6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Blood-Based CNS-Injury and Inflammation Biomarkers Sampled at Acute, Subacute, and Chronic phases After Mild TBI Demonstrate Diagnostic Utility For Patients With and Without Intracranial Injuries on Acute CT and MRI","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMild traumatic brain injury (mTBI) is the most common type of brain injury, representing up to 90% of all head injury cases.\u003csup\u003e1,2\u003c/sup\u003e mTBI encompasses a wide range of injury severities, from blows to the head with limited symptoms and rapid recovery, to injuries involving intracranial abnormalities detectable with neuroimaging techniques. Individuals with mTBI concurrent with traumatic intracranial findings determined by computed tomography (CT) or magnetic resonance imaging (MRI) are considered to have experienced a complicated mTBI. These individuals are at increased risk for cognitive sequalae and persistent post-concussive symptoms.\u003csup\u003e3\u0026ndash;6\u003c/sup\u003e Given this, low-cost and reliable identification of patients with potential intracranial injury in the emergency department (ED), such as through the utilization of blood-based biomarkers, could drastically improve patient triage protocols. Further, since most intracranial injuries following mTBI resolve over time, blood biomarkers able to reliably identify patients with potential complicated mTBI at later timepoints could be of great clinical utility. This could prove helpful also for those individuals with mTBI involved in litigation.\u003c/p\u003e \u003cp\u003eCT is currently the mainstay imaging technique used in the acute care of patients with TBI,\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e due in part to its speed and cost-effectiveness compared to MRI. Two biomarkers: UCH-L1 and GFAP are currently approved by the Food and Drug Administration in the US to assess the likelihood of mTBI-related intracranial injury in the acute phase.\u003csup\u003e9\u003c/sup\u003e In Scandinavia, blood S100B is recommended for triaging patients with mTBI to CT scanning during the first 24 hours after injury.\u003csup\u003e10\u003c/sup\u003e However, these biomarkers are only approved for patients presenting acutely, and it is unclear whether other biomarkers, or a combination of biomarkers could improve diagnostic accuracy in the acute phase as well as later. Furthermore, MRI is known to be more sensitive to certain brain injuries than CT, particularly in identifying traumatic axonal injury (TAI), including microbleeds\u003csup\u003e8,11,12\u003c/sup\u003e that are difficult to observe on CT.\u003csup\u003e3\u0026ndash;5\u003c/sup\u003e There are currently no recognized guidelines regarding which patients should be referred for clinical MRI examination instead of CT,\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e although emerging evidence suggests MRI findings improve functional outcome prediction,\u003csup\u003e11\u003c/sup\u003e and many patients with no observable lesions on CT have findings on MRI.\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBlood-based biomarker diagnostics in mTBI are currently being used in the acute phase, though there is currently no approved diagnostic biomarker at later timepoints. NFL \u0026ndash; considered a surrogate marker for axonal injury\u003csup\u003e14\u003c/sup\u003e \u0026ndash; is a promising candidate in this regard, given its late peak (~\u0026thinsp;10 days after injury),\u003csup\u003e15\u003c/sup\u003e and protracted course of elevation for at least 3 months in mTBI\u003csup\u003e16\u003c/sup\u003e and up to 5 years following more severe TBI.\u003csup\u003e17\u003c/sup\u003e Recent studies have suggested acutely measured NFL is able to discriminate intracranial abnormalities in patients with mTBI on both CT and MRI,\u003csup\u003e18\u0026ndash;20\u003c/sup\u003e though its diagnostic utility for this at later timepoints is yet to be determined.\u003c/p\u003e \u003cp\u003eTBI is also known to trigger increased inflammatory activity.\u003csup\u003e21,22\u003c/sup\u003e So far, clinical research on TBI and inflammation has primarily been performed in moderate-severe TBI cohorts.\u003csup\u003e21,22\u003c/sup\u003e Recent work on mild TBI cohorts has evidenced associations between certain inflammation markers and complicated mTBI (determined by both CT/MRI).\u003csup\u003e20,23\u0026ndash;25\u003c/sup\u003e However, many markers of inflammation in the context of mTBI diagnosis remain unexplored, as studies often pre-selected a small number of inflammation markers for analysis. Moreover, the focus has thus far been limited to acutely measured peripheral inflammation markers, highlighting the need for studies investigating chronic or late-phase inflammation with regard to mTBI diagnostics.\u003c/p\u003e \u003cp\u003eOur study aims to predict complicated mTBI on CT/MRI taken during the acute phase using a large array of candidate biomarkers related to CNS damage/inflammation obtained at admission (within 72 hours), 2 weeks (\u0026plusmn;\u0026thinsp;3 days), 3 months (\u0026plusmn;\u0026thinsp;2 weeks) and 12 months (\u0026plusmn;\u0026thinsp;1 month). Predictive models were generated with both single and multi-panel biomarkers and assessed using area under the curve analyses (AUCs). Moreover, we aimed to elucidate whether there were any biomarker profiles uniquely associated with findings on MRI vs. CT. In this manner, we aim to uncover accurate diagnostic biomarkers from the acute to chronic phase of mTBI, with the goal of informing more personalized treatment protocols, in line with current goals of developing precision-based medicine approaches.\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Recruitment\u003c/h2\u003e \u003cp\u003eThe Trondheim mTBI study is a large-scale prospective cohort study with follow up for 12 months in patients with mTBI between 16\u0026ndash;60 years of age. Patients with mTBI (n\u0026thinsp;=\u0026thinsp;378) were included from April 1st 2014 to December 15th 2015. They were recruited from two emergency departments (EDs): St. Olavs hospital (Trondheim University Hospital), a regional level 1 trauma center in Trondheim, Norway, and Trondheim Municipal Emergency clinic, a general practitioner-run, 24-hour/7-day out-patient clinic.\u003c/p\u003e \u003cp\u003eInclusion criteria were having sustained a mild TBI according to World Health Organization criteria,\u003csup\u003e27\u003c/sup\u003e i.e. Glasgow Coma Scale (GCS) score of 13\u0026ndash;15, \u0026lt; 30 minutes loss of consciousness (LOC), and \u0026lt;\u0026thinsp;24 hours post-traumatic amnesia (PTA). Exclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) non-fluency in the Norwegian language, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) pre-existing neurological, psychiatric, somatic, or substance use disorder; determined to be severe enough to interfere with follow-up and outcome assessment, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) a prior history of a complicated mild (i.e. having trauma-related intracranial findings on CT or MRI), moderate or severe TBI, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) other major trauma that could interfere with follow-up or outcome assessment, or (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) presentation\u0026thinsp;\u0026gt;\u0026thinsp;48 hours after the initial trauma. The sub-cohort selected for this investigation were all patients with mTBI (see Skandsen et al.\u003csup\u003e28\u003c/sup\u003e and Einarsen et al.\u003csup\u003e13\u003c/sup\u003e for more details regarding patient enrolment and clinical ratings) who had blood data collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical Information\u003c/h2\u003e \u003cp\u003e Clinical information was obtained from patient interviews and medical records. LOC was rated as present only if observed. Duration of PTA was recorded as time after injury for which the patient had no continuous memory (\u0026gt;\u0026thinsp;0 min and \u0026lt;\u0026thinsp;1 hour, or 1\u0026ndash;24 hours). GCS score was assessed in the ED or inferred from records.\u003csup\u003e29\u003c/sup\u003e Presence of injuries to parts of the body other than the head (e.g. dislocations, fractures, soft tissue injuries in need of treatment) was recorded based on self-report and ED/hospital records. Skin abrasions and contusions were not included in this rating.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCT imaging\u003c/h2\u003e \u003cp\u003eNon-contrast CT was performed on a Siemens Somatom Sensation 64 row scanner as part of the initial clinical assessment (within 24 hours of injury), according to the Scandinavian Guidelines for Head Injury Management.\u003csup\u003e10\u003c/sup\u003e The intracranial traumatic findings were classified by an experienced neuroradiologist into contusion, epidural hematoma (EDH), traumatic sub-arachnoid hemorrhage (tSAH) and subdural hematoma (SDH). The CT scans from patients with intracranial traumatic findings on MRI were later reviewed by an experienced neuroradiologist and a consultant in physical medicine and rehabilitation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMRI imaging\u003c/h2\u003e \u003cp\u003eSubjects underwent a standardized brain MRI scan within 72 hours of injury.\u003csup\u003e30\u003c/sup\u003e All MRI scans were acquired with the same protocol on the same 3.0 Tesla Siemens Skyra MRI scanner with a 32-channel head coil. The protocol included 3D volumes with T1-weighted (Magnetization Prepared Rapid Acquisition Gradient Echo), T2-weighted, Fluid-attenuated inversion recovery (FLAIR), and susceptibility-weighted (SWI) scans. The clinical scans were read by neuroradiologists according to standard criteria, and the inter-rater reliability was moderate to good.\u003csup\u003e13\u003c/sup\u003e TAI was diagnosed and graded as described previously.\u003csup\u003e31\u003c/sup\u003e Two patients with a positive CT scan were unable to undergo MRI at inclusion, hence the reading of the CT scan was used to describe TBI-related intracranial findings in place of MRI. More detailed patient MRI results and their development over time are presented in Einarsen et al.\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBlood Samples\u003c/h2\u003e \u003cp\u003eTime of blood sampling was measured as time from injury. Participants had their blood drawn at admission (within 72 h post-injury), then at 2 weeks (\u0026plusmn;\u0026thinsp;3 days), 3 months (\u0026plusmn;\u0026thinsp;2 weeks), and 12 months (\u0026plusmn;\u0026thinsp;1 month). Plasma samples were obtained with EDTA gel tubes which were immediately put on ice and centrifuged for 10 min at 4\u0026deg;C on 2,000 g within 30 min of acquisition and aliquoted into eight 0.5 mL Nunc tubes which were immediately frozen at -80\u0026deg;C. The tubes remained stored at -80\u0026deg;C until two tubes were retrieved and transported in the frozen condition to the labs that analyzed the CNS injury and the inflammation makers, respectively. No freeze thaw cycles were necessary. Plasma GFAP, NFL and tau concentrations were measured using the validated and commercially available Human Neurology 4-Plex A assay (N4PA) on an HD-1 Single molecule array (Simoa) instrument, according to instructions from the manufacturer (Quanterix, Billerica, MA). The measurements were performed in one round of experiments using one batch of reagents by board-certified laboratory technicians blinded to the clinical data. See Clarke et al.\u003csup\u003e32\u003c/sup\u003e for further details.\u003c/p\u003e \u003cp\u003eFor inflammation markers, the plasma samples were analyzed using a commercial fluorescence magnetic bead-based immunoassay, with high-sensitivity detection range and precision (Bio-Plex Human Cytokine 27-Plex, Bio-Rad Laboratories, Inc., Hercules, CA, USA). 27 cytokines were analyzed in total (see Chaban et al.\u003csup\u003e33\u003c/sup\u003e for full list). Plasma samples were diluted 1:4 in Sample Diluent (Bio-Rad Laboratories, Inc.). A lower detection limit for the cytokines in the low picogram/milliliter range (\u0026lt;\u0026thinsp;20 pg/mL for all cytokines) was determined automatically by the software based on the standard curve for each inflammation marker. Only markers that were present in methodologically and clinically meaningful amounts, according to our previous experience,\u003csup\u003e34\u003c/sup\u003e in more than 75% of all samples during the observation period, were selected for further study. These were: IL-1 receptor antagonist (IL-1ra), IL-8, IL-9, IL-17A, eotaxin-1 (CCL11), basic fibroblast growth factor (FGF-basic), interferon gamma (IFN-γ), IFN-γ-inducing protein 10 (IP-10/CXCL10), monocyte chemoattractant protein 1 (MCP-1/CCL2), macrophage inflammatory protein-1-beta (MIP-1β/CCL4), platelet-derived growth factor-BB (PDGF-BB), tumor necrosis factor (TNF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDemographic and clinical variables for the total number of patients included in this study are summarized by frequencies and percentages or means and standard deviations, as appropriate. Descriptive statistics (mean, standard deviation, median, interquartile range and range) for biomarkers are presented per timepoint in supplementary tables 1 and 2.\u003c/p\u003e \u003cp\u003eLinear mixed model (LMM) analyses were conducted with groups separated by CT+/CT- and MRI+/MRI-. LMM assesses the time course of multiple groups, allowing comparisons both across timepoints within a certain group, and group comparisons at a given timepoint. Time, group, and a time-by-group interaction were entered as fixed effects. The interaction coefficient was retained in all models regardless of statistical significance. To account for within-subject correlations, a covariance structure for the total residuals was selected among a set of candidate models: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) a model with an unstructured correlation matrix and homogeneous residual variance (UC-model), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) a random intercept only model (RI-model), and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) a random intercept model with heterogenous residual variances (HV-model). A fully unstructured covariance structure, including heterogeneous variances, was ruled out due to lack of convergence of the fitting algorithm. Model fit was assessed using a pragmatic combination of Aikake information criterion (AIC) and log-likelihood ratio (LR) tests, aimed at selecting the most parsimonious model with an acceptable model fit (without considering a specific threshold of significance). For biomarkers showing a significant group effect or a significant time-by-group interaction (main effects presented in supplementary table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), group differences at each timepoint were assessed using post-hoc contrasts adjusted by Tukey\u0026rsquo;s honest significant difference (HSD). Within-group changes across time were assessed only if there was a significant time-by-group interaction. Significant effects of time with no effect of group were not of interest, as these have been previously reported on.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTo determine the best combination of candidate biomarkers for predicting patients who were CT\u0026thinsp;+\u0026thinsp;and MRI+, elastic net regression was performed using all candidate biomarkers at each timepoint as possible predictors. Elastic net models are generalized linear models fit with a hybrid of lasso and ridge penalty functions.\u003csup\u003e35\u003c/sup\u003e Ridge regression penalizes the square of the regression coefficients for the predictors, shrinking coefficients for the least important predictors toward zero. Lasso imposes a penalty on the absolute value of the coefficients, shrinking them by a constant factor, thereby selecting a subset of predictors by shrinking the coefficients of the least predictive predictors to zero. Whereas ridge retains all predictors, adjusting for relative predictive importance, lasso tends to select only one predictor from a group of correlated predictors. Elastic net is a useful combination of both, performing shrinkage selection while enabling the inclusion of collinear predictors in the final model. This means all variables that have a meaningful effect on the outcome can be selected by the procedure, even if they are strongly correlated, while predictors unrelated to outcome will be set to 0.\u003c/p\u003e \u003cp\u003eTo determine the optimal penalization parameters and internally validate models, 5-fold cross-validation (CV) was used, testing over a grid of α and λ sequences and selecting the combination yielding the maximal AUC value. Uncertainty in variable selection was assessed by repeating the penalized regression procedure for each model in 1000 bootstrap samples. The uncertainty for each of the variables was assessed as the proportion of the 1000 bootstrap samples when the variable\u0026rsquo;s coefficient was not set to 0, i.e. the number of times the procedure determined the variable had a meaningful effect on outcome (see supplementary table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026amp; supplementary Fig.\u0026nbsp;1). For the subset of biomarkers selected via elastic net, we refit ordinary logistic regression models to obtain unpenalized parameter estimates. A complete-case per timepoint approach was used. Unpenalized regression coefficients are standardized for comparability between biomarkers.\u003c/p\u003e \u003cp\u003eThe ability of each biomarker \u0026ndash; at the four timepoints \u0026ndash; to discriminate patients with intracranial findings on CT or MRI from those without, was assessed with receiver operating curves (ROCs) and area under the curves (AUCs). The optimal pair of sensitivity and specificity was defined as the one corresponding to the Youden\u0026rsquo;s J statistic.\u003csup\u003e36\u003c/sup\u003e AUCs were calculated based on the unpenalized multivariable models. AUCs were classified based on the following system: 0.90-1.00\u0026thinsp;=\u0026thinsp;excellent, 0.80\u0026ndash;0.90\u0026thinsp;=\u0026thinsp;very good, 0.70\u0026ndash;0.80\u0026thinsp;=\u0026thinsp;moderate, 0.60\u0026ndash;0.70\u0026thinsp;=\u0026thinsp;poor, \u0026lt; 60\u0026thinsp;=\u0026thinsp;negligible.\u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTo provide some protection against false positives due to multiple comparisons, the significance level was set to α\u0026thinsp;=\u0026thinsp;0.01. P-values for unpenalized regression models are not provided, as p-values after variable selection tend to be underestimated. A small number of outliers (n\u0026thinsp;=\u0026thinsp;5) were determined and removed based on a pragmatic assessment of leverage values from LMMs and visual inspection of the data.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using R version 4.2.2.\u003csup\u003e38\u003c/sup\u003e Linear mixed models were conducted using the \u003cem\u003enlme\u003c/em\u003e package.\u003csup\u003e39\u003c/sup\u003e Post-hoc linear mixed model contrasts were conducted using the \u003cem\u003eemmeans\u003c/em\u003e package.\u003csup\u003e40\u003c/sup\u003e Elastic net regression was conducted using the \u003cem\u003eglmnet\u003c/em\u003e package.\u003csup\u003e41\u003c/sup\u003e Unpenalized logistic regressions were conducted in base R. AUC and ROC curves were computed using the \u003cem\u003epROC\u003c/em\u003e package.\u003csup\u003e42\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe flow chart in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes sample numbers for each timepoint and reasons for drop out/data loss. 207 had blood data at one or more timepoints. At 2 weeks there were 177 with blood data available, at 3 months 172, and by 12 months, 159 patients remained in the study, giving a long-term retention rate of 77%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a detailed summary of the demographic and clinical characteristics of the patients with mTBI included. Most were men (63.3%), with GCS scores of 15 in 76.3%. LOC was observed in 47.3%, and PTA between 1 hour and 24 hours in 30.9%, while 36.7% experienced concurrent extracranial injuries. A total of 8% of patients were CT+ (16% were not triaged to CT) and 12% were MRI+. 40.0% of patients had the same intracranial injuries on CT and MRI, 24.0% had additional or different findings on MRI compared to CT and 36.0% had findings on MRI but none on CT.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and injury characteristics of total patients with mild TBI included in the study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients with mTBI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (63.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (36.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at Injury\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age, y (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.4 (13.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge range, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGCS score (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (16.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (76.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot recorded\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLOC (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnobserved LOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (52.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObserved LOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (47.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePTA duration (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTA\u0026thinsp;\u0026lt;\u0026thinsp;1 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (69.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTA between 1\u0026ndash;24 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (30.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInjury Mechanism mTBI (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (38.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraffic Accident\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (27.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSports Accident\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (12.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViolence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHit Object \u0026amp; Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (6.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExtracranial Injuries\u003c/b\u003e \u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (63.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (36.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntracranial Finding on CT (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContusion only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntracranial hematoma only*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (4.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContusion and hematoma*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (76.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot triaged to CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (16.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntracranial Finding on MRI (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAI only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContusion only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntracranial hematoma only*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAI and contusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContusion and hematoma*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182 (88.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntracranial Finding on CT vs. MRI (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContusion on CT and MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntracranial hematoma* on CT and MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContusion and hematoma* on CT and MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContusion on CT and contusion and TAI on MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematoma* on CT and contusion and hematoma* on MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematoma* on CT and contusion on MRI*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAI and contusion on MRI, no CT findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAI findings on MRI, no CT findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAI findings on MRI, CT not performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo findings on either modality incl. not triaged to CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182 (88.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviations: mTBI\u0026thinsp;=\u0026thinsp;mild traumatic brain injury; GCS\u0026thinsp;=\u0026thinsp;Glasgow Coma Score; LOC\u0026thinsp;=\u0026thinsp;Loss of Consciousness; PTA\u0026thinsp;=\u0026thinsp;Post-Traumatic Amnesia; MRI\u0026thinsp;=\u0026thinsp;Magnetic Resonance Imaging\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e Extracranial injuries refer to the presence of concurrent injuries to parts of the body other than the head (e.g. bone fracture). * Intracranial hematoma includes epidural hematomas, subdural hematomas, and traumatic subarachnoid hemorrhaging.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[INSERT Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e HERE]\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLongitudinal Evolution of Biomarkers Based on Presence of Intracranial Findings\u003c/h2\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depict the temporal profiles of the biomarkers based on CT\u0026thinsp;+\u0026thinsp;versus CT- and MRI\u0026thinsp;+\u0026thinsp;versus MRI-, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGFAP concentrations were significantly elevated in both CT\u0026thinsp;+\u0026thinsp;and MRI\u0026thinsp;+\u0026thinsp;patients compared to CT- and MRI- at admission and 2 weeks, while NFL was significantly elevated at admission, 2 weeks and 3 months (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). MIP-1β was significantly lower in CT\u0026thinsp;+\u0026thinsp;and MRI\u0026thinsp;+\u0026thinsp;patients at all timepoints, while IP-10 was significantly lower in CT\u0026thinsp;+\u0026thinsp;and MRI\u0026thinsp;+\u0026thinsp;patients at admission, 3 months and 12 months. Biomarkers uniquely associated with MRI\u0026thinsp;+\u0026thinsp;were eotaxin, IL-9 and FGF-basic. Eotaxin was significantly lower in the MRI\u0026thinsp;+\u0026thinsp;group at all timepoints, IL-9 was significantly lower at admission, 3 months and 12 months, and FGF-basic was significantly lower at 3 months and 12 months only. No biomarker was uniquely associated with CT+.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroup contrasts of blood biomarker concentrations per timepoint between patients who were CT+/CT- and MRI+/MRI-.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003cp\u003eEstimate [95% CI]\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 weeks\u003c/p\u003e \u003cp\u003eEstimate [95% CI]\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 months\u003c/p\u003e \u003cp\u003eEstimate [95% CI]\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 months\u003c/p\u003e \u003cp\u003eEstimate [95% CI]\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT Imaging:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFAP \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60 [0.30\u0026ndash;0.90]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27 [0.10\u0026ndash;0.45]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17 [0.03\u0026ndash;0.30]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08 [-0.07\u0026ndash;0.23]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFL \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23 [0.03\u0026ndash;0.44]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 [0.45\u0026ndash;0.90]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44 [0.23\u0026ndash;0.66]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.07 [-0.29\u0026ndash;0.14]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIP-1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-24.26 [-38.28 \u0026ndash; -10.23]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-26.54 [-40.05 \u0026ndash; -13.02]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-25.98 [-41.01 \u0026ndash; -10.95]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-29.27 [-44.50 \u0026ndash; -14.03]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP-10 \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.25 [-0.39 \u0026ndash; -0.11]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.17 [-0.31 \u0026ndash; -0.02]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.23 [-0.36 \u0026ndash; -0.11]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.24 [-0.38 \u0026ndash; -0.09]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMRI Imaging\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFAP \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 [0.51\u0026ndash;1.07]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 [0.12\u0026ndash;0.39]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06 [-0.04\u0026ndash;0.16]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04 [-0.16\u0026ndash;0.07]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFL \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21 [0.09\u0026ndash;0.33]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 [0.65\u0026ndash;1.05]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55 [0.39\u0026ndash;0.72]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03 [-0.13\u0026ndash;0.08]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEotaxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.23 [-0.36 \u0026ndash; -0.10]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.20 [-0.33 \u0026ndash; -0.07]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.19 [-0.32 \u0026ndash; -0.07]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.18 [-0.30 \u0026ndash; -0.05]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIP-1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-21.97 [-33.06 \u0026ndash; -10.88]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-21.66 [-32.64 \u0026ndash; -10.68]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-28.64 [-40.17 \u0026ndash; -17.12]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30.33 [-43.22 \u0026ndash; -17.44]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.26 [-0.37 \u0026ndash; -0.14]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.14 [-0.26 \u0026ndash; -0.02]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.20 [-0.32 \u0026ndash; -0.08]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.19 [-0.31 \u0026ndash; -0.07]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-16.13 [-28.03 \u0026ndash; -4.24]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-14.28 [-25.86 \u0026ndash; -2.70]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-20.11 [-31.99 \u0026ndash; -8.22]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-21.15 [-33.29 \u0026ndash; -9.008]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF-basic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.15 [-11.14\u0026ndash;6.84]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.90 [-17.13\u0026ndash;1.34]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.51 [-20.26 \u0026ndash; -2.77]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.92 [-20.73 \u0026ndash; -3.11]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e Indicates base-10 log transformed data. Significant p-values are bolded (α\u0026thinsp;=\u0026thinsp;0.01, adjusted using Tukey\u0026rsquo;s HSD). Presented biomarkers are those that exhibited a significant group by time interaction or a significant main effect of group. Estimate refers to mean group differences as estimated by the mixed model; 95% CI is the 95% confidence interval of the estimated group difference.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003emTBI, mild traumatic brain injury; GFAP, Glial fibrillary acidic protein; NFL, Neurofilament light.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[INSERT Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e HERE]\u003c/p\u003e \u003cp\u003eContrasts comparing biomarker levels over time in patients with mTBI (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed a large, significant decrease in GFAP across all subgroups between admission and 2 weeks. There was also a significant decrease in GFAP between 2 weeks and 3 months in MRI+, MRI- and CT- groups. The difference between admission and 12-month GFAP levels was large and significant in all subgroups. There was a significant increase in NFL concentrations from admission to 2 weeks in all subgroups, followed by a significant decrease in NFL concentrations from 2 weeks and 3 months and also from 3 months to 12 months in all subgroups. The difference between 2-week and 12-month NFL levels was large and significant in all subgroups. Though there was no significant increase in FGF-basic in the MRI- group between successive timepoints, a steadily growing difference at every timepoint between admission and 12 months is evident, culminating in a statistically significant increase in FGF-basic concentrations at 12 months compared to admission in the MRI- group. There are no differences in FGF-basic in the MRI\u0026thinsp;+\u0026thinsp;group, nor based on CT findings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in blood biomarker concentrations between timepoints in patients who were CT+/CT- and/or MRI+/MRI-.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdmission \u0026ndash; 2 weeks\u003c/p\u003e \u003cp\u003eEstimate [95% CI]\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 weeks \u0026ndash; 3 months\u003c/p\u003e \u003cp\u003eEstimate [95% CI]\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 months \u0026ndash; 12 months\u003c/p\u003e \u003cp\u003eEstimate [95% CI]\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdmission \u0026ndash; 12 months\u003c/p\u003e \u003cp\u003eEstimate [95% CI]\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 weeks \u0026ndash; 12 months\u003c/p\u003e \u003cp\u003eEstimate [95% CI]\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT Imaging:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGFAP\u003c/b\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.71 [-1.10 \u0026ndash; -0.31]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.20 [-0.38 \u0026ndash; -0.02]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.026\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.10 [-0.23\u0026ndash;0.03]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.206\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.003 [-1.38 \u0026ndash; -0.02]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT- Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.38 [-0.50 \u0026ndash; -0.27]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.09 [-0.13 \u0026ndash; -0.05]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01 [-0.05\u0026ndash;0.02]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.742\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.49 [-0.60 \u0026ndash; -0.37]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNFL\u003c/b\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75 [0.46\u0026ndash;1.04]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.46 [-0.68 \u0026ndash; -0.24]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.67 [-0.86 \u0026ndash; -0.48]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.13 [-1.46 \u0026ndash; -0.79]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT- Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31 [0.23\u0026ndash;0.39]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.23 [-0.29 \u0026ndash; -0.17]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.15 [-0.21 \u0026ndash; -0.10]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.38 [-0.48 \u0026ndash; -0.29]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMRI Imaging\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGFAP\u003c/b\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI\u0026thinsp;+\u0026thinsp;Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.85 [-1.21 \u0026ndash; -0.48]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.26 [-0.40 \u0026ndash; -0.12]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.10 [-0.20 \u0026ndash; -0.01]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.21 [-1.56 \u0026ndash; -0.12]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI- Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.31 [-0.40 \u0026ndash; -0.22]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.06 [-0.09 \u0026ndash; -0.03]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.006 [-0.04\u0026ndash;0.02]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.38 [-0.47 \u0026ndash; -0.29]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNFL\u003c/b\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI\u0026thinsp;+\u0026thinsp;Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89 [0.64\u0026ndash;1.15]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.50 [-0.78 \u0026ndash; -0.21]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.68 [-0.87 \u0026ndash; -0.50]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.18 [-1.42 \u0026ndash; -0.95]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI- Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25 [0.18\u0026ndash;0.32]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.20 [-0.28 \u0026ndash; -0.12]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.10 [-0.15 \u0026ndash; -0.06]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.30 [-0.38 \u0026ndash; -0.23]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFGF-basic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI\u0026thinsp;+\u0026thinsp;Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.88 [-9.25\u0026ndash;5.49]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78 [-5.49\u0026ndash;7.04]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71 [-2.49\u0026ndash;5.91]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61 [-5.24\u0026ndash;7.04]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI- Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.87 [-0.33\u0026ndash;8.07]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.39 [0.22\u0026ndash;8.57]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.12 [-2.63\u0026ndash;6.86]\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.38 [5.59\u0026ndash;15.17]\u003c/p\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e Indicates base-10 log transformed data. Significant p-values are bolded (α\u0026thinsp;=\u0026thinsp;0.01, adjusted using Tukey\u0026rsquo;s HSD). Presented biomarkers are those that exhibited a significant group by time interaction. Estimate refers to mean timepoint differences as estimated by the mixed model; 95% CI is the 95% confidence interval of the estimated timepoint difference.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003emTBI, mild traumatic brain injury; GFAP, Glial fibrillary acidic protein; NFL, Neurofilament light.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[INSERT Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e HERE]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBiomarkers Able to Discriminate CT+ \u0026amp; MRI+\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the unpenalized odds ratios of the biomarkers selected by elastic net as important predictors of CT\u0026thinsp;+\u0026thinsp;and MRI+. The algorithm determined a combination of GFAP, NFL, MIP-1β, IP-10 and eotaxin to be predictive of both CT\u0026thinsp;+\u0026thinsp;and MRI\u0026thinsp;+\u0026thinsp;at admission and 2 weeks, while IL-1ra was uniquely predictive of intracranial findings on CT at those timepoints. At 3 months, NFL, MIP-1β and IP-10 were selected as predictors for CT\u0026thinsp;+\u0026thinsp;and MRI+. At 12 months, MIP-1β was predictive of findings in both modalities, while IP-10 uniquely predicted CT+, and IL-9 uniquely predicted MRI+. GFAP and NFL were positively predictive of intracranial findings (i.e. elevated in CT+/MRI\u0026thinsp;+\u0026thinsp;groups) while for all inflammation markers, except IL-1ra, associations were negative (significantly lower concentrations in CT+/MRI\u0026thinsp;+\u0026thinsp;groups).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnpenalized odds ratios of the algorithmically-selected blood biomarkers predicting patients who were CT\u0026thinsp;+\u0026thinsp;and/or MRI+.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 weeks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 months\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 months\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOdds Ratio [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds Ratio [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds Ratio [95% CI]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCT Imaging\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFAP \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 [0.62\u0026ndash;2.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10 [0.41\u0026ndash;3.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFL \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.82 [0.91\u0026ndash;3.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81 [0.69\u0026ndash;4.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77 [1.02\u0026ndash;3.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIP-1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46 [0.21\u0026ndash;0.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45 [0.17\u0026ndash;1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56 [0.25\u0026ndash;1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42 [0.13\u0026ndash;1.20]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP-10 \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63 [0.29\u0026ndash;1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66 [0.23\u0026ndash;1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43 [0.14\u0026ndash;1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57 [0.19\u0026ndash;1.45]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEotaxin \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64 [0.26\u0026ndash;1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54 [0.14\u0026ndash;1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-1ra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.30 [1.07\u0026ndash;6.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04 [0.73\u0026ndash;12.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMRI Imaging\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFAP \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.59 [1.44\u0026ndash;4.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 [0.45\u0026ndash;2.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFL \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47 [0.82\u0026ndash;2.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.52 [2.14\u0026ndash;11.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.37 [1.99\u0026ndash;6.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIP-1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56 [0.29\u0026ndash;1.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78 [0.38\u0026ndash;1.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49 [0.25\u0026ndash;0.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36 [0.13\u0026ndash;0.88]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIP-10 \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57 [0.29\u0026ndash;1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45 [0.16\u0026ndash;1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55 [0.25\u0026ndash;1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEotaxin \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 [0.40\u0026ndash;1.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44 [0.14\u0026ndash;0.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62 [0.28\u0026ndash;1.36]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003e Indicates base-10 log transformed data. The blood biomarkers were algorithmically selected via elastic net. Each marker of inflammation or CNS injury was included in the model as a possible predictor, at their respective timepoints. The biomarkers selected for each model were fed into an ordinary logistic regression, from which the above odds ratios, with 95% CIs (confidence intervals) were calculated.\u003c/p\u003e \u003cp\u003e95% CI\u0026thinsp;=\u0026thinsp;95% confidence interval; GFAP\u0026thinsp;=\u0026thinsp;Glial fibrillary acidic protein; NFL\u0026thinsp;=\u0026thinsp;neurofilament light; MIP\u0026thinsp;=\u0026thinsp;Macrophage Inflammatory Protein; IP\u0026thinsp;=\u0026thinsp;IFNγ-induced Protein; IL\u0026thinsp;=\u0026thinsp;Interleukin\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the ROC curves of the selected combination of biomarkers at each timepoint for classifying CT+/MRI\u0026thinsp;+\u0026thinsp;and AUC values are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The multivariable predictions yielded AUCs above 0.80 at all timepoints for both modalities, with AUCs\u0026thinsp;\u0026gt;\u0026thinsp;.90 for discriminating CT\u0026thinsp;+\u0026thinsp;from CT- at 2 weeks, and MRI\u0026thinsp;+\u0026thinsp;from MRI- at admission, 2 weeks and 3 months. Supplementary Figs.\u0026nbsp;2 and 3 present the ROC curves discriminating CT+/MRI\u0026thinsp;+\u0026thinsp;vs. CT-/MRI- for individual candidate biomarkers at each timepoint and supplementary tables 4 and 5 provide the corresponding AUC values, sensitivities, and specificities. Some notable single biomarkers for classifying CT\u0026thinsp;+\u0026thinsp;were: admission GFAP (sensitivity\u0026thinsp;=\u0026thinsp;1.00, specificity\u0026thinsp;=\u0026thinsp;0.58, AUC\u0026thinsp;=\u0026thinsp;0.78); 2-week NFL (sensitivity\u0026thinsp;=\u0026thinsp;1.00, specificity\u0026thinsp;=\u0026thinsp;0.54, AUC\u0026thinsp;=\u0026thinsp;0.81); 2-week eotaxin (sensitivity\u0026thinsp;=\u0026thinsp;1.00, specificity\u0026thinsp;=\u0026thinsp;0.51, AUC\u0026thinsp;=\u0026thinsp;0.76); MIP-1β at all timepoints (AUC\u0026thinsp;=\u0026thinsp;0.79 at admission, 2 weeks and 3 months and AUC\u0026thinsp;=\u0026thinsp;0.81 at 12 months). Notable biomarkers for discriminating MRI\u0026thinsp;+\u0026thinsp;were: admission GFAP (sensitivity\u0026thinsp;=\u0026thinsp;0.92, specificity\u0026thinsp;=\u0026thinsp;0.63, AUC\u0026thinsp;=\u0026thinsp;0.82); NFL at 2 weeks (sensitivity\u0026thinsp;=\u0026thinsp;0.74, specificity\u0026thinsp;=\u0026thinsp;0.90, AUC\u0026thinsp;=\u0026thinsp;0.89) and 3 months (sensitivity\u0026thinsp;=\u0026thinsp;0.68, specificity\u0026thinsp;=\u0026thinsp;0.92, AUC\u0026thinsp;=\u0026thinsp;0.86); and MIP-1β at 12 months (AUC\u0026thinsp;=\u0026thinsp;0.81).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification accuracy of unpenalized models with algorithmically-selected biomarkers for discriminating patients who were CT\u0026thinsp;+\u0026thinsp;and/or MRI +.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUC [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncluded Biomarkers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT Imaging:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89 [0.82\u0026ndash;0.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGFAp, NFL, MIP-1β, IP-10, Eotaxin, IL-ra\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90 [0.83\u0026ndash;0.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGFAp, NFL, MIP-1β, IP-10, Eotaxin, IL-ra\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85 [0.74\u0026ndash;0.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNFL, MIP-1β, IP-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82 [0.64\u0026ndash;0.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMIP-1β, IP-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMRI Imaging\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90 [0.85\u0026ndash;0.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGFAp, NFL, MIP-1β, IP-10, Eotaxin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94 [0.88\u0026ndash;0.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGFAp, NFL, MIP-1β, IP-10, Eotaxin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91 [0.84\u0026ndash;0.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNFL, MIP-1β, IP-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82 [0.69\u0026ndash;0.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMIP-1β, IL-9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eArea under the curve (AUCs), sensitivities and specificities from unpenalized models with the selected combination of blood biomarkers \u0026ndash; selected via elastic net \u0026ndash; for discriminating patients who were CT\u0026thinsp;+\u0026thinsp;and/or MRI\u0026thinsp;+\u0026thinsp;are presented.\u003c/p\u003e \u003cp\u003eBiomarker coefficients set to 0 in all models: Tau, IFNγ, IL-8, MCP-1, IL-17A, TNF, FGF-basic, PDGF.\u003c/p\u003e \u003cp\u003emTBI, mild traumatic brain injury; ROC, Receiver Operating Characteristic; AUC, Area Under the Curve; GFAP\u0026thinsp;=\u0026thinsp;Glial fibrillary acidic protein; NFL\u0026thinsp;=\u0026thinsp;Neurofilament light; IFNγ\u0026thinsp;=\u0026thinsp;Interferon Gamma; IL\u0026thinsp;=\u0026thinsp;Interleukin; MIP\u0026thinsp;=\u0026thinsp;Macrophage Inflammatory Protein; MCP\u0026thinsp;=\u0026thinsp;Monocyte Chemoattractant Protein; IP\u0026thinsp;=\u0026thinsp;IFNγ-induced Protein; TNF\u0026thinsp;=\u0026thinsp;Tumor Necrotic Factor; FGF-basic\u0026thinsp;=\u0026thinsp;Basic Fibroblast Growth Factor; PDGF\u0026thinsp;=\u0026thinsp;Platelet-derived Growth Factor\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this longitudinal study based on a representative sample of mixed-mechanism mild TBI patients with follow-up over one year, we present novel findings regarding the diagnostic utility of several blood-based biomarkers for predicting and discriminating intracranial injuries determined by acute-phase CT and MRI. We observed a shift in the diagnostic utility of blood biomarkers discriminating those with and without complicated mTBI based on timepoint of sampling after injury, representing a novel finding. In the acute to subacute phase, higher levels of CNS-injury biomarkers provided superior discrimination while lower levels of some inflammatory makers across the entire first year best identified those with intracranial injury in the acute phase.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBiomarkers of CNS Injury Demonstrating High Diagnostic Utility for Intracranial Findings at Acute, Subacute and Chronic Phases\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe CNS-injury markers NFL and GFAP were highly discriminative biomarkers for both CT\u0026thinsp;+\u0026thinsp;and MRI+. We present novel findings regarding the ability of NFL measured at 2 weeks and 3 months to classify patients with mTBI who were CT\u0026thinsp;+\u0026thinsp;or MRI+, given previous studies\u003csup\u003e18\u0026ndash;20\u003c/sup\u003e did not study NFL sampled during these subacute and chronic phases. Single-biomarker classification accuracy of CT+/MRI\u0026thinsp;+\u0026thinsp;is highest using NFL sampled at 2 weeks, while 3-month NFL also demonstrates high discriminability. Based on our results, we recommend NFL as a diagnostic biomarker for intracranial injury at 2 weeks especially, and potentially for up to 3 months after injury. GFAP\u0026rsquo;s diagnostic utility at admission is in line with previous research and current clinical recommendations.\u003csup\u003e9\u003c/sup\u003e GFAP in our sample demonstrated perfect sensitivity for discriminating CT\u0026thinsp;+\u0026thinsp;patients at admission and high sensitivity for discriminating MRI\u0026thinsp;+\u0026thinsp;patients, adding to the already solid evidence for its early diagnostic utility across imaging modalities.\u003csup\u003e9,19,20,43,44\u003c/sup\u003e Additionally, we demonstrated good diagnostic accuracy of GFAP sampled at 2 weeks, constituting a new finding that could guide futureclinical recommendations.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInflammation Markers Associated with Intracranial Findings on Both CT \u0026amp; MRI\u003c/h2\u003e \u003cp\u003eWe demonstrated primarily negative associations of inflammation markers with intracranial findings at all timepoints, on both imaging modalities, i.e. inflammation markers were significantly reduced in CT+/MRI\u0026thinsp;+\u0026thinsp;groups compared to CT-/MRI-. This indicates that low levels or lack of an inflammatory response may be important diagnostically, which is counter to expectations.\u003c/p\u003e \u003cp\u003eSpecifically, the inflammation markers MIP-1β and IP-10 demonstrated significant reductions in both patients who were CT\u0026thinsp;+\u0026thinsp;and MRI\u0026thinsp;+\u0026thinsp;at almost all timepoints. Both markers are chemokines best known for their proinflammatory and chemotactic effects.\u003csup\u003e45,46\u003c/sup\u003e MIP-1β is a key player in many inflammatory conditions, but also appears to be critical for wound healing and has the ability to promote homeostasis,\u003csup\u003e45\u003c/sup\u003e while IP-10 plays an important role in CNS inflammation in a number of diseases, such as multiple sclerosis and Alzheimer\u0026rsquo;s disease.\u003csup\u003e46,47\u003c/sup\u003e Studies have previously evidenced MIP-1β and IP-10 upregulation post-injury in both animal models of TBI\u003csup\u003e48\u0026ndash;50\u003c/sup\u003e and human TBI studies.\u003csup\u003e51\u0026ndash;53\u003c/sup\u003e We have also confirmed that both biomarkers are elevated compared to controls in a previous analysis on this sample.\u003csup\u003e33\u003c/sup\u003e However, despite studies reporting associations between poorer TBI outcome and both chemokines,\u003csup\u003e54,55\u003c/sup\u003e a recent study comparing the chronic phase of mTBI in rats and humans reported a positive correlation between MIP-1β and IP-10 concentrations and fractional anisotropy in several brain regions,\u003csup\u003e51\u003c/sup\u003e interpreted as better white matter integrity as a function of higher concentrations of both chemokines. A similar relationship \u0026ndash; higher levels of IP-10 and MIP-1β in those without intracranial findings \u0026ndash; is reported here and speaks to the complex nature of inflammatory processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInflammation Markers Uniquely Associated with Intracranial Findings on MRI\u003c/h2\u003e \u003cp\u003eThe growth factor FGF-basic, IL-9 and eotaxin were uniquely associated with MRI findings, perhaps due to biological mechanisms associated with TAI (TAI is the primary difference between MRI and CT groups, see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). IL-9 and eotaxin were significantly reduced in patients who were MRI+ (but not CT+) at almost all timepoints. Eotaxin is a chemokine that has long been associated with cognitive decline during aging in both humans and rodent models.\u003csup\u003e56\u003c/sup\u003e Its elevations have recently been detected in a number of neurodegenerative and psychiatric disorders, \u003csup\u003e56\u003c/sup\u003e and it is particularly associated with memory deficits in Alzheimer\u0026rsquo;s disease.\u003csup\u003e57\u003c/sup\u003e In both animal models and human studies of more severe TBI, eotaxin has exhibited elevation in response to injury.\u003csup\u003e54,55,58\u003c/sup\u003e It is therefore unclear why no TAI would result in greater concentrations of eotaxin. We have previously shown that patients with mTBI without other injuries (e.g. skin contusions, abrasions, bone fractures etc.) also have lower eotaxin levels.\u003csup\u003e33\u003c/sup\u003e Taken together, eotaxin appears to be most elevated in those with the \u0026ldquo;mildest\u0026rdquo; form of mTBI. These contradictory effects warrant replication and further investigation into their underlying mechanisms in both human and animal studies.\u003c/p\u003e \u003cp\u003eIL-9 is a pleiotropic cytokine primarily activated by Th9 cells.\u003csup\u003e59\u003c/sup\u003e Its major functions remain relatively underinvestigated, though it has been associated with a number of inflammatory diseases, specifically with regard to promoting immunotolerance.\u003csup\u003e59,60\u003c/sup\u003e Though some rodent studies have evidenced IL-9 elevation following mTBI,\u003csup\u003e61,62\u003c/sup\u003e its association with diagnostic and prognostic factors in mTBI is not yet investigated. Given IL-9 was algorithmically selected for the 12-month model predicting MRI findings, but not the corresponding CT model, it appears to demonstrate diagnostic specificity for MRI\u0026thinsp;+\u0026thinsp;above and beyond other biomarkers in the late/chronic phase of mTBI. The results presented here, coupled with our previous findings of IL-9 elevations in patients with PPCS,\u003csup\u003e32\u003c/sup\u003e warrant further investigation into the neurobiological mechanisms of both high and low IL-9 levels in mTBI.\u003c/p\u003e \u003cp\u003eIn our sample, FGF-basic showed steady increases in the MRI- group, culminating in statistically significantly greater concentrations in those who were MRI- compared to MRI\u0026thinsp;+\u0026thinsp;at 3 and 12 months. FGF-basic is a growth factor believed to broadly promote angio- and neurogenesis, to reduce pathogenic disruption of the blood-brain barrier and to increase neuronal survival.\u003csup\u003e63\u0026ndash;65\u003c/sup\u003e Following experimental TBI, it has been shown in human cell cultures to reduce apoptosis of human brain endothelial cells\u003csup\u003e63\u003c/sup\u003e and to upregulate neuronal survival in the adult hippocampus of a TBI mouse model,\u003csup\u003e64\u003c/sup\u003e along with alleviating neurological deficits. Based on these findings, FGFs were recently proposed as a therapeutic treatment for stroke, which could have relevance also for patients with TBI.\u003csup\u003e65\u003c/sup\u003e Given FGFs\u0026rsquo; evidenced neuroprotective effects, our results could indicate that MRI- patients (presumably, those without TAI) begin to naturally produce this beneficial growth factor given time, while the more severely injured MRI\u0026thinsp;+\u0026thinsp;patients are unable to do so within the first year following injury. Patients with mTBI who are MRI\u0026thinsp;+\u0026thinsp;may therefore represent a clinical target who would especially benefit from FGF therapies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMulti-Biomarker Panels Improve Discriminability Over Single Biomarkers at All Timepoints\u003c/h2\u003e \u003cp\u003eSingle-biomarker discriminability for intracranial findings (CT+/MRI+) was poor to moderate for all inflammation markers and very good for GFAP sampled at admission and NFL sampled at 2 weeks and 3 months. Using a multi-biomarker panel with biomarkers selected for the model via elastic net regression improved discriminability at all timepoints, with excellent discriminability for CT\u0026thinsp;+\u0026thinsp;at admission and 2 weeks (\u0026asymp;\u0026thinsp;0.90) and for MRI\u0026thinsp;+\u0026thinsp;at admission, 2 weeks and 3 months (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.90). Though IL-ra did not exhibit significant group differences in the LMMs, it was included in the model predicting CT\u0026thinsp;+\u0026thinsp;status at admission and 2 weeks and was the only inflammation marker to show a positive association with intracranial findings. IL-ra is an endogenous receptor antagonist of IL-1r\u003csup\u003e66\u003c/sup\u003e that is under investigation as a therapeutic target for TBI.\u003csup\u003e67\u003c/sup\u003e Its elevation in CT\u0026thinsp;+\u0026thinsp;could therefore reflect endogenous repair mechanisms, although given it was only included in some CT models (but not MRI models), its clinical/biological relevance remains unclear.\u003c/p\u003e \u003cp\u003ePut into context, our results show that a biomarker panel can identify with high accuracy patients with intracranial findings on CT/MRI, although similar discriminability can be achieved using only admission GFAP or 2-week NFL. Moreover, a greater number of biomarkers are discriminable of intracranial findings at early timepoints, though good discriminability is also achieved with a small selection of biomarkers at later timepoints. A validated panel of biomarkers for diagnosing intracranial injury late could help guide treatment plans in cases where CT/MRI were not performed acutely following injury. Taken together with our previous published works on this cohort (see \u003csup\u003e16,32,33\u003c/sup\u003e), we conclude that the biomarkers of CNS injury GFAP and NFL show high diagnostic utility for both intracranial findings on CT/MRI and for discriminating patients with mTBI from controls. Inflammation markers on the other hand show greater prognostic relevance for PPCS and remain elevated in patients with mTBI compared to controls for at least one year after injury. They showed diagnostic relevance also for intracranial findings on CT/MRI, but at levels lower than their CNS-injury counterparts, and in a biologically/clinically unexpected direction that warrants further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur study acknowledges several limitations. Firstly, the sample is comprised of those who were willing to participate in comprehensive data collection, meaning it may not be generalizable to all patients with mTBI. Secondly, the small number of CT+/MRI\u0026thinsp;+\u0026thinsp;cases compared to non-cases increases the likelihood of statistical overfitting, as is reflected in relatively large confidence intervals. As such, we do not present our models with the goal of generating accurate predictive models, but rather in the hopes that our findings can inform the selection of biomarkers for analysis in future, large-scale studies (such as CENTER-TBI and TRACK-TBI). However, we consider the small number of intracranial findings a consequence of recruiting a more representative sample from both the ED and ambulatory clinics. Furthermore, our upper-age limit of 60 years was designed to reduce the burden of age-related findings on MRI scans, however this means that known age-related effects of TBI were not investigated in this study. Regarding biomarkers, we sampled total tau, although previous literature suggests phosphorylated tau, or the ratio of phosphorylated tau:total tau may be more relevant for TBI diagnostics,\u003csup\u003e68,69\u003c/sup\u003e and a new method for isolating brain-derived tau\u003csup\u003e70\u003c/sup\u003e may prove diagnostically superior in future studies. Similarly, IL-6 and IL-10 are two of the most studied inflammatory biomarkers in mTBI,\u003csup\u003e71\u003c/sup\u003e however they were not expressed in sufficient quantities in our multiplex assay, de facto implying no effects of mTBI on their levels. Studies have also identified other potential diagnostic inflammation markers\u003csup\u003e72\u003c/sup\u003e that we unfortunately did not assess here. Lastly, due to technical constraints, our admission timepoint includes all blood drawn within 72 hours from injury, although it is known that many biomarkers show greater discriminability within 24 hours.\u003csup\u003e16,43,71\u003c/sup\u003e All-in-all, the limitations of our paper highlight the need for rigorous meta-analyses and pooling of data across labs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides novel evidence regarding the diagnostic utility of a large array of CNS-injury and inflammation biomarkers for triaging mTBI patients likely to have complicated mTBI in the acute phase as well as in the chronic phase (up to 12 months). We demonstrated NFL\u0026rsquo;s significant diagnostic utility at subacute (2 weeks) and chronic (3 months) timepoints and confirmed GFAP\u0026rsquo;s acute diagnostic utility, along with evidencing its potential subacute (2-week) diagnostic utility. We also shed light on interesting mechanisms of peripheral inflammation in complicated mTBI, whereby there appears to be lower inflammation in patients with intracranial findings than those without from the acute phase throughout the first year of injury. This highlights important differences between inflammatory profiles in mild versus moderate-severe TBI cohorts. Lastly, our study provides promising evidence regarding the diagnostic utility of a panel of biomarkers, rather than single blood biomarkers, encouraging further research with the goal of fine-tuning and validating predictive diagnostic models based on biomarkers in mTBI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval and Consent to Participate\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the Regional Committee for Medical Research Ethics\u0026nbsp;(2013/754), and participants provided written informed consent.\u003c/p\u003e\n\u003ch2\u003eConsent for Publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e\n\u003cp\u003eData, including biofluids, from the Trondheim mTBI study used in this manuscript can be accessed by contacting the last author ([email protected]) or Professor Toril Skandsen ([email protected]) by e-mail. Note that data will only be shared with qualified investigators in connection with planned investigations which have undergone scientific and ethical review and are in compliance with the European Union General Data Protection Regulations (GDPR), Norwegian laws and regulations, and NTNU regulations. The completion of a material transfer agreement (MTA) signed by an institutional official will be required. Analytic code used to conduct the analyses presented in this study are not available in a public repository; they may be available by emailing the first author ([email protected]).\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eHenrik Zetterberg is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2022-01018 and #2019-02397), the European Union\u0026rsquo;s Horizon Europe research and innovation programme under grant agreement No 101053962, and Swedish State Support for Clinical Research (#ALFGBG-71320). He has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). Kaj Blennow holds the Torsten S\u0026ouml;derberg Professorship in Medicine at the Royal Swedish Academy of Sciences, and was supported by the Swedish Research Council (#2017-00915), the Swedish Alzheimer Foundation (#AF651 742881), Hj\u0026auml;rnfonden, Sweden (#FO2017-0243), and a grant (#ALFGBG-715986) from the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement. He has served as a consultant or at advisory boards for Alzheon, CogRx, Biogen, Lilly, Novartis and Roche Diagnostics, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB, a GU Venture-based platform company at the University of Gothenburg. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential competing interests\u003c/p\u003e\n\u003ch2\u003eFunding Statement\u003c/h2\u003e\n\u003cp\u003eThe Trondheim mild TBI study was funded by the Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology (NTNU). Furthermore, the MRI image study was partly funded by the National Norwegian Advisory Unit for functional MRI and Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital. Additional, general financial support was obtained from, The Odd Fellow Foundation, and The Simon Fougner Hartmann Family Fund. First author Gerard Clarke is supported by the Centre for Innovative Ultrasound Solutions (CIUS) funded by the Research Council of Norway, grant number: 237887.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eGerard Clarke organized blood data results, performed all statistical analyses, and drafted the manuscript for intellectual content. Toril Skandsen (PI of Trondheim mild TBI study), Anne Vik, and Asta H\u0026aring;berg designed the study, oversaw all data collection, contributed to analysis, planned, and revised the manuscript. Henrik Zetterberg and Kaj Blennow selected and oversaw CNS injury biomarker analyses, quality assessed data, and revised the manuscript. Turid Follestad is the statistician who approved all statistical methods and presentation of results in writing and figures/tables. Anne Vik provided neurosurgical expertise and revised the manuscript. Asta H\u0026aring;berg is the principal supervisor of this manuscript. All authors carefully revised the manuscript and approved the submitted version.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank the staff at the Trondheim Municipal Emergency Department, the Department of Neurosurgery and the Department of Anaesthesiology, and Intensive Care Medicine for their cooperation during patient recruitment. Thanks to Stine Bj\u0026oslash;ralt for study coordination and to Jonas Stenberg, Simen Berg Saksvik and Migle Karaliute for recruitment of trauma controls and help with the blood samples. Thank you to Cathrine Einarsen for recruitment of participants, collection and organization of demographic, clinical and blood sample data. Thank you to S\u0026oslash;ren Erik Pischke and Tom Eirik Mollnes who oversaw inflammation biomarker analyses and quality assessment of that data.\u0026nbsp;Thank you to Biobank 1 for the storage of our blood samples and thank you to the laboratory technicians of the Clinical Neurochemistry Laboratory at the Sahlgrenska University Hospital. We thank Marit Kristina Indergaard and Ina M\u0026oslash;ller for assistance with the blood testing procedures for inflammation biomarkers.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCassidy JD, Carroll LJ, Peloso PM, et al. Incidence, risk factors and prevention of mild traumatic brain injury: results of the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. 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PLoS ONE. 2017;12(3):e0173798. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0173798\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0173798\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"concussion, mixed-mechanism mild TBI, prediction, predictive modelling, cytokines, growth factors, neuroimaging","lastPublishedDoi":"10.21203/rs.3.rs-3959215/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3959215/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIdentifying individuals with intracranial injuries following mild traumatic brain injury (mTBI), i.e. complicated mTBI cases, is important for follow-up and prognostication. The aim of the current study was to identify the ability of single and multi-panel blood biomarkers of CNS injury and inflammation, from the acute to chronic phase after injury, to classify people with complicated mTBI on computer tomography (CT) and/or magnetic resonance imaging (MRI) acquired within 72 hours.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePatients with mTBI (n\u0026thinsp;=\u0026thinsp;207, 16\u0026ndash;60 years), i.e., Glasgow Coma Scale (GCS) score between 13 and 15, loss of consciousness (LOC)\u0026thinsp;\u0026lt;\u0026thinsp;30 min and post-traumatic amnesia (PTA)\u0026thinsp;\u0026lt;\u0026thinsp;24 hours, were included. Complicated mTBI was present in 8% (n\u0026thinsp;=\u0026thinsp;16) based on CT (CT+) and 12% (n\u0026thinsp;=\u0026thinsp;25) based on MRI (MRI+). Blood biomarkers were sampled at four timepoints following injury: admission (within 72 hours), 2 weeks (\u0026plusmn;\u0026thinsp;3 days), 3 months (\u0026plusmn;\u0026thinsp;2 weeks) and 12 months (\u0026plusmn;\u0026thinsp;1 month). CNS biomarkers included were GFAP, NFL and tau, along with a panel of 12 inflammation markers. Predictive models were generated with both single and multi-panel biomarkers and assessed using area under the curve analyses (AUCs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe most discriminative single biomarkers were GFAP at admission (CT+: AUC\u0026thinsp;=\u0026thinsp;0.78; MRI+: AUC\u0026thinsp;=\u0026thinsp;0.82) and NFL at 2 weeks (CT+: AUC\u0026thinsp;=\u0026thinsp;0.81; MRI+: AUC\u0026thinsp;=\u0026thinsp;0.89) and 3 months (MRI+: AUC\u0026thinsp;=\u0026thinsp;0.86). MIP-1β and IP-10 concentrations were significantly lower at almost all timepoints in patients who were CT\u0026thinsp;+\u0026thinsp;and MRI+. Eotaxin and IL-9 were significantly lower in patients who were MRI\u0026thinsp;+\u0026thinsp;only. FGF-basic concentrations increased over time in patients who were MRI- and were significantly higher than patients MRI\u0026thinsp;+\u0026thinsp;at 3- and 12 months. Multi-biomarker panels improved discriminability at all timepoints (AUCs\u0026thinsp;\u0026asymp;\u0026thinsp;0.90 of admission and 2-week models for CT\u0026thinsp;+\u0026thinsp;and AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.90 of admission, 2-week and 3-month models for MRI+).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe CNS biomarkers GFAP and NFL were useful diagnostic biomarkers of complicated mTBI in acute, subacute and chronic phases after mTBI. Several inflammation markers were significantly lower in patients with complicated mTBI, at all timepoints, and could discriminate between CT\u0026thinsp;+\u0026thinsp;and MRI\u0026thinsp;+\u0026thinsp;even after 12 months. Multi-biomarker panels improved diagnostic accuracy at all timepoints.\u003c/p\u003e","manuscriptTitle":"Blood-Based CNS-Injury and Inflammation Biomarkers Sampled at Acute, Subacute, and Chronic phases After Mild TBI Demonstrate Diagnostic Utility For Patients With and Without Intracranial Injuries on Acute CT and MRI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 18:14:25","doi":"10.21203/rs.3.rs-3959215/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-03-01T17:05:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8ad30055-6065-43d5-a02e-973c950da5d5","date":"2024-02-26T13:35:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-26T11:25:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-19T13:54:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-19T12:19:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuroinflammation","date":"2024-02-15T16:36:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96d23bcc-eb54-4f13-a22a-890afeefca84","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-04-05T19:59:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-21 18:14:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3959215","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3959215","identity":"rs-3959215","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}