scholarly journals Imputation strategies for missing baseline neurological assessment covariates after traumatic brain injury: A CENTER-TBI study

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0253425
Author(s):  
Ari Ercole ◽  
Abhishek Dixit ◽  
David W. Nelson ◽  
Shubhayu Bhattacharyay ◽  
Frederick A. Zeiler ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Multiple Imputation ◽  
Model Performance ◽  
Predictive Performance ◽  
Baseline Risk ◽  
Multiple Time ◽  
Variable Degree ◽  
Time Points ◽  
Simple Substitution

Statistical models for outcome prediction are central to traumatic brain injury research and critical to baseline risk adjustment. Glasgow coma score (GCS) and pupil reactivity are crucial covariates in all such models but may be measured at multiple time points between the time of injury and hospital and are subject to a variable degree of unreliability and/or missingness. Imputation of missing data may be undertaken using full multiple imputation or by simple substitution of measurements from other time points. However, it is unknown which strategy is best or which time points are more predictive. We evaluated the pseudo-R2 of logistic regression models (dichotomous survival) and proportional odds models (Glasgow Outcome Score—extended) using different imputation strategies on the The Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study dataset. Substitution strategies were easy to implement, achieved low levels of missingness (<< 10%) and could outperform multiple imputation without the need for computationally costly calculations and pooling multiple final models. While model performance was sensitive to imputation strategy, this effect was small in absolute terms and clinical relevance. A strategy of using the emergency department discharge assessments and working back in time when these were missing generally performed well. Full multiple imputation had the advantage of preserving time-dependence in the models: the pre-hospital assessments were found to be relatively unreliable predictors of survival or outcome. The predictive performance of later assessments was model-dependent. In conclusion, simple substitution strategies for imputing baseline GCS and pupil response can perform well and may be a simple alternative to full multiple imputation in many cases.

scholarly journals Imputation strategies for missing baseline neurological assessment covariates after traumatic brain injury: A CENTER-TBI study

2021 ◽  
Author(s):  
Ari Ercole ◽  
Abhishek Dixit ◽  
David W Nelson ◽  
Frederick A Zeiler ◽  
Daan Nieboer ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Multiple Imputation ◽  
Model Performance ◽  
Predictive Performance ◽  
Baseline Risk ◽  
Multiple Time ◽  
Variable Degree ◽  
Time Points ◽  
Simple Substitution

Statistical models for outcome prediction are central to traumatic brain injury research and critical to baseline risk adjustment. Glasgow coma score (GCS) and pupil reactivity are crucial co- variates in all such models but may be measured at multiple time points between the time of injury and hospital and are subject to a variable degree of unreliability and/or missingness. Imputation of missing data may be undertaken using full multiple imputation or by simple substitution of measurements from other time points. However it is unknown which strategy is best or which time points are more predictive. We evaluated the pseudo-R2 of logistic regression models (dichotomous survival) and proportional odds models (Glasgow Outcome Score- extended) using different imputation strategies from data from the The Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Substitution strategies were easy to implement, achieved low levels of missingness (<< 10%) and could outperform multiple imputation without the need for computationally costly calculations and pooling multiple final models. Model performance was sensitive to imputation strategy although this was small in absolute terms and clinical relevance. A strategy of using the emergency department discharge assessments and working back in time when these were missing generally performed well. Full multiple imputation had the advantage of preserving time-dependence in the models: The pre-hospital assessments were found to be relatively unreliable predictors of survival or outcome. The predictive performance of later assessments was model-dependent. In conclusion, simple substitution strategies for imputing baseline GCS and pupil response can perform well and may be a simple alternative to full multiple imputation in many cases.

scholarly journals Glutamate and GABA concentrations following mild traumatic brain injury: a pilot study

2018 ◽  
Vol 120 (3) ◽  
pp. 1318-1322 ◽  
Author(s):  
Alia L. Yasen ◽  
Jolinda Smith ◽  
Anita D. Christie
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Primary Motor Cortex ◽  
Brain Regions ◽  
Resonance Spectroscopy ◽  
Multiple Time ◽  
Time Points ◽  
The Brain

Animal models of mild traumatic brain injury (mTBI) suggest that metabolic changes in the brain occur immediately after a mechanical injury to the head. Proton magnetic resonance spectroscopy (1H-MRS) can be used to determine relative concentrations of metabolites in vivo in the human brain. The purpose of this study was to determine concentrations of glutamate and GABA in the brain acutely after mTBI and throughout 2 mo of recovery. Concentrations of glutamate and GABA were obtained using 1H-MRS in nine individuals who had suffered an mTBI and nine control individuals in two brain regions of interest: the primary motor cortex (M1), and the dorsolateral prefrontal cortex (DLPFC), and at three different time points postinjury: 72 h, 2 wk, and 2 mo postinjury. There were no differences between groups in concentrations of glutamate or GABA, or the ratio of glutamate to GABA, in M1. In the DLPFC, glutamate concentration was lower in the mTBI group compared with controls at 72 h postinjury (d = 1.02), and GABA concentration was lower in the mTBI group at 72 h and 2 wk postinjury (d = 0.81 and d = 1.21, respectively). The ratio of glutamate to GABA in the DLPFC was higher in the mTBI group at 2 wk postinjury (d = 1.63). These results suggest that changes in glutamate and GABA concentrations in the brain may be region-specific and may depend on the amount of time that has elapsed postinjury. NEW & NOTEWORTHY To our knowledge, this is the first study to examine neurotransmitter concentrations in vivo at multiple time points throughout recovery from mild traumatic brain injury in humans.

scholarly journals Chronic complement dysregulation drives neuroinflammation after traumatic brain injury: a transcriptomic study

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Amer Toutonji ◽  
Mamatha Mandava ◽  
Silvia Guglietta ◽  
Stephen Tomlinson
Keyword(s):  
Gene Expression ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Complement System ◽  
Classical Pathway ◽  
Post Injury ◽  
Complement Inhibition ◽  
Time Points ◽  
Complement Gene ◽  
The Complement System

AbstractActivation of the complement system propagates neuroinflammation and brain damage early and chronically after traumatic brain injury (TBI). The complement system is complex and comprises more than 50 components, many of which remain to be characterized in the normal and injured brain. Moreover, complement therapeutic studies have focused on a limited number of histopathological outcomes, which while informative, do not assess the effect of complement inhibition on neuroprotection and inflammation in a comprehensive manner. Using high throughput gene expression technology (NanoString), we simultaneously analyzed complement gene expression profiles with other neuroinflammatory pathway genes at different time points after TBI. We additionally assessed the effects of complement inhibition on neuropathological processes. Analyses of neuroinflammatory genes were performed at days 3, 7, and 28 post injury in male C57BL/6 mice following a controlled cortical impact injury. We also characterized the expression of 59 complement genes at similar time points, and also at 1- and 2-years post injury. Overall, TBI upregulated the expression of markers of astrogliosis, immune cell activation, and cellular stress, and downregulated the expression of neuronal and synaptic markers from day 3 through 28 post injury. Moreover, TBI upregulated gene expression across most complement activation and effector pathways, with an early emphasis on classical pathway genes and with continued upregulation of C2, C3 and C4 expression 2 years post injury. Treatment using the targeted complement inhibitor, CR2-Crry, significantly ameliorated TBI-induced transcriptomic changes at all time points. Nevertheless, some immune and synaptic genes remained dysregulated with CR2-Crry treatment, suggesting adjuvant anti-inflammatory and neurotropic therapy may confer additional neuroprotection. In addition to characterizing complement gene expression in the normal and aging brain, our results demonstrate broad and chronic dysregulation of the complement system after TBI, and strengthen the view that the complement system is an attractive target for TBI therapy.

scholarly journals Development of prognostic models for Health-Related Quality of Life following traumatic brain injury

2021 ◽  
Author(s):  
Isabel R. A. Retel Helmrich ◽  
David van Klaveren ◽  
Simone A. Dijkland ◽  
Hester F. Lingsma ◽  
Suzanne Polinder ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Model Performance ◽  
Prognostic Models ◽  
Health Related Quality ◽  
Mild Tbi ◽  
Related Quality ◽  
Health Related

Abstract Background Traumatic brain injury (TBI) is a leading cause of impairments affecting Health-Related Quality of Life (HRQoL). We aimed to identify predictors of and develop prognostic models for HRQoL following TBI. Methods We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Core study, including patients with a clinical diagnosis of TBI and an indication for computed tomography presenting within 24 h of injury. The primary outcome measures were the SF-36v2 physical (PCS) and mental (MCS) health component summary scores and the Quality of Life after Traumatic Brain Injury (QOLIBRI) total score 6 months post injury. We considered 16 patient and injury characteristics in linear regression analyses. Model performance was expressed as proportion of variance explained (R2) and corrected for optimism with bootstrap procedures. Results 2666 Adult patients completed the HRQoL questionnaires. Most were mild TBI patients (74%). The strongest predictors for PCS were Glasgow Coma Scale, major extracranial injury, and pre-injury health status, while MCS and QOLIBRI were mainly related to pre-injury mental health problems, level of education, and type of employment. R2 of the full models was 19% for PCS, 9% for MCS, and 13% for the QOLIBRI. In a subset of patients following predominantly mild TBI (N = 436), including 2 week HRQoL assessment improved model performance substantially (R2 PCS 15% to 37%, MCS 12% to 36%, and QOLIBRI 10% to 48%). Conclusion Medical and injury-related characteristics are of greatest importance for the prediction of PCS, whereas patient-related characteristics are more important for the prediction of MCS and the QOLIBRI following TBI.

Cardiovascular risk model performance in women with and without hypertensive disorders of pregnancy

Heart ◽  
2018 ◽  
Vol 105 (4) ◽  
pp. 330-336 ◽  
Author(s):  
Veerle Dam ◽  
N Charlotte Onland-Moret ◽  
W M Monique Verschuren ◽  
Jolanda M A Boer ◽  
Laura Benschop ◽  
...  
Keyword(s):  
Risk Model ◽  
Model Performance ◽  
Predictive Performance ◽  
Baseline Risk ◽  
Hypertensive Disorders Of Pregnancy ◽  
Cvd Risk ◽  
Hypertensive Disorders ◽  
Framingham Risk ◽  
History Of ◽  
Score Model

ObjectivesCompare the predictive performance of Framingham Risk Score (FRS), Pooled Cohort Equations (PCEs) and Systematic COronary Risk Evaluation (SCORE) model between women with and without a history of hypertensive disorders of pregnancy (hHDP) and determine the effects of recalibration and refitting on predictive performance.MethodsWe included 29 751 women, 6302 with hHDP and 17 369 without. We assessed whether models accurately predicted observed 10-year cardiovascular disease (CVD) risk (calibration) and whether they accurately distinguished between women developing CVD during follow-up and not (discrimination), separately for women with and without hHDP. We also recalibrated (updating intercept and slope) and refitted (recalculating coefficients) the models.ResultsOriginal FRS and PCEs overpredicted 10-year CVD risks, with expected:observed (E:O) ratios ranging from 1.51 (for FRS in women with hHDP) to 2.29 (for PCEs in women without hHDP), while E:O ratios were close to 1 for SCORE. Overprediction attenuated slightly after recalibration for FRS and PCEs in both hHDP groups. Discrimination was reasonable for all models, with C-statistics ranging from 0.70-0.81 (women with hHDP) and 0.72–0.74 (women without hHDP). C-statistics improved slightly after refitting 0.71–0.83 (with hHDP) and 0.73–0.80 (without hHDP). The E:O ratio of the original PCE model was statistically significantly better in women with hHDP compared with women without hHDP.ConclusionsSCORE performed best in terms of both calibration and discrimination, while FRS and PCEs overpredicted risk in women with and without hHDP, but improved after recalibrating and refitting the models. No separate model for women with hHDP seems necessary, despite their higher baseline risk.

scholarly journals Highly Sensitive Single Molecular Array Immunoassay Measurement of t-Tau, NF-L, GFAP, and UCH-L1 Biomarkers in Acute Concussion/Mild Traumatic Brain Injury (mTBI) Patient Serum and Saliva Samples

Neurology ◽  
2019 ◽  
Vol 93 (14 Supplement 1) ◽  
pp. S19.3-S20
Author(s):  
Ahmed Chenna ◽  
Christos Petropoulos ◽  
John Winslow
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Limited Information ◽  
Post Injury ◽  
Time Points ◽  
Highly Sensitive ◽  
Biomarker Analysis ◽  
T Tau ◽  
Control Samples

ObjectiveTo determine if t-Tau, NF-L, GFAP and UCH-L1 protein biomarkers are elevated in early time points of acute concussion/mild traumatic brain injury patient serum and saliva, relative to control samples.Backgroundt-Tau, NF-L, GFAP and UCH-L1 levels have been reported to increase in cerebral spinal fluid (CSF) and blood following head trauma within 24 hours or longer, and are candidate diagnostic and prognostic biomarkers of concussion and mild to moderate TBI. However, limited information exists on the relationship between these biomarkers at short time points post-injury, and detectability in saliva of mTBI patients.Design/MethodsBiomarker analysis of serum from a total of 120 participants, derived from two independent sample groups consisting of 60 concussion/mTBI patients each, with blood collected within 1-4 hr and 8-16 hr post-injury, respectively, was compared with 30 healthy control sera. Saliva samples were collected after 8-16 hr post-injury from a n = 30 subset of the same patients. Quanterix Simoa 4-plex immunoassay was used for highly sensitive measurements of these biomarkers.ResultsMedian levels of NF-L, GFAP and UCH-L1 were significantly higher in independent sets of patient serum samples (n = 60 each), both at early (1–4 hr) and later (8–16 hr) time points post-mTBI/concussion, relative to control samples (n = 30) (p < 0.0001, = 0.0001, <0.0001, respectively). Low levels of t-Tau are detected, but are significantly elevated post-concussion relative to controls (p = 0.0001). Significant correlations were observed between levels of t-Tau and UCH-L1, NF-L and GFAP, and t-Tau and GFAP in both post-injury time-point groups, and between NF-L and UCH-L1 levels in the 8-16 hr group. The four biomarkers were detected in saliva from concussion/mTBI patients (n = 30).ConclusionsThis study supports the utility of ultra-sensitive multiplex immunoassays to detect increases in CNS proteins at high sensitivity in serum and saliva within 1-4 and 8-16 hr of concussion/mTBI.

Prognosticating Traumatic Brain Injury (TBI): Analysis of CRASH, IMPACT Models and Patient Age in a Cohort of TBI Patients

Neurosurgery ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. E271-E272 ◽  
Author(s):  
Conor Gillespie ◽  
Catherine McMahon
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Survival Rates ◽  
Predictive Performance ◽  
Good Survival ◽  
Post Injury ◽  
Patient Age ◽  
Patient Âgé ◽  
Significant Difference ◽  
Impact Models

Abstract INTRODUCTION Both CRASH and IMPACT models have been developed in recent years to predict the outcome of Traumatic Brain Injury (TBI). However, there is no clear evidence as to how these models perform in a modern cohort of UK-patients. There is also predictive uncertainty with regards to survival rates and functional outcome in elderly (>65 yr) patients. METHODS Patients referred to a tertiary neuroscience center from December 2014 to January 2016 with a suspected TBI were retrospectively examined. For each model, the predicted survival and overall outcome were compared to the actual outcome on admission and at 6 mo post injury, stratified by patient age (>65 yr vs ≤65 yr). RESULTS A total of 161 patients met the initial criteria; mean age 65 yr (SD = 21) and 110 male. Both CRASH and IMPACT correctly predicted 6-mo mortality rates and functional outcomes in most patients (range 61.7%-82.4%), with better predictive performance for patients not accepted to the center (range 84%-98%). There was no significant difference in the initial survival of elderly patients if accepted (78% [95% CI 50.6-104.0] vs 81% [95% CI 67.8-94.8] but were lower for those not accepted (24% [95% CI 4.2-43.7] vs 76% [95% CI 63.5-88.5], P = .027). CONCLUSION Patients >65 yr admitted to tertiary neuroscience center had good survival rates on admission and at 6 mo. The lesser ability of CRASH and IMPACT models to predict poorer outcomes when accepted suggests that acceptance to specialist centers may be able to improve outcome and suggests more optimistic treatment and acceptance of appropriate over 65 yr should be considered.

scholarly journals Diffusion Tensor Imaging of the Evolving Response to Mild Traumatic Brain Injury in Rats

2019 ◽  
Vol 13 ◽  
pp. 117906951985862 ◽  
Author(s):  
Wouter S Hoogenboom ◽  
Todd G Rubin ◽  
Kenny Ye ◽  
Min-Hui Cui ◽  
Kelsey C Branch ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Diffusion Tensor Imaging ◽  
Brain Injury ◽  
White Matter ◽  
Mild Traumatic Brain Injury ◽  
Diffusion Tensor ◽  
Axonal Injury ◽  
Mean Diffusivity ◽  
Multiple Time ◽  
Post Injury

Mild traumatic brain injury (mTBI), also known as concussion, is a serious public health challenge. Although most patients recover, a substantial minority suffers chronic disability. The mechanisms underlying mTBI-related detrimental effects remain poorly understood. Although animal models contribute valuable preclinical information and improve our understanding of the underlying mechanisms following mTBI, only few studies have used diffusion tensor imaging (DTI) to study the evolution of axonal injury following mTBI in rodents. It is known that DTI shows changes after human concussion and the role of delineating imaging findings in animals is therefore to facilitate understanding of related mechanisms. In this work, we used a rodent model of mTBI to investigate longitudinal indices of axonal injury. We present the results of 45 animals that received magnetic resonance imaging (MRI) at multiple time points over a 2-week period following concussive or sham injury yielding 109 serial observations. Overall, the evolution of DTI metrics following concussive or sham injury differed by group. Diffusion tensor imaging changes within the white matter were most noticeable 1 week following injury and returned to baseline values after 2 weeks. More specifically, we observed increased fractional anisotropy in combination with decreased radial diffusivity and mean diffusivity, in the absence of changes in axial diffusivity, within the white matter of the genu corpus callosum at 1 week post-injury. Our study shows that DTI can detect microstructural white matter changes in the absence of gross abnormalities as indicated by visual screening of anatomical MRI and hematoxylin and eosin (H&E)-stained sections in a clinically relevant animal model of mTBI. Whereas additional histopathologic characterization is required to better understand the neurobiological correlates of DTI measures, our findings highlight the evolving nature of the brain’s response to injury following concussion.

scholarly journals Acute or Delayed Treatment with Anatabine Improves Spatial Memory and Reduces Pathological Sequelae at Late Time-Points after Repetitive Mild Traumatic Brain Injury

2017 ◽  
Vol 34 (8) ◽  
pp. 1676-1691 ◽  
Author(s):  
Scott Ferguson ◽  
Benoit Mouzon ◽  
Daniel Paris ◽  
Destinee Aponte ◽  
Laila Abdullah ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Spatial Memory ◽  
Mild Traumatic Brain Injury ◽  
Late Time ◽  
Delayed Treatment ◽  
Time Points

scholarly journals Changes in the gray and white matter of patients with ischemic-edematous insults after traumatic brain injury

2019 ◽  
Vol 131 (4) ◽  
pp. 1243-1253 ◽  
Author(s):  
Hakseung Kim ◽  
Young-Tak Kim ◽  
Eun-Suk Song ◽  
Byung C. Yoon ◽  
Young Hun Choi ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
White Matter ◽  
Area Under The Curve ◽  
Hounsfield Unit ◽  
Predictive Performance ◽  
Prognostic Models ◽  
Quantitative Ct ◽  
Densitometric Analysis ◽  
Secondary Insult

OBJECTIVEGray matter (GM) and white matter (WM) are vulnerable to ischemic-edematous insults after traumatic brain injury (TBI). The extent of secondary insult after brain injury is quantifiable using quantitative CT analysis. One conventional quantitative CT measure, the gray-white matter ratio (GWR), and a more recently proposed densitometric analysis are used to assess the extent of these insults. However, the prognostic capacity of the GWR in patients with TBI has not yet been validated. This study aims to test the prognostic value of the GWR and evaluate the alternative parameters derived from the densitometric analysis acquired during the acute phase of TBI. In addition, the prognostic ability of the conventional TBI prognostic models (i.e., IMPACT [International Mission for Prognosis and Analysis of Clinical Trials in TBI] and CRASH [Corticosteroid Randomisation After Significant Head Injury] models) were compared to that of the quantitative CT measures.METHODSThree hundred patients with TBI of varying ages (92 pediatric, 94 adult, and 114 geriatric patients) and admitted between 2008 and 2013 were included in this retrospective cohort study. The normality of the density of the deep GM and whole WM was evaluated as the proportion of CT pixels with Hounsfield unit values of 31–35 for GM and 26–30 for WM on CT images of the entire supratentorial brain. The outcome was evaluated using the Glasgow Outcome Scale (GOS) at discharge (GOS score ≤ 3, n = 100).RESULTSLower proportions of normal densities in the deep GM and whole WM indicated worse outcomes. The proportion of normal WM exhibited a significant prognostic capacity (area under the curve [AUC] = 0.844). The association between the outcome and the normality of the WM density was significant in adult (AUC = 0.792), pediatric (AUC = 0.814), and geriatric (AUC = 0.885) patients. In pediatric patients, the normality of the overall density and the density of the GM were indicative of the outcome (AUC = 0.751). The average GWR was not associated with the outcome (AUC = 0.511). IMPACT and CRASH models showed adequate and reliable performance in the pediatric and geriatric groups but not in the adult group. The highest overall predictive performance was achieved by the densitometry-augmented IMPACT model (AUC = 0.881).CONCLUSIONSBoth deep GM and WM are susceptible to ischemic-edematous insults during the early phase of TBI. The extent of the secondary injury was better evaluated by analyzing the normality of the deep GM and WM rather than by calculating the GWR.

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