scholarly journals A predictive model for postoperative progressive haemorrhagic injury in traumatic brain injuries

BMC Neurology ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Tiange Chen ◽  
Siming Chen ◽  
Yun Wu ◽  
Yilei Chen ◽  
Lei Wang ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Predictive Model ◽  
Brain Injuries ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Individual Risk ◽  
Lasso Regression ◽  
Predictive Tool

Abstract Background Progressive haemorrhagic injury after surgery in patients with traumatic brain injury often results in poor patient outcomes. This study aimed to develop and validate a practical predictive tool that can reliably estimate the risk of postoperative progressive haemorrhagic injury (PHI) in patients with traumatic brain injury (TBI). Methods Data from 645 patients who underwent surgery for TBI between March 2018 and December 2020 were collected. The outcome was postoperative intracranial PHI, which was assessed on postoperative computed tomography. The least absolute shrinkage and selection operator (LASSO) regression model, univariate analysis, and Delphi method were applied to select the most relevant prognostic predictors. We combined conventional coagulation test (CCT) data, thromboelastography (TEG) variables, and several predictors to develop a predictive model using binary logistic regression and then presented the results as a nomogram. The predictive performance of the model was assessed with calibration and discrimination. Internal validation was assessed. Results The signature, which consisted of 11 selected features, was significantly associated with intracranial PHI (p < 0.05, for both primary and validation cohorts). Predictors in the prediction nomogram included age, S-pressure, D-pressure, pulse, temperature, reaction time, PLT, prothrombin time, activated partial thromboplastin time, FIB, and kinetics values. The model showed good discrimination, with an area under the curve of 0.8694 (95% CI, 0.8083–0.9304), and good calibration. Conclusion This model is based on a nomogram incorporating CCT and TEG variables, which can be conveniently derived at hospital admission. It allows determination of this individual risk for postoperative intracranial PHI and will facilitate a timely intervention to improve outcomes.

scholarly journals A Predictive Model for Postoperative Progressive Haemorrhagic Injury in Traumatic Brain Injuries

2021 ◽  
Author(s):  
Tiange Chen ◽  
Siming Chen ◽  
Yilei Chen ◽  
Lei Wang ◽  
Yun Wu ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Predictive Model ◽  
Brain Injuries ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Individual Risk ◽  
Lasso Regression ◽  
Predictive Tool

Abstract Background Progressive haemorrhagic injury after surgery in patients with traumatic brain injury often results in poor patient outcomes. This study aimed to develop and validate a practical predictive tool that can reliably estimate the risk of postoperative progressive haemorrhagic injury (PHI) in patients with traumatic brain injury (TBI). Methods Data from 645 patients who underwent surgery for TBI between March 2018 and December 2020 were collected. The outcome was postoperative intracranial PHI, which was assessed on postoperative computed tomography. The least absolute shrinkage and selection operator (LASSO) regression model, univariate analysis, and Delphi method were applied to select the most relevant prognostic predictors. We combined conventional coagulation test (CCT) data, thromboelastography (TEG) variables, and several predictors to develop a predictive model using binary logistic regression and then presented the results as a nomogram. The predictive performance of the model was assessed with calibration and discrimination. Internal validation was assessed. ResultsThe signature, which consisted of 11 selected features, was significantly associated with intracranial PHI (p < 0.05, for both primary and validation cohorts). Predictors in the prediction nomogram included age, S-pressure, D-pressure, pulse, temperature, reaction time, PLT, prothrombin time, activated partial thromboplastin time, FIB, and kinetics values. The model showed good discrimination, with an area under the curve of 0.8694 (95% CI, 0.8083–0.9304), and good 3 calibration. ConclusionThis model is based on a nomogram incorporating CCT and TEG variables, which can be conveniently derived at hospital admission. It allows determination of this individual risk for postoperative intracranial PHI and will facilitate a timely intervention to improve outcomes.

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.

scholarly journals P078: Derivation and validation of a non return to work predictive model three months after a mild traumatic brain injury

CJEM ◽  
2017 ◽  
Vol 19 (S1) ◽  
pp. S104
Author(s):  
N. Le Sage ◽  
J. Chauny ◽  
M. Émond ◽  
L. Moore ◽  
P.M. Archambault ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Return To Work ◽  
Predictive Model ◽  
Mild Traumatic Brain Injury ◽  
Predictive Value ◽  
Prospective Cohort ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Area Under The Curve

Introduction: Mild traumatic brain injury (mTBI) is a common problem and until now, ED physicians don’t have any tool to predict when the patient will return to work. The purpose of this study is to develop and validate a clinical decision rule to identify the ED patients who are at risk of non-return to work or to school three months after a mTBI. Methods: Patients were recruiting in five Level I and II Trauma Centers ED in the province of Québec. All patients were referred for a systematic telephone follow-up after three months. Information about their return to work/school, partial or complete, was collected. Log binomial regression was used to develop a predictive model and the validation of this model was performed on a different prospective cohort. Results: 13,7% of the patients did not return to work/school at three months. The final model was derived from a prospective cohort of 398 patients and included three risk factors: motor vehicle accident (2 points), loss of consciousness (1 point) and headache during the emergency department assessment (1 point). With a one-point threshold, this model has a sensitivity of 97% and a negative predictive value (NPV) of 98%. However, the specificity is only 23% and the positive predictive value (PPV) is 17%. The area under the curve is 0.786. Validation of the model was performed with a new prospective cohort of 517 patients, and demonstrated a sensitivity of 86% and a NPV of 91%. Conclusion: Although this model is not very specific, its high sensitivity and NPV indicate to the clinician that mTBI patients who don’t have any of the three criteria are at low risk of prolonged work stoppage after their trauma.

scholarly journals Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model

2022 ◽  
Vol 2 (1) ◽  
pp. 106-123
Author(s):  
Nor Safira Elaina Mohd Noor ◽  
Haidi Ibrahim ◽  
Muhammad Hanif Che Lah ◽  
Jafri Malin Abdullah
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Independent Component Analysis ◽  
Model Development ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Component Analysis ◽  
Independent Component ◽  
Moderate Traumatic Brain Injury ◽  
Continuous Eeg

The computational electroencephalogram (EEG) is recently garnering significant attention in examining whether the quantitative EEG (qEEG) features can be used as new predictors for the prediction of recovery in moderate traumatic brain injury (TBI). However, the brain’s recorded electrical activity has always been contaminated with artifacts, which in turn further impede the subsequent processing steps. As a result, it is crucial to devise a strategy for meticulously flagging and extracting clean EEG data to retrieve high-quality discriminative features for successful model development. This work proposed the use of multiple artifact rejection algorithms (MARA), which is an independent component analysis (ICA)-based algorithm, to eliminate artifacts automatically, and explored their effects on the predictive performance of the random undersampling boosting (RUSBoost) model. Continuous EEG were acquired using 64 electrodes from 27 moderate TBI patients at four weeks to one-year post-accident. The MARA incorporates an artifact removal stage based on ICA prior to RUSBoost, SVM, DT, and k-NN classification. The area under the curve (AUC) of RUSBoost was higher in absolute power spectral density (PSD) in AUCδ = 0.75, AUC α = 0.73 and AUCθ = 0.71 bands than SVM, DT, and k-NN. The MARA has provided a good generalization performance of the RUSBoost prediction model.

MANAGEMENT OF MULTIPLE TRAUMATIC INTRACRANIAL HEMATOMAS

2020 ◽  
Vol 3 (1) ◽  
pp. 70-74
Author(s):  
Rustam Hazratkulov ◽  
Keyword(s):  
Traumatic Brain Injury ◽  
Surgical Treatment ◽  
Brain Injury ◽  
Hospital Stay ◽  
Treatment Outcomes ◽  
Brain Injuries ◽  
Traumatic Brain Injuries ◽  
Diagnostic Approach ◽  
Early Activation ◽  
Intracranial Hematomas

Multiple traumatic hematomas (MG) account for 0.74% of all traumatic brain injuries. A comprehensive diagnostic approach to multiple traumatic intracranial hematomas allows to establish a diagnosis in the early stages of traumatic brain injury and to determine treatment tactics. A differentiated approach to the choice of surgical treatment of multiple hematomas allows to achieve satisfactory results and treatment outcomes, which accordingly contributes to the early activation of the patient, a reduction in hospital stay, a decrease in mortality and disabilityin patients with traumatic brain injury

Rapid Detection of Significant Traumatic Brain Injury Requiring Emergency Intervention

2020 ◽  
pp. 000313482097335
Author(s):  
Isaac W. Howley ◽  
Jonathan D. Bennett ◽  
Deborah M. Stein
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Brain Injuries ◽  
Trauma Team ◽  
Altered Mental Status ◽  
Acute Care Surgery ◽  
Ct Scanner ◽  
Mickey Mouse ◽  
Clinically Significant ◽  
Care Surgery

Moderate and severe traumatic brain injuries (TBI) are a major cause of severe morbidity and mortality; rapid diagnosis and management allow secondary injury to be minimized. Traumatic brain injury is only one of many potential causes of altered mental status; head computed tomography (HCT) is used to definitively diagnose TBI. Despite its widespread use and obvious importance, interpretation of HCT images is rarely covered by formal didactics during general surgery or even acute care surgery training. The schema illustrated here may be applied in a rapid and reliable fashion to HCT images, expediting the diagnosis of clinically significant traumatic brain injury that warrants emergent medical and surgical therapies to reduce intracranial pressure. It consists of 7 normal anatomic structures (cerebrospinal fluid around the brain stem, open fourth ventricle, “baby’s butt,” “Mickey Mouse ears,” absence of midline shift, sulci and gyri, and gray-white differentiation). These 7 features can be seen even as the CT scanner obtains images, allowing the trauma team to expedite medical management of intracranial hypertension and pursue neurosurgical consultation prior to radiologic interpretation if the features are abnormal.

scholarly journals Reply to Comment on Tsai, Y.-C., et al. Association of Stress-Induced Hyperglycemia and Diabetic Hyperglycemia with Mortality in Patients with Traumatic Brain Injury: Analysis of a Propensity Score-Matched Population. Int. J. Environ. Res. Public Health 2020, 17, 4266

2021 ◽  
Vol 18 (5) ◽  
pp. 2531
Author(s):  
Yu-Chin Tsai ◽  
Shao-Chun Wu ◽  
Ting-Min Hsieh ◽  
Hang-Tsung Liu ◽  
Chun-Ying Huang ◽  
...  
Keyword(s):  
Public Health ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Propensity Score ◽  
Brain Injuries ◽  
Traumatic Brain Injuries ◽  
Injury Analysis

Thank you for Eduardo Mekitarian Filho’s appreciation of our work on the study of stress-induced hyperglycemia (SIH) and diabetic hyperglycemia (DH) in patients with traumatic brain injuries [...]

scholarly journals POS1439 A MULTI-PREDICTOR MODEL TO PREDICT RISK OF SCLERODERMA RENAL CRISIS IN SYSTEMIC SCLEROSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1004.1-1004
Author(s):  
D. Xu ◽  
R. Mu
Keyword(s):  
Systemic Sclerosis ◽  
Prediction Model ◽  
Renal Involvement ◽  
Area Under The Curve ◽  
Individual Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Scleroderma Renal Crisis ◽  
Lasso Regression ◽  
Patients At Risk ◽  
Renal Crisis

Background:Scleroderma renal crisis (SRC) is a life-threatening syndrome. The early identification of patients at risk is essential for timely treatment to improve the outcome[1].Objectives:We aimed to provide a personalized tool to predict risk of SRC in systemic sclerosis (SSc).Methods:We tried to set up a SRC prediction model based on the PKUPH-SSc cohort of 302 SSc patients. The least absolute shrinkage and selection operator (Lasso) regression was used to optimize disease features. Multivariable logistic regression analysis was applied to build a SRC prediction model incorporating the features of SSc selected in the Lasso regression. Then, a multi-predictor nomogram combining clinical characteristics was constructed and evaluated by discrimination and calibration.Results:A multi-predictor nomogram for evaluating the risk of SRC was successfully developed. In the nomogram, four easily available predictors were contained including disease duration <2 years, cardiac involvement, anemia and corticosteroid >15mg/d exposure. The nomogram displayed good discrimination with an area under the curve (AUC) of 0.843 (95% CI: 0.797-0.882) and good calibration.Conclusion:The multi-predictor nomogram for SRC could be reliably and conveniently used to predict the individual risk of SRC in SSc patients, and be a step towards more personalized medicine.References:[1]Woodworth TG, Suliman YA, Li W, Furst DE, Clements P (2016) Scleroderma renal crisis and renal involvement in systemic sclerosis. Nat Rev Nephrol 12 (11):678-91.Disclosure of Interests:None declared

Early versus late decompressive craniectomy in traumatic brain injury: A retrospective comparative case study

Trauma ◽  
2020 ◽  
pp. 146040862093576
Author(s):  
Nida Fatima ◽  
Mujeeb-Ur-Rehman ◽  
Samia Shaukat ◽  
Ashfaq Shuaib ◽  
Ali Raza ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Regression Analysis ◽  
Brain Injury ◽  
Functional Outcome ◽  
Decompressive Craniectomy ◽  
Univariate Analysis ◽  
Timing Of Surgery ◽  
Comparative Case Study ◽  
Outcome Parameters ◽  
Good Functional Outcome

Objectives Decompressive craniectomy is a last-tier therapy in the treatment of raised intracranial pressure after traumatic brain injury. We report the association of demographic, radiographic, and injury characteristics with outcome parameters in early (<24 h) and late (≥24 h) decompressive craniectomy following traumatic brain injury. Methods We retrospectively identified 204 patients (158 (early decompressive craniectomy) and 46 (late decompressive craniectomy)), with a median age of 34 years (range 2–78 years) between 2015 and 2018. The primary endpoint was Glasgow Outcome Scale Extended (GOSE) at 60 days, while secondary endpoints included Glasgow Coma Score (GCS) at discharge, mortality at 30 days, and length of hospital stay. Regression analysis was used to assess the independent predictive variables of functional outcome. Results With a clinical follow-up of 60 days, the good functional outcome (GOSE = 5–8) was 73.5% versus 74.1% (p = 0.75) in early and late decompressive craniectomy, respectively. GCS ≥ 9 at discharge was 82.2% versus 91.3% (p = 0.21), mortality at 30 days was 10.8% versus 8.7% (p = 0.39), and length of stay in the hospital was 21 days versus 28 days (p = 0.20), respectively, in early and late decompressive craniectomy groups. Univariate analysis identified that GCS at admission (0.07 (0.32–0.18; < 0.05)) and indication for decompressive craniectomy (3.7 (1.3–11.01; 0.01)) are significantly associated with good functional outcome. Multivariate regression analysis revealed that GCS at admission (<9/≥9) (0.07 (0.03–0.16; <0.05)) and indication for decompressive craniectomy (extradural alone/ other hematoma) (1.75 (1.09–3.25; 0.02)) were significant independent predictors of good functional outcome irrespective of the timing of surgery. Conclusions Our results corroborate that the timing of surgery does not affect the outcome parameters. Furthermore, GCS ≥ 9 and/or extra dural hematoma are associated with relatively good clinical outcome after decompressive craniectomy.

scholarly journals Study of Traumatic Brain Injury Due To Recent Earthquake in Manipal Teaching Hospital

2016 ◽  
Vol 12 (2) ◽  
pp. 63-66
Author(s):  
Bal G Karmacharya ◽  
Brijesh Sathian
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Head Injury ◽  
Teaching Hospital ◽  
Brain Injuries ◽  
Injury Severity ◽  
Traumatic Brain Injuries ◽  
The Body ◽  
Mild Head Injury ◽  
Associated Injuries

The objective of this study was to review the demographics, causes injury, severity, treatment and outcome of traumatic brain injuries in victims of the April 2015 earthquake who were admitted in Manipal Teaching Hospital, Pokhara. A total of 37 patients was admitted under Neurosurgery Services. Collapse of buildings was the commonest cause of head injury. The majority of them had mild head injury. Associated injuries to other parts of the body were present in 40.54% patients.Nepal Journal of Neuroscience 12:63-66, 2015

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