Longitudinal Course of Traumatic Brain Injury Biomarkers for the Prediction of Clinical Outcomes: A Review

Peters, Austin; Schnell, Eric; Saugstad, Julie A.

doi:10.1089/neu.2020.7448

Cited by 11 publications

(18 citation statements)

References 33 publications

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“…Of all the patients that required airway support, 31.8% of them were already intubated when they arrived at the Emergency Department. Many studies showed that a risk factor associated with mortality is a need for MV, however, the results of our study did not support this (12). TBI deaths due to cardiac arrest are also very high.…”

Section: Discussioncontrasting

confidence: 99%

Predictors of Early Outcome in Patients Admitted at the Emergency Department with Traumatic Brain Injury: a Retrospective Cross-Sectional Study

Shaikh¹,

Shah²,

Mehmood³

2021

PJMHS

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Aim: The purpose of this study was to determine the factors that help predict early mortality in patients in the emergency department that have suffered a severe traumatic brain injury. Study design: A retrospective cross-sectional study Place and duration: Chandka Medical College/ Civil Hospital Larkana. April 2018- March 2020 Methodology: Retrospective analysis was used to study 198 patients that were admitted to the emergency department due to severe traumatic brain injury. The radiological, clinical, basic demographical, and biological data were all recorded when the patient was first admitted and while they were staying in the emergency department. Results: The results showed that 42 patients died. According to the univariate analysis, the presence of low value of Glasgow coma scale, bilateral mydriasis, shock, the high value of injury severity score, and cardiac arrest played a major role in the deaths of such patients. Poor outcome was associated with cerebral hematoma, subdural hematoma, and meningeal hemorrhage. A link was seen between poor prognosis and cardiac arrest and cerebral hematoma. Conclusions: In our study, subdural hematoma, intracranial hematoma, and meningeal hemorrhage were associated with mortality. To improve the prognosis of severe traumatic brain injury, prehospital care has to be improved. Additionally, futile resuscitations should be avoided. It is also very important to take a CT scan of the head as urgently as possible to detect operable mass lesions. Keywords: traumatic brain injury, early outcome, emergency department

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Section: Discussioncontrasting

confidence: 99%

Predictors of Early Outcome in Patients Admitted at the Emergency Department with Traumatic Brain Injury: a Retrospective Cross-Sectional Study

Shaikh¹,

Shah²,

Mehmood³

2021

PJMHS

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“…44,45 It has been shown that GFAP and UCH-L1 can aid in detecting intracranial lesions, predicting unfavorable outcomes, and guiding therapeutic interventions, according to meta-analyses and longitudinal studies. 44,46 Moreover, evaluating multiple biomarkers with distinct cellular origins can improve outcome prediction models, highlighting the importance of incorporating these biomarkers into evaluations of TBI patients. 45 Additionally, numerous studies have proposed cutoff values by illustrating a correlation between elevated GFAP and UCH-L1 levels with TBI diagnosis or prognosis.…”

Section: Discussionmentioning

confidence: 99%

Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study

Zhu,

Ozkara,

Chen

et al. 2023

Neuroradiol J

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Purpose We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients. Methods In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables. Results Four hundred forty patients were used to predict predictions 1 and 2, while 271 patients were used in prediction 3. Due to Prediction 3’s hospitalization requirement, deceased and discharged patients could not be utilized. The Random Forest model achieved an average accuracy of 1.00 for prediction 1 and an accuracy of 0.99 for prediction 2. The Random Forest model achieved a mean accuracy of 0.93 for prediction 3. Key features were extracranial injury, hemorrhage, UCH-L1 for prediction 1; The Glasgow Coma Scale, age, GFAP for prediction 2; and GFAP, subdural hemorrhage volume, and pneumocephalus for prediction 3, per SHAP analysis. Conclusion Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers.

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“…It is believed that these biomarkers may be more cost-effective than other diagnostic tests. To date, biofluid-based biomarkers known to have diagnostic and prognostic potential in TBI include S100B, neuron-specific enolase, UCH-L1 (neuronal damage marker), and GFAP (astrocyte damage marker) [ 65 ].…”

Section: Biomarkersmentioning

confidence: 99%

Treatment and Rehabilitation for Traumatic Brain Injury: Current Update

Min

Shin

2022

Brain Neurorehabil

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Longitudinal Course of Traumatic Brain Injury Biomarkers for the Prediction of Clinical Outcomes: A Review

Cited by 11 publications

References 33 publications

Predictors of Early Outcome in Patients Admitted at the Emergency Department with Traumatic Brain Injury: a Retrospective Cross-Sectional Study

Predictors of Early Outcome in Patients Admitted at the Emergency Department with Traumatic Brain Injury: a Retrospective Cross-Sectional Study

Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study

Treatment and Rehabilitation for Traumatic Brain Injury: Current Update

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