The diagnostic and prognostic value of glial fibrillary acidic protein in traumatic brain injury: a systematic review and meta-analysis

Pei, Yunlong; Tang, Xiaojia; Zhang, Enpeng; Lu, Kongye; Xia, Boming; Zhang, Jun; Huang, Yujia; Zhang, Hengzhu

doi:10.1007/s00068-022-01979-y

Cited by 9 publications

(5 citation statements)

References 32 publications

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“…Once broadly established and running, it is also anticipated that the novel databank will be open for the inclusion of additional laboratory datapoints and biomarkers. In the present issue, the meta-analysis by Yunlong Pei and colleagues once more confirmed the diagnostic (for traumatic intracranial lesions) and prognostic value (for mortality) of GFAP, in particular during early TBI [ 8 ].…”

mentioning

confidence: 66%

Traumatic brain injury: the research continues with higher data quality evolving!

Maegele

2023

Eur J Trauma Emerg Surg

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mentioning

confidence: 66%

Traumatic brain injury: the research continues with higher data quality evolving!

Maegele

2023

Eur J Trauma Emerg Surg

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“…43 There are other studies highlighting the diagnostic and prognostic potential of protein biomarkers, specifically GFAP and UCH-L1, in TBI patients. 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.…”

Section: Discussionmentioning

confidence: 99%

“…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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“…Recovery from traumatic brain injury (TBI) is limited by early diagnosis and treatment. While traditional methods like CT scans and GCS are used, they have limitations, such as negative CTs for mild TBI and sedation affecting GCS evaluation [ 74 , 75 ]. In addition, each exposure to the radiation required for CT scanning incrementally increases the risk of radiation-related malignancy [ 76 ].…”

Section: Gfapmentioning

confidence: 99%

Tau, Glial Fibrillary Acidic Protein, and Neurofilament Light Chain as Brain Protein Biomarkers in Cerebrospinal Fluid and Blood for Diagnosis of Neurobiological Diseases

Park,

KC,

Paneque

et al. 2024

IJMS

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Neurological damage is the pathological substrate of permanent disability in various neurodegenerative disorders. Early detection of this damage, including its identification and quantification, is critical to preventing the disease’s progression in the brain. Tau, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), as brain protein biomarkers, have the potential to improve diagnostic accuracy, disease monitoring, prognostic assessment, and treatment efficacy. These biomarkers are released into the cerebrospinal fluid (CSF) and blood proportionally to the degree of neuron and astrocyte damage in different neurological disorders, including stroke, traumatic brain injury, multiple sclerosis, neurodegenerative dementia, and Parkinson’s disease. Here, we review how Tau, GFAP, and NfL biomarkers are detected in CSF and blood as crucial diagnostic tools, as well as the levels of these biomarkers used for differentiating a range of neurological diseases and monitoring disease progression. We also discuss a biosensor approach that allows for the real-time detection of multiple biomarkers in various neurodegenerative diseases. This combined detection system of brain protein biomarkers holds significant promise for developing more specific and accurate clinical tools that can identify the type and stage of human neurological diseases with greater precision.

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The diagnostic and prognostic value of glial fibrillary acidic protein in traumatic brain injury: a systematic review and meta-analysis

Cited by 9 publications

References 32 publications

Traumatic brain injury: the research continues with higher data quality evolving!

Traumatic brain injury: the research continues with higher data quality evolving!

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

Tau, Glial Fibrillary Acidic Protein, and Neurofilament Light Chain as Brain Protein Biomarkers in Cerebrospinal Fluid and Blood for Diagnosis of Neurobiological Diseases

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