Jiang JY et al (2019) Traumatic brain injury in China. Lancet Neurol 18(3):286–295

Google Scholar 

Maas AIR et al (2017) Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research. Lancet Neurol 16(12):987–1048

Google Scholar 

Dewan MC et al (2018) Estimating the global incidence of traumatic brain injury. J Neurosurg 130(4):1080–1097

Google Scholar 

Rizoli S et al (2016) Early prediction of outcome after severe traumatic brain injury: a simple and practical model. BMC Emerg Med 16(1):32

Google Scholar 

Sobuwa S et al (2014) Predicting outcome in severe traumatic brain injury using a simple prognostic model. S Afr Med J 104(7):492–494

Google Scholar 

Gómez PA et al (2014) Validation of a prognostic score for early mortality in severe head injury cases. J Neurosurg 121(6):1314–1322

Google Scholar 

Lingsma HF et al (2010) Early prognosis in traumatic brain injury: from prophecies to predictions. Lancet Neurol 9(5):543–554

Google Scholar 

Tasaki O et al (2009) Prognostic indicators and outcome prediction model for severe traumatic brain injury. J Trauma 66(2):304–308

Google Scholar 

Perel P et al (2008) Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ 336(7641):425–429

Google Scholar 

Maas AI et al (2007) Prognosis and clinical trial design in traumatic brain injury: the IMPACT study. J Neurotrauma 24(2):232–238

Google Scholar 

Cremer OL et al (2006) Prognosis following severe head injury: Development and validation of a model for prediction of death, disability, and functional recovery. J Trauma 61(6):1484–1491

Google Scholar 

Edwards P et al (2005) Final results of MRC CRASH, a randomised placebo-controlled trial of intravenous corticosteroid in adults with head injury-outcomes at 6 months. Lancet 365(9475):1957–1959

Google Scholar 

Moorthy D et al (2021) Prediction of Outcome Based on Trauma and Injury Severity Score, IMPACT and CRASH Prognostic Models in Moderate-to-Severe Traumatic Brain Injury in the Elderly. Asian J Neurosurg 16(3):500–506

Google Scholar 

Chen L et al (2022) Performance of the IMPACT and Helsinki models for predicting 6-month outcomes in a cohort of patients with traumatic brain injury undergoing cranial surgery. Front Neurol 13:1031865

Google Scholar 

Steyerberg EW et al (2008) Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med 5(8):e165 (discussion e165)

Google Scholar 

Lang L et al (2023) An independently validated nomogram for individualised estimation of short-term mortality risk among patients with severe traumatic brain injury: a modelling analysis of the CENTER-TBI China Registry Study. EClinicalMedicine 59:101975

Google Scholar 

Nasrallah F et al (2023) PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury (PREDICT-TBI) study protocol: an observational, prospective, multicentre cohort study for the prediction of outcome in moderate-to-severe TBI. BMJ Open 13(4):e067740

Google Scholar 

Jain S et al (2019) Automatic Quantification of Computed Tomography Features in Acute Traumatic Brain Injury. J Neurotrauma 36(11):1794–1803

Google Scholar 

Marshall LF et al (1991) A new classification of head injury based on computerized tomography. J Neurosurg 75(Supplement):S14–S20

Google Scholar 

Maas AI et al (2005) Prediction of outcome in traumatic brain injury with computed tomographic characteristics: a comparison between the computed tomographic classification and combinations of computed tomographic predictors. Neurosurgery 57(6):1173–82 (discussion 1173–82)

Google Scholar 

Raj R et al (2014) Predicting outcome in traumatic brain injury: development of a novel computerized tomography classification system (Helsinki computerized tomography score). Neurosurgery 75(6):632–46 (discussion 646–7)

Google Scholar 

Frodsham KM et al (2020) Day-of-injury computed tomography and longitudinal rehabilitation outcomes: a comparison of the marshall and rotterdam computed tomography scoring methods. Am J Phys Med Rehabil 99(9):821–829

Google Scholar 

Nelson DW et al (2010) Extended analysis of early computed tomography scans of traumatic brain injured patients and relations to outcome. J Neurotrauma 27(1):51–64

Google Scholar 

Liao B et al (2023) The prognostic value of systemic immune-inflammation index in patients with aneurysmal subarachnoid hemorrhage: a systematic review. Neurosurg Rev 46(1):219

Google Scholar 

Luo S et al (2022) The clinical value of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and D-dimer-to-fibrinogen ratio for predicting pneumonia and poor outcomes in patients with acute intracerebral hemorrhage. Front Immunol 13:1037255

CAS  Google Scholar 

Nasr IW, Chun Y, Kannan S (2019) Neuroimmune responses in the developing brain following traumatic brain injury. Exp Neurol 320:112957

CAS  Google Scholar 

Jassam YN et al (2017) Neuroimmunology of Traumatic Brain Injury: Time for a Paradigm Shift. Neuron 95(6):1246–1265

CAS  Google Scholar 

Jo S et al (2020) The prognostic value of platelet-to-lymphocyte ratio on in-hospital mortality in admitted adult traffic accident patients. PLoS ONE 15(6):e0233838

CAS  Google Scholar 

Chen L et al (2022) Systemic immune inflammation index and peripheral blood carbon dioxide concentration at admission predict poor prognosis in patients with severe traumatic brain injury. Front Immunol 13:1034916

CAS  Google Scholar 

Ge X et al (2022) Red cell distribution width to platelet count ratio: a promising routinely available indicator of mortality for acute traumatic brain injury. J Neurotrauma 39(1–2):159–171

Google Scholar 

Li W, Deng W (2022) Platelet-to-lymphocyte ratio predicts short-term mortality in patients with moderate to severe traumatic brain injury. Sci Rep 12(1):13976

CAS  Google Scholar 

Mao B et al (2022) The predictive role of systemic inflammation response index in the prognosis of traumatic brain injury: A propensity score matching study. Front Neurol 13:995925

Google Scholar 

Wang R et al (2021) A prognostic model incorporating red cell distribution width to platelet ratio for patients with traumatic brain injury. Ther Clin Risk Manag 17:1239–1248

CAS  Google Scholar 

Carney N et al (2017) Guidelines for the management of severe traumatic brain injury. Fourth Edition Neurosurg 80(1):6–15

Google Scholar 

Steyerberg EW et al (2019) Case-mix, care pathways, and outcomes in patients with traumatic brain injury in CENTER-TBI: a European prospective, multicentre, longitudinal, cohort study. Lancet Neurol 18(10):923–934

Google Scholar 

Leary OP et al (2021) Computer-assisted measurement of traumatic brain hemorrhage volume is more predictive of functional outcome and mortality than standard ABC/2 method: an analysis of computed tomography imaging data from the progesterone for traumatic brain injury experimental clinical treatment phase-III trial. J Neurotrauma 38(5):604–615

Vijian K et al (2020) Initial leucocytosis and other significant indicators of poor outcome in severe traumatic brain injury: an observational study. Chin Neurosurg J 6:5

Google Scholar 

Deepika A et al (2015) Comparison of predictability of Marshall and Rotterdam CT scan scoring system in determining early mortality after traumatic brain injury. Acta Neurochir (Wien) 157(11):2033–2038

Google Scholar 

Chen JY et al (2023) The establishment and validation of a prediction model for traumatic intracranial injury patients: a reliable nomogram. Front Neurol 14:1165020

Google Scholar 

Dijkland SA et al (2020) Prognosis in moderate and severe traumatic brain injury: a systematic review of contemporary models and validation studies. J Neurotrauma 37(1):1–13

Google Scholar 

Thelin EP et al (2017) Evaluation of novel computerized tomography scoring systems in human traumatic brain injury: An observational, multicenter study. PLoS Med 14(8):e1002368

Google Scholar 

Biuki NM et al (2023) Comparison of the predictive value of the Helsinki, Rotterdam, and Stockholm CT scores in predicting 6-month outcomes in patients with blunt traumatic brain injuries. Chin J Traumatol 26(6):357–362

Google Scholar 

Neugebauer H et al (2013) Space-occupying cerebellar infarction: complications, treatment, and outcome. Neurosurgical Focus FOC 34(5):E8

Google Scholar 

Petr O et al (2023) Link between both infratentorial and supratentorial intracranial pressure burdens and final outcome in patients with infratentorial brain injury. J Neurosurg 139(5):1430–1438

Google Scholar 

Su TM et al (2023) Head trauma associated with supra- and infratentorial epidural hematoma: diagnostic and surgical considerations. World Neurosurg 176:e273–e280

Google Scholar 

Jang JW et al (2011) Traumatic epidural haematoma of the posterior cranial fossa. Br J Neurosurg 25(1):55–61

Google Scholar 

Bernard F et al (2008) Serum albumin level as a predictor of outcome in traumatic brain injury: potential for treatment. J Trauma 64(4):872–875

CAS  Google Scholar 

Luo HC et al (2019) Comparison of admission serum albumin and hemoglobin as predictors of outcome in children with moderate to severe traumatic brain injury: A retrospective study. Medicine (Baltimore) 98(44):e17806

Google Scholar 

Chen D et al (2014) Serum albumin and prealbumin predict the poor outcome of traumatic brain injury. PLoS ONE 9(3):e93167

Google Scholar 

Garwe T et al (2016) Hypoalbuminemia at admission is associated with increased incidence of in-hospital complications in geriatric trauma patients. Am J Surg 212(1):109–115

Google Scholar 

Corrigan F et al (2016) Neurogenic inflammation after traumatic brain injury and its potentiation of classical inflammation. J Neuroinflammation 13(1):264

Google Scholar 

Sorby-Adams AJ et al (2017) The role of neurogenic inflammation in blood-brain barrier disruption and development of cerebral oedema following acute central nervous system (CNS) injury. Int J Mol Sci 18(8):1788

Google Scholar 

Rodoman GV et al (2006) Serum albumin in systemic inflammatory reaction syndrome. Anesteziol Reanimatol 2:62–64

Google Scholar 

Healey C et al (2003) Improving the Glasgow Coma Scale score: motor score alone is a better predictor. J Trauma 54(4):671–8 (discussion 678–80)

CAS  Google Scholar 

Ross SE et al (1998) Efficacy of the motor component of the Glasgow Coma Scale in trauma triage. J Trauma 45(1):42–44

CAS  Google Scholar 

Hoffmann M et al (2012) Pupil evaluation in addition to Glasgow Coma Scale components in prediction of traumatic brain injury and mortality. Br J Surg 99(Suppl 1):122–130

Google Scholar 

Sulhan S et al (2020) Neuroinflammation and blood-brain barrier disruption following traumatic brain injury: Pathophysiology and potential therapeutic targets. J Neurosci Res 98(1):19–28

CAS  Google Scholar 

Loane DJ, Faden AI (2010) Neuroprotection for traumatic brain injury: translational challenges and emerging therapeutic strategies. Trends Pharmacol Sci 31(12):596–604

CAS  Google Scholar 

Winkler EA et al (2016) Cerebral edema in traumatic brain injury: pathophysiology and prospective therapeutic targets. Neurosurg Clin N Am 27(4):473–488

Google Scholar 

Nimmerjahn A, Kirchhoff F, Helmchen F (2005) Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo. Science 308(5726):1314–1318

CAS  Google Scholar 

Kumar A, Loane DJ (2012) Neuroinflammation after traumatic brain injury: opportunities for therapeutic intervention. Brain Behav Immun 26(8):1191–1201

Google Scholar 

Aungst SL et al (2014) Repeated mild traumatic brain injury causes chronic neuroinflammation, changes in hippocampal synaptic plasticity, and associated cognitive deficits. J Cereb Blood Flow Metab 34(7):1223–1232

CAS  Google Scholar 

Loane DJ et al (2014) Novel mGluR5 positive allosteric modulator improves functional recovery, attenuates neurodegeneration, and alters microglial polarization after experimental traumatic brain injury. Neurotherapeutics 11(4):857–869

CAS  Google Scholar 

Mezquita L et al (2021) Predicting immunotherapy outcomes under therapy in patients with advanced NSCLC using dNLR and its early dynamics. Eur J Cancer 151:211–220

CAS  Google Scholar 

Xiu WJ et al (2022) ALB-dNLR Score Predicts Mortality in Coronary Artery Disease Patients After Percutaneous Coronary Intervention. Front Cardiovasc Med 9:709868

CAS  Google Scholar