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Semin Neurol 2023; 43(05): 664-674
DOI: 10.1055/s-0043-1775749
Review Article

Clinical Grading Scales and Neuroprognostication in Acute Brain Injury

Sahily Reyes-Esteves
1   Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
,
Monisha Kumar
1   Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
,
Scott E. Kasner
1   Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
,
Jens Witsch 
1   Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
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Abstract

Prediction of neurological clinical outcome after acute brain injury is critical because it helps guide discussions with patients and families and informs treatment plans and allocation of resources. Numerous clinical grading scales have been published that aim to support prognostication after acute brain injury. However, the development and validation of clinical scales lack a standardized approach. This in turn makes it difficult for clinicians to rely on prognostic grading scales and to integrate them into clinical practice. In this review, we discuss quality measures of score development and validation and summarize available scales to prognosticate outcomes after acute brain injury. These include scales developed for patients with coma, cardiac arrest, ischemic stroke, nontraumatic intracerebral hemorrhage, subarachnoid hemorrhage, and traumatic brain injury; for each scale, we discuss available validation studies.

Keywords

clinical grading scales - neuroprognostication - traumatic brain injury - stroke - cardiac arrest

Supplementary Material



Publikationsverlauf

Artikel online veröffentlicht:
03. Oktober 2023

© 2023. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 

  • References

  • 1 Feigin VL, Abajobir AA, Abate KH. et al; GBD 2015 Neurological Disorders Collaborator Group. Global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol 2017; 16 (11) 877-897
    CrossrefPubMedGoogle Scholar
  • 2 Maas AIR, Menon DK, Adelson PD. et al; InTBIR Participants and Investigators. Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research. Lancet Neurol 2017; 16 (12) 987-1048
    CrossrefPubMedGoogle Scholar
  • 3 Wartenberg KE, Hwang DY, Haeusler KG. et al. Gap analysis regarding prognostication in neurocritical care: a joint statement from the German Neurocritical Care Society and the Neurocritical Care Society. Neurocrit Care 2019; 31 (02) 231-244
    CrossrefPubMedGoogle Scholar
  • 4 Helmrich IRAR, Mikolić A, Kent DM. et al. Does poor methodological quality of prediction modeling studies translate to poor model performance? An illustration in traumatic brain injury. Diagn Progn Res 2022; 6 (01) 8
    CrossrefPubMedGoogle Scholar
  • 5 Finley Caulfield A, Mlynash M, Eyngorn I. et al. Prognostication of ICU patients by providers with and without neurocritical care training. Neurocrit Care 2022; 37 (01) 190-199
    CrossrefPubMedGoogle Scholar
  • 6 Hwang DY, Dell CA, Sparks MJ. et al. Clinician judgment vs formal scales for predicting intracerebral hemorrhage outcomes. Neurology 2016; 86 (02) 126-133
    CrossrefPubMedGoogle Scholar
  • 7 Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J 2014; 35 (29) 1925-1931
    CrossrefPubMedGoogle Scholar
  • 8 Witsch J, Siegerink B, Nolte CH. et al. Prognostication after intracerebral hemorrhage: a review. Neurol Res Pract 2021; 3 (01) 22
    CrossrefPubMedGoogle Scholar
  • 9 Auriemma CL, Taylor SP, Harhay MO, Courtright KR, Halpern SD. Hospital-free days: a pragmatic and patient-centered outcome for trials among critically and seriously Ill patients. Am J Respir Crit Care Med 2021; 204 (08) 902-909
    CrossrefPubMedGoogle Scholar
  • 10 Huang Y, Li W, Macheret F, Gabriel RA, Ohno-Machado L. A tutorial on calibration measurements and calibration models for clinical prediction models. J Am Med Inform Assoc 2020; 27 (04) 621-633
    CrossrefPubMedGoogle Scholar
  • 11 Vickers AJ, Van Calster B, Steyerberg EW. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ 2016; 352: i6
    CrossrefPubMedGoogle Scholar
  • 12 Vickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res 2019; 3 (01) 18
    CrossrefPubMedGoogle Scholar
  • 13 Morgenstern LB, Zahuranec DB, Sánchez BN. et al. Full medical support for intracerebral hemorrhage. Neurology 2015; 84 (17) 1739-1744
    CrossrefPubMedGoogle Scholar
  • 14 Iyer VN, Mandrekar JN, Danielson RD, Zubkov AY, Elmer JL, Wijdicks EFM. Validity of the FOUR score coma scale in the medical intensive care unit. Mayo Clin Proc 2009; 84 (08) 694-701
    CrossrefPubMedGoogle Scholar
  • 15 Claassen J, Doyle K, Matory A. et al. Detection of brain activation in unresponsive patients with acute brain injury. N Engl J Med 2019; 380 (26) 2497-2505
    CrossrefPubMedGoogle Scholar
  • 16 Kondziella D, Friberg CK, Frokjaer VG, Fabricius M, Møller K. Preserved consciousness in vegetative and minimal conscious states: systematic review and meta-analysis. J Neurol Neurosurg Psychiatry 2016; 87 (05) 485-492
    CrossrefPubMedGoogle Scholar
  • 17 Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet 1974; 2 (7872) 81-84
    CrossrefPubMedGoogle Scholar
  • 18 Perel P, Arango M, Clayton T. et al; MRC CRASH Trial Collaborators. Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ 2008; 336 (7641) 425-429
    CrossrefPubMedGoogle Scholar
  • 19 Witsch J, Frey HP, Patel S. et al. Prognostication of long-term outcomes after subarachnoid hemorrhage: the FRESH score. Ann Neurol 2016; 80 (01) 46-58
    CrossrefPubMedGoogle Scholar
  • 20 Hemphill III JC, Bonovich DC, Besmertis L, Manley GT, Johnston SC. The ICH score: a simple, reliable grading scale for intracerebral hemorrhage. Stroke 2001; 32 (04) 891-897
    CrossrefPubMedGoogle Scholar
  • 21 Reith FCM, Van den Brande R, Synnot A, Gruen R, Maas AIR. The reliability of the Glasgow Coma Scale: a systematic review. Intensive Care Med 2016; 42 (01) 3-15
    CrossrefPubMedGoogle Scholar
  • 22 Leitgeb J, Mauritz W, Brazinova A. et al. Glasgow Coma Scale score at intensive care unit discharge predicts the 1-year outcome of patients with severe traumatic brain injury. Eur J Trauma Emerg Surg 2013; 39 (03) 285-292
    CrossrefPubMedGoogle Scholar
  • 23 Marmarou A, Lu J, Butcher I. et al. Prognostic value of the Glasgow Coma Scale and pupil reactivity in traumatic brain injury assessed pre-hospital and on enrollment: an IMPACT analysis. J Neurotrauma 2007; 24 (02) 270-280
    CrossrefPubMedGoogle Scholar
  • 24 Wijdicks EFM, Bamlet WR, Maramattom BV, Manno EM, McClelland RL. Validation of a new coma scale: the FOUR score. Ann Neurol 2005; 58 (04) 585-593
    CrossrefPubMedGoogle Scholar
  • 25 Foo CC, Loan JJM, Brennan PM. The relationship of the FOUR Score to patient outcome: a systematic review. J Neurotrauma 2019; 36 (17) 2469-2483
    CrossrefPubMedGoogle Scholar
  • 26 Mozaffarian D, Benjamin EJ, Go AS. et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics–2015 update: a report from the American Heart Association. Circulation 2015; 131 (04) e29-e322
    PubMedGoogle Scholar
  • 27 Adrie C, Cariou A, Mourvillier B. et al. Predicting survival with good neurological recovery at hospital admission after successful resuscitation of out-of-hospital cardiac arrest: the OHCA score. Eur Heart J 2006; 27 (23) 2840-2845
    CrossrefPubMedGoogle Scholar
  • 28 Choi JY, Jang JH, Lim YS. et al. Performance on the APACHE II, SAPS II, SOFA and the OHCA score of post-cardiac arrest patients treated with therapeutic hypothermia. PLoS One 2018; 13 (05) e0196197
    CrossrefPubMedGoogle Scholar
  • 29 Maupain C, Bougouin W, Lamhaut L. et al. The CAHP (Cardiac Arrest Hospital Prognosis) score: a tool for risk stratification after out-of-hospital cardiac arrest. Eur Heart J 2016; 37 (42) 3222-3228
    CrossrefPubMedGoogle Scholar
  • 30 Kim HS, Park KN, Kim SH. et al. Prognostic value of OHCA, C-GRApH and CAHP scores with initial neurologic examinations to predict neurologic outcomes in cardiac arrest patients treated with targeted temperature management. PLoS One 2020; 15 (04) e0232227
    CrossrefPubMedGoogle Scholar
  • 31 Kiehl EL, Parker AM, Matar RM. et al. C-GRApH: a validated scoring system for early stratification of neurologic outcome after out-of-hospital cardiac arrest treated with targeted temperature management. J Am Heart Assoc 2017; 6 (05) e003821
    CrossrefPubMedGoogle Scholar
  • 32 Heo WY, Jung YH, Lee HY. et al. External validation of cardiac arrest-specific prognostication scores developed for early prognosis estimation after out-of-hospital cardiac arrest in a Korean multicenter cohort. PLoS One 2022; 17 (04) e0265275
    CrossrefPubMedGoogle Scholar
  • 33 Martinell L, Nielsen N, Herlitz J. et al. Early predictors of poor outcome after out-of-hospital cardiac arrest. Crit Care 2017; 21 (01) 96
    CrossrefPubMedGoogle Scholar
  • 34 Kägi E, Weck A, Iten M, Levis A, Haenggi M. Value of the TTM risk score for early prognostication of comatose patients after out-of-hospital cardiac arrest in a Swiss University Hospital. Swiss Med Wkly 2020; 150: w20344
    CrossrefPubMedGoogle Scholar
  • 35 Rost NS, Smith EE, Chang Y. et al. Prediction of functional outcome in patients with primary intracerebral hemorrhage: the FUNC score. Stroke 2008; 39 (08) 2304-2309
    CrossrefPubMedGoogle Scholar
  • 36 Kasner SE. Clinical interpretation and use of stroke scales. Lancet Neurol 2006; 5 (07) 603-612
    CrossrefPubMedGoogle Scholar
  • 37 Smith WS. Safety of mechanical thrombectomy and intravenous tissue plasminogen activator in acute ischemic stroke. Results of the multi Mechanical Embolus Removal in Cerebral Ischemia (MERCI) trial, part I. AJNR Am J Neuroradiol 2006; 27 (06) 1177-1182
    PubMedGoogle Scholar
  • 38 Smith WS, Sung G, Starkman S. et al; MERCI Trial Investigators. Safety and efficacy of mechanical embolectomy in acute ischemic stroke: results of the MERCI trial. Stroke 2005; 36 (07) 1432-1438
    CrossrefPubMedGoogle Scholar
  • 39 Smith WS, Sung G, Saver J. et al; Multi MERCI Investigators. Mechanical thrombectomy for acute ischemic stroke: final results of the multi MERCI trial. Stroke 2008; 39 (04) 1205-1212
    CrossrefPubMedGoogle Scholar
  • 40 Flint AC, Cullen SP, Faigeles BS, Rao VA. Predicting long-term outcome after endovascular stroke treatment: the totaled health risks in vascular events score. AJNR Am J Neuroradiol 2010; 31 (07) 1192-1196
    CrossrefPubMedGoogle Scholar
  • 41 Flint AC, Faigeles BS, Cullen SP. et al; VISTA Collaboration. THRIVE score predicts ischemic stroke outcomes and thrombolytic hemorrhage risk in VISTA. Stroke 2013; 44 (12) 3365-3369
    CrossrefPubMedGoogle Scholar
  • 42 Flint AC, Rao VA, Chan SL. et al; SITS International and VISTA-plus investigators. Improved ischemic stroke outcome prediction using model estimation of outcome probability: the THRIVE-c calculation. Int J Stroke 2015; 10 (06) 815-821
    CrossrefPubMedGoogle Scholar
  • 43 Ntaios G, Faouzi M, Ferrari J, Lang W, Vemmos K, Michel P. An integer-based score to predict functional outcome in acute ischemic stroke: the ASTRAL score. Neurology 2012; 78 (24) 1916-1922
    CrossrefPubMedGoogle Scholar
  • 44 Heo J, Yoon JG, Park H, Kim YD, Nam HS, Heo JH. Machine learning-based model for prediction of outcomes in acute stroke. Stroke 2019; 50 (05) 1263-1265
    CrossrefPubMedGoogle Scholar
  • 45 Liu G, Ntaios G, Zheng H. et al. External validation of the ASTRAL score to predict 3- and 12-month functional outcome in the China National Stroke Registry. Stroke 2013; 44 (05) 1443-1445
    CrossrefPubMedGoogle Scholar
  • 46 Papavasileiou V, Milionis H, Michel P. et al. ASTRAL score predicts 5-year dependence and mortality in acute ischemic stroke. Stroke 2013; 44 (06) 1616-1620
    CrossrefPubMedGoogle Scholar
  • 47 Shen B, Yang X, Sui RB, Yang B. The prognostic value of the THRIVE Score, the iScore Score and the ASTRAL Score in Chinese patients with acute ischemic stroke. J Stroke Cerebrovasc Dis 2018; 27 (10) 2877-2886
    CrossrefPubMedGoogle Scholar
  • 48 Saposnik G, Kapral MK, Liu Y. et al; Investigators of the Registry of the Canadian Stroke Network, Stroke Outcomes Research Canada (SORCan) Working Group. IScore: a risk score to predict death early after hospitalization for an acute ischemic stroke. Circulation 2011; 123 (07) 739-749
    CrossrefPubMedGoogle Scholar
  • 49 Saposnik G, Raptis S, Kapral MK. et al; Investigators of the Registry of the Canadian Stroke Network and the Stroke Outcome Research Canada Working Group. The iScore predicts poor functional outcomes early after hospitalization for an acute ischemic stroke. Stroke 2011; 42 (12) 3421-3428
    CrossrefPubMedGoogle Scholar
  • 50 Saposnik G, Fang J, Kapral MK. et al; Investigators of the Registry of the Canadian Stroke Network (RCSN), Stroke Outcomes Research Canada (SORCan) Working Group. The iScore predicts effectiveness of thrombolytic therapy for acute ischemic stroke. Stroke 2012; 43 (05) 1315-1322
    CrossrefPubMedGoogle Scholar
  • 51 Hallevi H, Barreto AD, Liebeskind DS. et al; UCLA Intra-Arterial Therapy Investigators. Identifying patients at high risk for poor outcome after intra-arterial therapy for acute ischemic stroke. Stroke 2009; 40 (05) 1780-1785
    CrossrefPubMedGoogle Scholar
  • 52 Sarraj A, Albright K, Barreto AD. et al. Optimizing prediction scores for poor outcome after intra-arterial therapy in anterior circulation acute ischemic stroke. Stroke 2013; 44 (12) 3324-3330
    CrossrefPubMedGoogle Scholar
  • 53 Feigin VL, Lawes CMM, Bennett DA, Barker-Collo SL, Parag V. Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review. Lancet Neurol 2009; 8 (04) 355-369
    CrossrefPubMedGoogle Scholar
  • 54 Greenberg SM, Ziai WC, Cordonnier C. et al; American Heart Association/American Stroke Association. 2022 guideline for the management of patients with spontaneous intracerebral hemorrhage: a guideline from the American Heart Association/American Stroke Association. Stroke 2022; 53 (07) e282-e361
    CrossrefPubMedGoogle Scholar
  • 55 Clarke JL, Johnston SC, Farrant M, Bernstein R, Tong D, Hemphill III JC. External validation of the ICH score. Neurocrit Care 2004; 1 (01) 53-60
    CrossrefPubMedGoogle Scholar
  • 56 Jamora RDG, Kishi-Generao Jr EM, Bitanga ES, Gan RN, Apaga NEP, San Jose MC. The ICH score: predicting mortality and functional outcome in an Asian population. Stroke 2003; 34 (01) 6-7 , author reply 6–7
    CrossrefPubMedGoogle Scholar
  • 57 Patriota GC, Silva-Júnior JM, Barcellos ACES. et al. Determining ICH Score: can we go beyond?. Arq Neuropsiquiatr 2009; 67 (3A): 605-608
    CrossrefPubMedGoogle Scholar
  • 58 Hemphill III JC, Farrant M, Neill Jr TA. Prospective validation of the ICH Score for 12-month functional outcome. Neurology 2009; 73 (14) 1088-1094
    CrossrefPubMedGoogle Scholar
  • 59 Sembill JA, Gerner ST, Volbers B. et al. Severity assessment in maximally treated ICH patients: the max-ICH score. Neurology 2017; 89 (05) 423-431
    CrossrefPubMedGoogle Scholar
  • 60 Garrett JS, Zarghouni M, Layton KF, Graybeal D, Daoud YA. Validation of clinical prediction scores in patients with primary intracerebral hemorrhage. Neurocrit Care 2013; 19 (03) 329-335
    CrossrefPubMedGoogle Scholar
  • 61 Weimar C, Benemann J, Diener HC. German Stroke Study Collaboration. Development and validation of the Essen Intracerebral Haemorrhage Score. J Neurol Neurosurg Psychiatry 2006; 77 (05) 601-605
    CrossrefPubMedGoogle Scholar
  • 62 Sembill JA, Castello JP, Sprügel MI. et al. Multicenter validation of the max-ICH Score in intracerebral hemorrhage. Ann Neurol 2021; 89 (03) 474-484
    CrossrefPubMedGoogle Scholar
  • 63 Al-Khindi T, Macdonald RL, Schweizer TA. Cognitive and functional outcome after aneurysmal subarachnoid hemorrhage. Stroke 2010; 41 (08) e519-e536
    CrossrefPubMedGoogle Scholar
  • 64 Lu H, Xue G, Li S. et al. An accurate prognostic prediction for aneurysmal subarachnoid hemorrhage dedicated to patients after endovascular treatment. Ther Adv Neurol Disord 2022; 15: 17 562864221099473
    CrossrefPubMedGoogle Scholar
  • 65 Report of World Federation of Neurological Surgeons Committee on a Universal Subarachnoid Hemorrhage Grading Scale. J Neurosurg 1988; 68 (06) 985-986
    PubMedGoogle Scholar
  • 66 Fung C, Inglin F, Murek M. et al. Reconsidering the logic of World Federation of Neurosurgical Societies grading in patients with severe subarachnoid hemorrhage. J Neurosurg 2016; 124 (02) 299-304
    CrossrefPubMedGoogle Scholar
  • 67 Hoogmoed J, Coert BA, van den Berg R. et al. Early treatment decisions in poor-grade patients with subarachnoid hemorrhage. World Neurosurg 2018; 119: e568-e573
    CrossrefPubMedGoogle Scholar
  • 68 Fisher CM, Kistler JP, Davis JM. Relation of cerebral vasospasm to subarachnoid hemorrhage visualized by computerized tomographic scanning. Neurosurgery 1980; 6 (01) 1-9
    CrossrefPubMedGoogle Scholar
  • 69 Claassen J, Bernardini GL, Kreiter K. et al. Effect of cisternal and ventricular blood on risk of delayed cerebral ischemia after subarachnoid hemorrhage: the Fisher scale revisited. Stroke 2001; 32 (09) 2012-2020
    CrossrefPubMedGoogle Scholar
  • 70 Pegoli M, Mandrekar J, Rabinstein AA, Lanzino G. Predictors of excellent functional outcome in aneurysmal subarachnoid hemorrhage. J Neurosurg 2015; 122 (02) 414-418
    CrossrefPubMedGoogle Scholar
  • 71 Macdonald RL, Kassell NF, Mayer S. et al; CONSCIOUS-1 Investigators. Clazosentan to overcome neurological ischemia and infarction occurring after subarachnoid hemorrhage (CONSCIOUS-1): randomized, double-blind, placebo-controlled phase 2 dose-finding trial. Stroke 2008; 39 (11) 3015-3021
    CrossrefPubMedGoogle Scholar
  • 72 Witsch J, Kuohn L, Hebert R. et al. Early prognostication of 1-year outcome after subarachnoid hemorrhage: the FRESH Score validation. J Stroke Cerebrovasc Dis 2019; 28 (10) 104280
    CrossrefPubMedGoogle Scholar
  • 73 Jaja BNR, Saposnik G, Lingsma HF. et al; SAHIT collaboration. Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage: the SAHIT multinational cohort study. BMJ 2018; 360: j5745
    CrossrefPubMedGoogle Scholar
  • 74 Mascitelli JR, Cole T, Yoon S. et al. External validation of the Subarachnoid Hemorrhage International Trialists (SAHIT) predictive model using the Barrow Ruptured Aneurysm Trial (BRAT) cohort. Neurosurgery 2020; 86 (01) 101-106
    CrossrefPubMedGoogle Scholar
  • 75 GBD 2016 Traumatic Brain Injury and Spinal Cord Injury Collaborators. Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 2019; 18 (01) 56-87
    CrossrefPubMedGoogle Scholar
  • 76 Johnson WD, Griswold DP. Traumatic brain injury: a global challenge. Lancet Neurol 2017; 16 (12) 949-950
    CrossrefPubMedGoogle Scholar
  • 77 Schneider ALC, Huie JR, Boscardin WJ. et al; TRACK-TBI Investigators. Cognitive outcome 1 year after mild traumatic brain injury: results from the TRACK-TBI study. Neurology 2022; 98 (12) e1248-e1261
    CrossrefPubMedGoogle Scholar
  • 78 Madhok DY, Rodriguez RM, Barber J. et al; TRACK-TBI Investigators. Outcomes in patients with mild traumatic brain injury without acute intracranial traumatic injury. JAMA Netw Open 2022; 5 (08) e2223245
    CrossrefPubMedGoogle Scholar
  • 79 Brennan PM, Murray GD, Teasdale GM. Simplifying the use of prognostic information in traumatic brain injury. Part 1: the GCS-Pupils score: an extended index of clinical severity. J Neurosurg 2018; 128 (06) 1612-1620
    CrossrefPubMedGoogle Scholar
  • 80 Steyerberg EW, Mushkudiani N, Perel P. et al. Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med 2008; 5 (08) e165
    CrossrefPubMedGoogle Scholar
  • 81 Roozenbeek B, Lingsma HF, Lecky FE. et al; International Mission on Prognosis Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) Study Group, Corticosteroid Randomisation After Significant Head Injury (CRASH) Trial Collaborators, Trauma Audit and Research Network (TARN). Prediction of outcome after moderate and severe traumatic brain injury: external validation of the International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) prognostic models. Crit Care Med 2012; 40 (05) 1609-1617
    CrossrefPubMedGoogle Scholar
  • 82 Panczykowski DM, Puccio AM, Scruggs BJ. et al. Prospective independent validation of IMPACT modeling as a prognostic tool in severe traumatic brain injury. J Neurotrauma 2012; 29 (01) 47-52
    CrossrefPubMedGoogle Scholar
  • 83 Hukkelhoven CWPM, Steyerberg EW, Habbema JDF. et al. Predicting outcome after traumatic brain injury: development and validation of a prognostic score based on admission characteristics. J Neurotrauma 2005; 22 (10) 1025-1039
    CrossrefPubMedGoogle Scholar
  • 84 Han J, King NKK, Neilson SJ, Gandhi MP, Ng I. External validation of the CRASH and IMPACT prognostic models in severe traumatic brain injury. J Neurotrauma 2014; 31 (13) 1146-1152
    CrossrefPubMedGoogle Scholar
  • 85 Harrison DA, Griggs KA, Prabhu G. et al; Risk Adjustment In Neurocritical care (RAIN) Study Investigators. External validation and recalibration of risk prediction models for acute traumatic brain injury among critically ill adult patients in the United Kingdom. J Neurotrauma 2015; 32 (19) 1522-1537
    CrossrefPubMedGoogle Scholar
  • 86 Kremers F, Venema E, Duvekot M. et al; MR CLEAN Registry Investigators. Outcome prediction models for endovascular treatment of ischemic stroke: systematic review and external validation. Stroke 2022; 53 (03) 825-836
    CrossrefPubMedGoogle Scholar
  • 87 Hunt WE, Hess RM. Surgical risk as related to time of intervention in the repair of intracranial aneurysms. J Neurosurg 1968; 28 (01) 14-20
    CrossrefPubMedGoogle Scholar