Skip to main content

Outcome Prediction and Shared Decision-Making in Neurocritical Care

  • Chapter
  • First Online:
Neurointensive Care Unit

Part of the book series: Current Clinical Neurology ((CCNEU))

  • 1333 Accesses

Abstract

The neurointensive care physician is frequently called upon to lead in the determination of neurological prognosis and in the decision-making that follows. This is a complex task that must integrate objective clinical information and test results together with published evidence and professional guidelines or recommendations when available. A number of scoring systems have been developed on the basis of multivariable models that typically integrate clinical severity indicators as well as features extracted from neuroimaging and neurophysiological testing and, in some cases, serum biomarkers. Performance of these models, evaluated with indices of discrimination and calibration, is often insufficient to enable prediction at the individual patient level—perhaps due to underpowered samples, lack of external validation, and failure to consider treatments as predictive features. The aim of effectively delivering prognostic information may fail because of the lack of consistency and coordination in communication between treatment teams and families and the unqualified use of population-based evidence to formulate point estimates of individual outcome. To help overcome this barrier, a model of “shared decision-making” has been proposed in which providers, patients, and surrogate decision-makers work collaboratively to make medical decisions that consider the best scientific evidence while integrating the patient’s values, goals, and preferences.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Stevens RD, Sutter R. Prognosis in severe brain injury. Crit Care Med. 2013;41(4):1104–23.

    Article  PubMed  Google Scholar 

  2. Geurts M, Macleod MR, van Thiel GJ, et al. End-of-life decisions in patients with severe acute brain injury. Lancet Neurol. 2014;13(5):515–24.

    Article  PubMed  Google Scholar 

  3. Stevens RD, Hart N, Herridge MS. Textbook of post-ICU medicine: the legacy of critical care. 1st ed. Oxford University Press, New York, 2014.

    Google Scholar 

  4. Sandroni C, Geocadin RG. Neurological prognostication after cardiac arrest. Curr Opin Crit Care. 2015;21(3):209–14.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Morgenstern LB, Zahuranec DB, Sanchez BN, et al. Full medical support for intracerebral hemorrhage. Neurology. 2015;84(17):1739–44.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Turgeon AF, Lauzier F, Simard JF, et al. Mortality associated with withdrawal of life-sustaining therapy for patients with severe traumatic brain injury: a Canadian multicentre cohort study. CMAJ. 2011;183(14):1581–8.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kirkman MA, Jenks T, Bouamra O, et al. Increased mortality associated with cerebral contusions following trauma in the elderly: bad patients or bad management? J Neurotrauma. 2013;30(16):1385–90.

    Article  PubMed  Google Scholar 

  8. Weimer JM, Nowacki AS, Frontera JA. Withdrawal of life-sustaining therapy in patients with intracranial hemorrhage: self-fulfilling prophecy or accurate prediction of outcome? Crit Care Med. 2016;44(6):1161–72.

    Article  PubMed  Google Scholar 

  9. Jain A, Jain M, Bellolio MF, et al. Is early DNR a self-fulfilling prophecy for patients with spontaneous intracerebral hemorrhage? Neurocrit Care. 2013;19(3):342–6.

    Article  CAS  PubMed  Google Scholar 

  10. McCracken DJ, Lovasik BP, McCracken CE, et al. The intracerebral hemorrhage score: a self-fulfilling prophecy? Neurosurgery. 2019;84(3):741–8.

    Article  PubMed  Google Scholar 

  11. Becker KJ, Baxter AB, Cohen WA, et al. Withdrawal of support in intracerebral hemorrhage may lead to self-fulfilling prophecies. Neurology. 2001;56(6):766–72.

    Article  CAS  PubMed  Google Scholar 

  12. Mayer SA, Kossoff SB. Withdrawal of life support in the neurological intensive care unit. Neurology. 1999;52(8):1602–9.

    Article  CAS  PubMed  Google Scholar 

  13. Hemphill JC 3rd, Bonovich DC, Besmertis L, et al. The ICH score: a simple, reliable grading scale for intracerebral hemorrhage. Stroke. 2001;32(4):891–7.

    Article  PubMed  Google Scholar 

  14. Hwang DY, Dell CA, Sparks MJ, et al. Clinician judgment vs formal scales for predicting intracerebral hemorrhage outcomes. Neurology. 2016;86(2):126–33.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. JAMA. 2017;318(14):1377–84.

    Article  PubMed  Google Scholar 

  16. Hanczar B, Hua J, Sima C, et al. Small-sample precision of ROC-related estimates. Bioinformatics. 2010;26(6):822–30.

    Article  CAS  PubMed  Google Scholar 

  17. Hosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression. In: Wiley series in probability and statistics. 3rd ed. Chicester: Wiley; 2013. p. 1 online resource (767 p.).

    Google Scholar 

  18. Laleva M, Gabrovsky N, Naseva E, et al. Delayed intraventricular hemorrhage in moderate-to-severe traumatic brain injury: prevalence, associated risk factors, and prognosis. Acta Neurochir. 2016;158(8):1465–72.

    Article  PubMed  Google Scholar 

  19. Leitgeb J, Mauritz W, Brazinova A, et al. Outcome after severe brain trauma due to acute subdural hematoma. J Neurosurg. 2012;117(2):324–33.

    Article  PubMed  Google Scholar 

  20. Leitgeb J, Mauritz W, Brazinova A, et al. Outcome after severe brain trauma associated with epidural hematoma. Arch Orthop Trauma Surg. 2013;133(2):199–207.

    Article  PubMed  Google Scholar 

  21. Spaite DW, Hu C, Bobrow BJ, et al. Mortality and prehospital blood pressure in patients with major traumatic brain injury: implications for the hypotension threshold. JAMA Surg. 2017;152(4):360–8.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Manley G, Knudson MM, Morabito D, et al. Hypotension, hypoxia, and head injury: frequency, duration, and consequences. JAMA Surg. 2001;136(10):1118–23.

    CAS  Google Scholar 

  23. Roozenbeek B, Lingsma HF, Lecky FE, et al. 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(5):1609–17.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Collaborators MCT, Perel P, Arango M, et al. Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ. 2008;336(7641):425–9.

    Article  Google Scholar 

  25. 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(8):e165; discussion e165.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Raj R, Skrifvars M, Bendel S, et al. Predicting six-month mortality of patients with traumatic brain injury: usefulness of common intensive care severity scores. Crit Care. 2014;18(2):R60.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Raj R, Siironen J, Kivisaari R, et al. Predicting outcome after traumatic brain injury: development of prognostic scores based on the IMPACT and the APACHE II. J Neurotrauma. 2014;31(20):1721–32.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Myat A, Song KJ, Rea T. Out-of-hospital cardiac arrest: current concepts. Lancet (London, England). 2018;391(10124):970–9.

    Article  Google Scholar 

  29. Martinell L, Nielsen N, Herlitz J, et al. Early predictors of poor outcome after out-of-hospital cardiac arrest. Crit Care. 2017;21(1):96.

    Article  PubMed  PubMed Central  Google Scholar 

  30. 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):2840–5.

    Article  PubMed  Google Scholar 

  31. Skrifvars MB, Varghese B, Parr MJ. Survival and outcome prediction using the Apache III and the out-of-hospital cardiac arrest (OHCA) score in patients treated in the intensive care unit (ICU) following out-of-hospital, in-hospital or ICU cardiac arrest. Resuscitation. 2012;83(6):728–33.

    Article  CAS  PubMed  Google Scholar 

  32. Bisbal M, Jouve E, Papazian L, et al. Effectiveness of SAPS III to predict hospital mortality for post-cardiac arrest patients. Resuscitation. 2014;85(7):939–44.

    Article  PubMed  Google Scholar 

  33. Luescher T, Mueller J, Isenschmid C, et al. Neuron-specific enolase (NSE) improves clinical risk scores for prediction of neurological outcome and death in cardiac arrest patients: results from a prospective trial. Resuscitation. 2019;142:50–60.

    Article  PubMed  Google Scholar 

  34. 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(5):e0196197.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. 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):3222–8.

    Article  PubMed  Google Scholar 

  36. Wang CH, Huang CH, Chang WT, et al. Prognostic performance of simplified out-of-hospital cardiac arrest (OHCA) and cardiac arrest hospital prognosis (CAHP) scores in an East Asian population: a prospective cohort study. Resuscitation. 2019;137:133–9.

    Article  PubMed  Google Scholar 

  37. Rossetti AO, Oddo M, Logroscino G, et al. Prognostication after cardiac arrest and hypothermia: a prospective study. Ann Neurol. 2010;67(3):301–7.

    PubMed  Google Scholar 

  38. Velly L, Perlbarg V, Boulier T, et al. Use of brain diffusion tensor imaging for the prediction of long-term neurological outcomes in patients after cardiac arrest: a multicentre, international, prospective, observational, cohort study. Lancet Neurol. 2018;17(4):317–26.

    Article  PubMed  Google Scholar 

  39. Weimar C, Konig IR, Kraywinkel K, et al. Age and National Institutes of Health Stroke Scale Score within 6 hours after onset are accurate predictors of outcome after cerebral ischemia: development and external validation of prognostic models. Stroke. 2004;35(1):158–62.

    Article  CAS  PubMed  Google Scholar 

  40. Adams HP Jr, Davis PH, Leira EC, et al. Baseline NIH Stroke Scale score strongly predicts outcome after stroke: a report of the Trial of Org 10172 in Acute Stroke Treatment (TOAST). Neurology. 1999;53(1):126–31.

    Article  CAS  PubMed  Google Scholar 

  41. Jorgensen HS, Nakayama H, Raaschou HO, et al. Stroke. Neurologic and functional recovery the Copenhagen Stroke Study. Phys Med Rehabil Clin N Am. 1999;10(4):887–906.

    Article  CAS  PubMed  Google Scholar 

  42. Saposnik G, Kapral MK, Liu Y, et al. IScore: a risk score to predict death early after hospitalization for an acute ischemic stroke. Circulation. 2011;123(7):739–49.

    Article  PubMed  Google Scholar 

  43. Koennecke HC, Belz W, Berfelde D, et al. Factors influencing in-hospital mortality and morbidity in patients treated on a stroke unit. Neurology. 2011;77(10):965–72.

    Article  PubMed  Google Scholar 

  44. Hankey GJ, Spiesser J, Hakimi Z, et al. Rate, degree, and predictors of recovery from disability following ischemic stroke. Neurology. 2007;68(19):1583–7.

    Article  CAS  PubMed  Google Scholar 

  45. Andersen KK, Andersen ZJ, Olsen TS. Predictors of early and late case-fatality in a nationwide Danish study of 26,818 patients with first-ever ischemic stroke. Stroke. 2011;42(10):2806–12.

    Article  PubMed  Google Scholar 

  46. Saver JL, Altman H. Relationship between neurologic deficit severity and final functional outcome shifts and strengthens during first hours after onset. Stroke. 2012;43(6):1537–41.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Konig IR, Ziegler A, Bluhmki E, et al. Predicting long-term outcome after acute ischemic stroke: a simple index works in patients from controlled clinical trials. Stroke. 2008;39(6):1821–6.

    Article  PubMed  Google Scholar 

  48. Baird AE, Dambrosia J, Janket S, et al. A three-item scale for the early prediction of stroke recovery. Lancet (London, England). 2001;357(9274):2095–9.

    Article  CAS  Google Scholar 

  49. Steiner T, Mendoza G, De Georgia M, et al. Prognosis of stroke patients requiring mechanical ventilation in a neurological critical care unit. Stroke. 1997;28(4):711–5.

    Article  CAS  PubMed  Google Scholar 

  50. Brott T, Adams HP Jr, Olinger CP, et al. Measurements of acute cerebral infarction: a clinical examination scale. Stroke. 1989;20(7):864–70.

    Article  CAS  PubMed  Google Scholar 

  51. Vogt G, Laage R, Shuaib A, et al. Initial lesion volume is an independent predictor of clinical stroke outcome at day 90: an analysis of the Virtual International Stroke Trials Archive (VISTA) database. Stroke. 2012;43(5):1266–72.

    Article  PubMed  Google Scholar 

  52. O’Donnell MJ, Fang J, D’Uva C, et al. The PLAN score: a bedside prediction rule for death and severe disability following acute ischemic stroke. Arch Intern Med. 2012;172(20):1548–56.

    Article  PubMed  Google Scholar 

  53. Yahalom G, Schwartz R, Schwammenthal Y, et al. Chronic kidney disease and clinical outcome in patients with acute stroke. Stroke. 2009;40(4):1296–303.

    Article  PubMed  Google Scholar 

  54. Smith EE, Shobha N, Dai D, et al. Risk score for in-hospital ischemic stroke mortality derived and validated within the Get With the Guidelines-Stroke Program. Circulation. 2010;122(15):1496–504.

    Article  PubMed  Google Scholar 

  55. Xu J, Tao Y, Xie X, et al. A comparison of mortality prognostic scores in ischemic stroke patients. J Stroke Cerebrovasc Dis: Off J Natl Stroke Assoc. 2016;25(2):241–7.

    Article  Google Scholar 

  56. Rangaraju S, Liggins JT, Aghaebrahim A, et al. Pittsburgh outcomes after stroke thrombectomy score predicts outcomes after endovascular therapy for anterior circulation large vessel occlusions. Stroke. 2014;45(8):2298–304.

    Article  PubMed  Google Scholar 

  57. Weimar C, Benemann J, Diener HC, et al. Development and validation of the Essen Intracerebral Haemorrhage Score. J Neurol Neurosurg Psychiatry. 2006;77(5):601–5.

    Article  CAS  PubMed  Google Scholar 

  58. Zis P, Leivadeas P, Michas D, et al. Predicting 30-day case fatality of primary inoperable intracerebral hemorrhage based on findings at the emergency department. J Stroke Cerebrovasc Dis: Off J Natl Stroke Assoc. 2014;23(7):1928–33.

    Article  Google Scholar 

  59. Rost NS, Smith EE, Chang Y, et al. Prediction of functional outcome in patients with primary intracerebral hemorrhage: the FUNC score. Stroke. 2008;39(8):2304–9.

    Article  PubMed  Google Scholar 

  60. Cho D-Y, Chen C-C, Lee W-Y, et al. A new modified intracerebral hemorrhage score for treatment decisions in basal ganglia hemorrhage—a randomized. Trial. 2008;36(7):2151–6.

    Google Scholar 

  61. Ruiz-Sandoval JL, Chiquete E, Romero-Vargas S, et al. Grading scale for prediction of outcome in primary intracerebral hemorrhages. Stroke. 2007;38(5):1641–4.

    Article  PubMed  Google Scholar 

  62. Sembill JA, Gerner ST, Volbers B, et al. Severity assessment in maximally treated ICH patients: the max-ICH score. Neurology. 2017;89(5):423–31.

    Article  PubMed  Google Scholar 

  63. Qureshi AI, Palesch YY, Barsan WG, et al. Intensive blood-pressure lowering in patients with acute cerebral hemorrhage. N Engl J Med. 2016;375(11):1033–43.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Anderson CS, Heeley E, Huang Y, et al. Rapid blood-pressure lowering in patients with acute intracerebral hemorrhage. N Engl J Med. 2013;368(25):2355–65.

    Article  CAS  PubMed  Google Scholar 

  65. Steiner T, Weitz JI, Veltkamp RJS. Anticoagulant-associated intracranial hemorrhage in the era of reversal agents. Stroke. 2017;48(5):1432–7.

    Article  PubMed  Google Scholar 

  66. Clarke JL, Johnston SC, Farrant M, et al. External validation of the ICH score. Neurocrit Care. 2004;1(1):53–60.

    Article  PubMed  Google Scholar 

  67. Schmidt FA, Liotta EM, Prabhakaran S, et al. Assessment and comparison of the max-ICH score and ICH score by external validation. Neurology. 2018;91(10):e939–46.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Hemphill JC 3rd, Farrant M, Neill TA Jr. Prospective validation of the ICH Score for 12-month functional outcome. Neurology. 2009;73(14):1088–94.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Parry-Jones Adrian R, Abid Kamran A, Di Napoli M, et al. Accuracy and clinical usefulness of intracerebral hemorrhage grading scores. Stroke. 2013;44(7):1840–5.

    Article  CAS  PubMed  Google Scholar 

  70. Molyneux AJ, Kerr RS, Yu LM, et al. International subarachnoid aneurysm trial (ISAT) of neurosurgical clipping versus endovascular coiling in 2143 patients with ruptured intracranial aneurysms: a randomised comparison of effects on survival, dependency, seizures, rebleeding, subgroups, and aneurysm occlusion. Lancet (London, England). 2005;366(9488):809–17.

    Article  Google Scholar 

  71. Wartenberg KE, Schmidt JM, Claassen J, et al. Impact of medical complications on outcome after subarachnoid hemorrhage. Crit Care Med. 2006;34(3):617–23; quiz 624.

    Article  PubMed  Google Scholar 

  72. Frontera JA, Fernandez A, Schmidt JM, et al. Impact of nosocomial infectious complications after subarachnoid hemorrhage. Neurosurgery. 2008;62(1):80–7; discussion 87.

    Article  PubMed  Google Scholar 

  73. Zacharia BE, Ducruet AF, Hickman ZL, et al. Renal dysfunction as an independent predictor of outcome after aneurysmal subarachnoid hemorrhage: a single-center cohort study. Stroke. 2009;40(7):2375–81.

    Article  PubMed  Google Scholar 

  74. Todd MM, Hindman BJ, Clarke WR, et al. Perioperative fever and outcome in surgical patients with aneurysmal subarachnoid hemorrhage. Neurosurgery. 2009;64(5):897–908; discussion 908.

    Article  PubMed  Google Scholar 

  75. Langham J, Reeves BC, Lindsay KW, et al. Variation in outcome after subarachnoid hemorrhage: a study of neurosurgical units in UK and Ireland. Stroke. 2009;40(1):111–8.

    Article  PubMed  Google Scholar 

  76. O’Kelly CJ, Kulkarni AV, Austin PC, et al. The impact of therapeutic modality on outcomes following repair of ruptured intracranial aneurysms: an administrative data analysis. Clinical article. J Neurosurg. 2010;113(4):795–801.

    Article  PubMed  Google Scholar 

  77. Lindsay KW, Teasdale GM, Knill-Jones RP. Observer variability in assessing the clinical features of subarachnoid hemorrhage. J Neurosurg. 1983;58(1):57.

    Article  CAS  PubMed  Google Scholar 

  78. Degen LA, Dorhout Mees SM, Algra A, et al. Interobserver variability of grading scales for aneurysmal subarachnoid hemorrhage. Stroke. 2011;42(6):1546–9.

    Article  PubMed  Google Scholar 

  79. Naval NS, Kowalski RG, Chang TR, et al. The SAH Score: a comprehensive communication tool. J Stroke Cerebrovasc Dis: Off J Natl Stroke Assoc. 2014;23(5):902–9.

    Article  Google Scholar 

  80. van Donkelaar CE, Bakker NA, Veeger NJ, et al. Prediction of outcome after subarachnoid hemorrhage: timing of clinical assessment. J Neurosurg. 2017;126(1):52–9.

    Article  PubMed  Google Scholar 

  81. Giraldo EA, Mandrekar JN, Rubin MN, et al. Timing of clinical grade assessment and poor outcome in patients with aneurysmal subarachnoid hemorrhage. J Neurosurg. 2012;117(1):15–9.

    Article  PubMed  Google Scholar 

  82. Jaja BNR, Saposnik G, Lingsma HF, et al. Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage: the SAHIT multinational cohort study. BMJ. 2018;360:j5745.

    Article  PubMed  Google Scholar 

  83. Zhao B, Yang H, Zheng K, et al. Preoperative and postoperative predictors of long-term outcome after endovascular treatment of poor-grade aneurysmal subarachnoid hemorrhage. J Neurosurg. 2017;126(6):1764–71.

    Article  PubMed  Google Scholar 

  84. Zhao B, Rabinstein A, Murad MH, et al. Surgical and endovascular treatment of poor-grade aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis. J Neurosurg Sci. 2017;61(4):403–15.

    PubMed  Google Scholar 

  85. Evans LR, Boyd EA, Malvar G, et al. Surrogate decision-makers’ perspectives on discussing prognosis in the face of uncertainty. Am J Respir Crit Care Med. 2009;179(1):48–53.

    Article  PubMed  Google Scholar 

  86. Turgeon AF, Lauzier F, Burns KE, et al. Determination of neurologic prognosis and clinical decision making in adult patients with severe traumatic brain injury: a survey of Canadian intensivists, neurosurgeons, and neurologists. Crit Care Med. 2013;41(4):1086–93.

    Article  PubMed  Google Scholar 

  87. Izzy S, Compton R, Carandang R, et al. Self-fulfilling prophecies through withdrawal of care: do they exist in traumatic brain injury, too? Neurocrit Care. 2013;19(3):347–63.

    Article  PubMed  Google Scholar 

  88. Curtis JR, Tonelli MR. Shared decision-making in the ICU: value, challenges, and limitations. Am J Respir Crit Care Med. 2011;183(7):840–1.

    Article  PubMed  Google Scholar 

  89. Khan MW, Muehlschlegel S. Shared decision making in neurocritical care. Neurosurg Clin N Am. 2018;29(2):315–21.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Souter MJ, Blissitt PA, Blosser S, et al. Recommendations for the critical care management of devastating brain injury: prognostication, psychosocial, and ethical management : a position statement for healthcare professionals from the neurocritical care society. Neurocrit Care. 2015;23(1):4–13.

    Article  PubMed  Google Scholar 

  91. Guiza F, Depreitere B, Piper I, et al. Novel methods to predict increased intracranial pressure during intensive care and long-term neurologic outcome after traumatic brain injury: development and validation in a multicenter dataset. Crit Care Med. 2013;41(2):554–64.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Robert D. Stevens .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sharrock, M.F., Stevens, R.D. (2020). Outcome Prediction and Shared Decision-Making in Neurocritical Care. In: Nelson, S., Nyquist, P. (eds) Neurointensive Care Unit. Current Clinical Neurology. Humana, Cham. https://doi.org/10.1007/978-3-030-36548-6_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36548-6_21

  • Published:

  • Publisher Name: Humana, Cham

  • Print ISBN: 978-3-030-36547-9

  • Online ISBN: 978-3-030-36548-6

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics