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Part II - Other Topics in Neuroprognostication

Published online by Cambridge University Press:  14 November 2024

David M. Greer
Affiliation:
Boston University School of Medicine and Boston Medical Center
Neha S. Dangayach
Affiliation:
Icahn School of Medicine at Mount Sinai and Mount Sinai Health System
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Publisher: Cambridge University Press
Print publication year: 2024

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References

References

Diringer, MN, Edwards, DF. Admission to a neurologic/neurosurgical intensive care unit is associated with reduced mortality rate after intracerebral hemorrhage. Crit Care Med. 2001;29:635–40.CrossRefGoogle ScholarPubMed
Mayer, SA, Kossoff, SB. Withdrawal of life support in the neurological intensive care unit. Neurology. 1999;52:1602–9.CrossRefGoogle ScholarPubMed
Rabinstein, AA. Ethical dilemmas in the neurologic ICU: withdrawing life-support measures after devastating brain injury. Continuum Lifelong Learning Neurol. 2009;15:1325.CrossRefGoogle Scholar
De Georgia, MA. History of brain death as death: 1968 to the present. J Crit Care. 2014;29:673–8.CrossRefGoogle ScholarPubMed
Luce, JM, White, DB. A history of ethics and law in the intensive care unit. Crit Care Clin. 2009;25:221–37.CrossRefGoogle Scholar
[no authors listed] A definition of irreversible coma: report of the Ad Hoc Committee of the Harvard Medical School to examine the definition of brain death. JAMA. 1968;205:337340.Google Scholar
In Re Quinlan, 70 N.J. 10, 355 A.2d 647. 1976.CrossRefGoogle Scholar
Falck, DP. In re Quinlan: one court’s answer to the problem of death with dignity. Wash Lee Law Rev. 1977;34:285308.Google Scholar
Siegler, M, Taylor, RM. Intimacy and caring: the legacy of Karen Ann Quinlan. Trends Health Care Law Ethics. 1993;8:2830, 38.Google ScholarPubMed
President’s Commission for the Study of Ethical Problems in Medicine and Biomedical and Behavioral Research. Deciding to Forego Life-Sustaining Treatment. A report on the Ethical, Medical, and Legal Issues in Treatment decisions. Washington, DC: US Government Printing Office, 1983.Google Scholar
US Supreme Court. Cruzan v. Director, Missouri Department of Health. Wests Supreme Court Report. 1990;110:2841–92.Google Scholar
Annas, GJ. Nancy Cruzan and the right to die. N Engl J Med. 1990;323:670–3.CrossRefGoogle ScholarPubMed
Schor, NF. Comment: autonomy vs beneficence. Neurology. 2014;83:1370.CrossRefGoogle ScholarPubMed
Truog, RD, Campbell, M, Curtis, Jr., et al. Recommendations for end-of-life care in the intensive care unit: a consensus statement by the American Academy of Critical Care Medicine. Crit Care Med. 2008;36:953.CrossRefGoogle ScholarPubMed
Manno, EM, Wijdicks, EF. The declaration of death and the withdrawal of care in the neurologic patient. Neurol Clin. 2006;24:159–69.CrossRefGoogle ScholarPubMed
Morgan, CK, Varas, GM, Pedroza, C, Almoosa, KF. Defining the practice of “no escalation of care” in the ICU. Crit Care Med. 2014;42:357–61.CrossRefGoogle Scholar
Thompson, DR. “No escalation of treatment” as a routine strategy for decision-making in the ICU: pro. Intensive Care Med. 2014;40:1372–3.CrossRefGoogle ScholarPubMed
Curtis, JR, Rubenfeld, GD. “No escalation of treatment” as a routine strategy for decision-making in the ICU: con. Intensive Care Med. 2014;40:1374–6.CrossRefGoogle ScholarPubMed
Frontera, JA, Curtis, JR, Nelson, JE, et al. Integrating palliative care into the care of neurocritically ill patients: a report from the improving palliative care in the ICU project advisory board and the center to advance palliative care. Crit Care Med. 2015;43:1964–77.CrossRefGoogle ScholarPubMed
Murray, SA, Kendall, M, Boyd, K, Sheikh, A. Illness trajectories and palliative care. BMJ. 2005;330:1007–11.Google ScholarPubMed
Diringer, MN, Edwards, DF, Aiyagari, V, Hollingsworth, H. Factors associated with withdrawal of mechanical ventilation in a neurology/neurosurgery intensive care unit. Crit Care Med. 2001;29:1792–7.CrossRefGoogle Scholar
Prendergast, TJ, Luce, JM. Increasing incidence of withholding and withdrawal of life support from the critically ill. Am J Respir Crit Care Med. 1997;155:1520.CrossRefGoogle ScholarPubMed
Souter, MJ, Blissitt, PA, Blosser, S, et al. Recommendations for the critical care management of devastating brain injury: prognostication, psychosocial, and ethical management. Neurocritical Care. 2015;23:413.CrossRefGoogle ScholarPubMed
Hemphill, JC 3rd, Greenberg, SM, Anderson, CS, et al. Guidelines for the management of spontaneous intracerebral hemorrhage: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2015;46:2032–60.CrossRefGoogle ScholarPubMed
Tran, LN, Back, AL, Creutzfeldt, CJ. Palliative care consultations in the neuro-ICU: a qualitative study. Neurocrit Care. 2016;25:266–72.CrossRefGoogle ScholarPubMed
Yoong, J, Park, ER, Greer, JA, et al. Early palliative care in advanced lung cancer: a qualitative study. JAMA Intern Med. 2013;173:283–20.CrossRefGoogle ScholarPubMed
Jacobsen, J, Jackson, V, Dahlin, C, et al. Components of early outpatient palliative care consultation in patients with metastatic nonsmall cell lung cancer. J Palliat Med. 2011;14:459–64.CrossRefGoogle ScholarPubMed
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;41:1086–93.CrossRefGoogle ScholarPubMed
Sprung, CL, Cohen, SL, Sjokvist, P, et al. End-of-life practices in European intensive care units: the Ethicus study. JAMA. 2003;290:790–7.CrossRefGoogle ScholarPubMed
Sprung, CL, Maia, P, Bulow, HH, et al. The importance of religious affiliation and culture on end-of-life decisions in European intensive care units. Intensive Care Med. 2007;33:1732–9.CrossRefGoogle ScholarPubMed
White, DB, Evans, LR, Bautista, CA, Luce, JM, Lo, B. Are physicians’ recommendations to limit life support beneficial or burdensome? Bringing empirical data to the debate. Am J Respir Crit Care Med. 2009;180:320–5.CrossRefGoogle ScholarPubMed
Charles, C, Whelan, T, Gafni, A. What do we mean by partnership in making decisions about treatment? BMJ. 1999;319:780–2.CrossRefGoogle ScholarPubMed
Quinn, T, Moskowitz, J, Khan, MW, et al. What families need and physicians deliver: contrasting communication preferences between surrogate decision-makers and physicians during outcome prognostication in critically ill TBI patients. Neurocrit Care. 2017;27:154–62.CrossRefGoogle ScholarPubMed
Mahoney, FI, Barthel, DW. Functional evaluation: the Barthel Index. Md State Med J. 1965;14:61–5.Google ScholarPubMed
Sulter, G, Steen, C, De Keyser, J. Use of the Barthel Index and modified Rankin Scale in acute stroke trials. Stroke. 1999;30:1538–41.CrossRefGoogle ScholarPubMed
Granger, CV, Dewis, LS, Peters, NC, Sherwood, CC, Barrett, JE. Stroke rehabilitation: analysis of repeated Barthel Index measures. Arch Phys Med Rehabil. 1979;60:1417.Google ScholarPubMed
Rankin, J. Cerebral vascular accidents in patients over the age of 60. II. Prognosis. Scott Med J. 1957;2:200–15.Google Scholar
Farrell, B, Godwin, J, Richards, S, Warlow, C. The United Kingdom Transient Ischaemic Attack (UK-TIA) aspirin trial: final results. J Neurol Neurosurg Psychiatry. 1991;54:1044–54.CrossRefGoogle ScholarPubMed
van Swieten, JC, Koudstaal, PJ, Visser, MC, Schouten, HJ, van Gijn, J. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. 1988;19:604–7.CrossRefGoogle ScholarPubMed
Jennett, B, Bond, M. Assessment of outcome after severe brain damage. Lancet. 1975;1:480–4.Google ScholarPubMed
Jennett, B, Snoek, J, Bond, MR, Brooks, N. Disability after severe head injury: observations on the use of the Glasgow Outcome Scale. J Neurol Neurosurg Psychiatry. 1981;44:285–93.CrossRefGoogle ScholarPubMed
Weir, J, Steyerberg, EW, Butcher, I, et al. Does the extended Glasgow Outcome Scale add value to the conventional Glasgow Outcome Scale? J Neurotrauma. 2012;29:53–8.CrossRefGoogle Scholar
Okonkwo, DO, Shutter, LA, Moore, C, et al. Brain oxygen optimization in severe traumatic brain injury phase-II: a phase II randomized trial. Crit Care Med. 2017;45:1907–14.CrossRefGoogle ScholarPubMed
Brain Resuscitation Clinical Trial IISG. A randomized clinical study of a calcium-entry blocker (lidoflazine) in the treatment of comatose survivors of cardiac arrest. N Engl J Med. 1991;324:1225–31.Google Scholar
Ajam, K, Gold, LS, Beck, SS, et al. Reliability of the cerebral performance category to classify neurological status among survivors of ventricular fibrillation arrest: a cohort study. Scand J Trauma Resusc Emerg Med. 2011;19:38.CrossRefGoogle ScholarPubMed
Devlin, NJ, Brooks, R. EQ-5D and the EuroQol Group: past, present and future. Appl Health Econ Health Policy. 2017;15:127–37.CrossRefGoogle ScholarPubMed
Whynes, DK, Group, T. Correspondence between EQ-5D health state classifications and EQ VAS scores. Health Qual Life Outcomes. 2008;6:94.CrossRefGoogle ScholarPubMed
Rabin, R, de Charro, F. EQ-5D: a measure of health status from the EuroQol Group. Ann Med. 2001;33:337–43.CrossRefGoogle ScholarPubMed
Hodes, RJ, Insel, TR, Landis, SC, Research NIHBf, N. The NIH Toolbox: setting a standard for biomedical research. Neurology. 2013;80:S1.CrossRefGoogle ScholarPubMed
Gershon, RC, Cella, D, Fox, NA, et al. Assessment of neurological and behavioural function: the NIH Toolbox. Lancet Neurol. 2010;9:138–9.CrossRefGoogle ScholarPubMed
Cella, D, Lai, JS, Nowinski, CJ, et al. Neuro-QOL: brief measures of health-related quality of life for clinical research in neurology. Neurology. 2012;78:1860–7.CrossRefGoogle ScholarPubMed
Asch, DA, Hansen-Flaschen, J, Lanken, PN. Decisions to limit or continue life-sustaining treatment by critical care physicians in the United States: conflicts between physicians’ practices and patients’ wishes. Am J Respir Crit Care Med. 1995;151:288–92.CrossRefGoogle ScholarPubMed
Luce, JM, Alpers, A. End-of-life care: what do the American courts say? Crit Care Med. 2001;29:N40–5.CrossRefGoogle ScholarPubMed
Hanson, LC, Danis, M, Garrett, J. What is wrong with end-of-life care? Opinions of bereaved family members. J Am Geriatr Soc. 1997;45:1339–44.CrossRefGoogle ScholarPubMed
Johnson, SK, Bautista, CA, Hong, SY, Weissfeld, L, White, DB. An empirical study of surrogates’ preferred level of control over value-laden life support decisions in intensive care units. Am J Respir Crit Care Med. 2011;183:915–21.CrossRefGoogle ScholarPubMed
Azoulay, E, Pochard, F, Kentish-Barnes, N, et al. Risk of post-traumatic stress symptoms in family members of intensive care unit patients. Am J Respir Crit Care Med. 2005;171:987–94.CrossRefGoogle ScholarPubMed
Petrinec, AB, Daly, BJ. Post-traumatic stress symptoms in post-ICU family members: review and methodological challenges. West J Nurs Res. 2016;38:5778.CrossRefGoogle ScholarPubMed
Nunez, ER, Schenker, Y, Joel, ID, et al. Acutely bereaved surrogates’ stories about the decision to limit life support in the ICU. Crit Care Med. 2015;43:2387–93.CrossRefGoogle ScholarPubMed
Rocker, GM, Heyland, DK, Cook, DJ, et al. Most critically ill patients are perceived to die in comfort during withdrawal of life support: a Canadian multicentre study. Can J Anaesth. 2004;51:623–30.CrossRefGoogle ScholarPubMed
Keenan, SP, Mawdsley, C, Plotkin, D, Webster, GK, Priestap, F. Withdrawal of life support: how the family feels, and why. J Palliat Care. 2000;16 (Suppl):S40–4.CrossRefGoogle ScholarPubMed
Gries, CJ, Curtis, JR, Wall, RJ, Engelberg, RA. Family member satisfaction with end-of-life decision making in the ICU. Chest. 2008;133:704–12.CrossRefGoogle ScholarPubMed
Ahrens, T, Yancey, V, Kollef, M. Improving family communications at the end of life: implications for length of stay in the intensive care unit and resource use. Am J Crit Care. 2003;12:317–23; discussion 324.CrossRefGoogle ScholarPubMed
Khandelwal, N, Curtis, JR. Economic implications of end-of-life care in the ICU. Curr Opin Crit Care. 2014;20:656–61.CrossRefGoogle ScholarPubMed
Dewan, MC, Rattani, A, Gupta, S, et al. Estimating the global incidence of traumatic brain injury. J Neurosurg. 2018:130:1080–97.Google ScholarPubMed
van Erp, WS, Lavrijsen, JC, Vos, PE, et al. The vegetative state: prevalence, misdiagnosis, and treatment limitations. J Am Med Dir Assoc. 2015;16:e89–5 e14.CrossRefGoogle ScholarPubMed
Donis, J, Kraftner, B. The prevalence of patients in a vegetative state and minimally conscious state in nursing homes in Austria. Brain Inj. 2011;25:1101–17.CrossRefGoogle Scholar
Crispi, F, Crisci, C. Patients in persistent vegetative state … and what of their relatives? Nurs Ethics. 2000;7:533–5.CrossRefGoogle ScholarPubMed
Li, YH, Xu, ZP. Psychological crisis intervention for the family members of patients in a vegetative state. Clinics (Sao Paulo). 2012;67:341–5.CrossRefGoogle Scholar
Moretta, P, Estraneo, A, De Lucia, L, et al. A study of the psychological distress in family caregivers of patients with prolonged disorders of consciousness during in-hospital rehabilitation. Clin Rehabil. 2014;28:717–25.CrossRefGoogle ScholarPubMed
Chiambretto, P, Rossi Ferrario, S, Zotti, AM. Patients in a persistent vegetative state: caregiver attitudes and reactions. Acta Neurol Scand. 2001;104:364–8.CrossRefGoogle Scholar
Elvira de la Morena, MJ, Cruzado, JA. Caregivers of patients with disorders of consciousness: coping and prolonged grief. Acta Neurol Scand. 2013;127:413–18.Google ScholarPubMed
Goudarzi, F, Abedi, H, Zarea, K, Ahmadi, F. Multiple victims: the result of caring patients in vegetative state. Iran Red Crescent Med J. 2015;17:e23571.CrossRefGoogle ScholarPubMed
Hoofien, D, Gilboa, A, Vakil, E, Donovick, PJ. Traumatic brain injury (TBI) 10–20 years later: a comprehensive outcome study of psychiatric symptomatology, cognitive abilities and psychosocial functioning. Brain Inj. 2001;15:189209.Google Scholar
Humphreys, I, Wood, RL, Phillips, CJ, Macey, S. The costs of traumatic brain injury: a literature review. Clinicoecon Outcomes Res. 2013;5:281–27.Google ScholarPubMed
Thomsen, IV. Late psychosocial outcome in severe traumatic brain injury. Preliminary results of a third follow-up study after 20 years. Scand J Rehabil Med Suppl. 1992;26:142–52.Google ScholarPubMed
Coradazzi, A, Inhaia, C, Santana, M, et al. Palliative withdrawal ventilation: why, when and how to do it? HPMIJ. 2019;3.CrossRefGoogle Scholar
Rubenfeld, GD. Principles and practice of withdrawing life-sustaining treatments. Crit Care Clin. 2004;20:435–51, ix.CrossRefGoogle ScholarPubMed
Ropper, AH. Unusual spontaneous movements in brain-dead patients. Neurology. 1984;34:1089–92.CrossRefGoogle ScholarPubMed
Abbott, KH, Sago, JG, Breen, CM, Abernethy, AP, Tulsky, JA. Families looking back: one year after discussion of withdrawal or withholding of life-sustaining support. Crit Care Med. 2001;29:197201.CrossRefGoogle ScholarPubMed
National Institute of Neurological Disorders and Stroke rt-PA SSG. Tissue plasminogen activator for acute ischemic stroke. N Engl J Med. 1995;333:1581–7.Google Scholar
Hacke, W, Kaste, M, Fieschi, C, et al. Randomised double-blind placebo-controlled trial of thrombolytic therapy with intravenous alteplase in acute ischaemic stroke (ECASS II). Second European-Australasian acute Stroke Study Investigators. Lancet. 1998;352:1245–51.CrossRefGoogle ScholarPubMed
Nogueira, RG, Jadhav, AP, Haussen, DC, et al. Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med. 2018;378:1121.CrossRefGoogle ScholarPubMed
Albers, GW, Marks, MP, Kemp, S, et al. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med. 2018;378:708–18.CrossRefGoogle ScholarPubMed
Juttler, E, Schwab, S, Schmiedek, P, et al. Decompressive surgery for the treatment of malignant infarction of the middle cerebral artery (DESTINY): a randomized, controlled trial. Stroke. 2007;38:2518–25.CrossRefGoogle ScholarPubMed
Vahedi, K, Vicaut, E, Mateo, J, et al. Sequential-design, multicenter, randomized, controlled trial of early decompressive craniectomy in malignant middle cerebral artery infarction (DECIMAL trial). Stroke. 2007;38:2506–17.CrossRefGoogle ScholarPubMed
Hofmeijer, J, Kappelle, LJ, Algra, A, et al. Surgical decompression for space-occupying cerebral infarction (the hemicraniectomy after middle cerebral artery infarction with life-threatening edema trial [HAMLET]): A multicentre, open, randomised trial. Lancet Neurol. 2009;8:326–33.CrossRefGoogle Scholar
Anderson, CS, Heeley, E, Huang, Y, et al. Rapid blood-pressure lowering in patients with acute intracerebral hemorrhage. N Engl J Med. 2013;368:2355–65.CrossRefGoogle ScholarPubMed
Qureshi, AI, Palesch, YY, Barsan, WG, et al. Intensive blood-pressure lowering in patients with acute cerebral hemorrhage. N Engl J Med. 2016;375:1033–43.CrossRefGoogle ScholarPubMed
Mendelow, AD, Gregson, BA, Rowan, EN, et al. Early surgery versus initial conservative treatment in patients with spontaneous supratentorial lobar intracerebral haematomas (STICH II): A randomised trial. Lancet. 2013;382:397408.CrossRefGoogle Scholar
Ziai, WC, Tuhrim, S, Lane, K, et al. A multicenter, randomized, double-blinded, placebo-controlled phase III study of clot lysis evaluation of accelerated resolution of intraventricular hemorrhage (CLEAR III). Int J Stroke. 2014;9:536–42.CrossRefGoogle ScholarPubMed
Hanley, DF, Thompson, RE, Rosenblum, M, et al. Efficacy and safety of minimally invasive surgery with thrombolysis in intracerebral haemorrhage evacuation (MISTIE III): a randomised, controlled, open-label, blinded endpoint phase 3 trial. Lancet. 2019;393:1021–32.CrossRefGoogle Scholar
Bernard, SA, Gray, TW, Buist, MD, et al. Treatment of comatose survivors of out-of-hospital cardiac arrest with induced hypothermia. N Engl J Med. 2002;346:557–63.CrossRefGoogle ScholarPubMed
Nielsen, N, Wetterslev, J, Cronberg, T, et al. Targeted temperature management at 33 degrees c versus 36 degrees c after cardiac arrest. N Engl J Med. 2013;369:21972206.CrossRefGoogle Scholar
Cooper, DJ, Rosenfeld, JV, Murray, L, et al. Decompressive craniectomy in diffuse traumatic brain injury. N Engl J Med. 2011;364:14931502.CrossRefGoogle ScholarPubMed
Edwards, P, Arango, M, Balica, L, et al. Final results of MRC CRASH, a randomised placebo-controlled trial of intravenous corticosteroid in adults with head injury-outcomes at 6 months. Lancet. 2005;365:1957–9.Google ScholarPubMed

References

Nelson, J.E., Cox, C.E., Hope, A.A., Carson, S.S. Chronic critical illness. Am J Respir Crit Care Med 2010;182(4):446–54.CrossRefGoogle ScholarPubMed
Sjoding, M.W., Cooke, C.R. Chronic critical illness: a growing legacy of successful advances in critical care. Crit Care Med 2015; 43(2):476–7.CrossRefGoogle ScholarPubMed
Kahn, J.M., Werner, R.M., David, G., et al. Effectiveness of long-term acute care hospitalization in elderly patients with chronic critical illness. Med Care 2013;51(1):410.CrossRefGoogle ScholarPubMed
Carson, S.S., Bach, P.B. The epidemiology and costs of chronic critical illness. Crit Care Clin 2002;18(3):461–76.CrossRefGoogle ScholarPubMed
Kahn, J.M., Le, T., Angus, D.C., et al. The epidemiology of chronic critical illness in the United States. Crit Care Med 2015;43(2):282–7.CrossRefGoogle ScholarPubMed
Cox, C.E. Persistent systemic inflammation in chronic critical illness. Respir Care 2012;57(6):859–64; discussion 64–6.CrossRefGoogle ScholarPubMed
Iwashyna, T.J., Hodgson, C.L., Pilcher, D., Bailey, M., Bellomo, R. Persistent critical illness characterised by Australian and New Zealand ICU clinicians. Crit Care Resusc 2015;17(3):153–8.Google ScholarPubMed
Iwashyna, T.J., Hodgson, C.L., Pilcher, D., et al. Towards defining persistent critical illness and other varieties of chronic critical illness. Crit Care Resusc 2015;17(3):215–18.Google ScholarPubMed
Kandilov, A., Ingber, I.M., Morley, M, et al. Chronically critically ill population payment recommendations (CCIP-PR): final report. RTI Project No. 0212355.000.010. RTI International. March 2014.Google Scholar
Hough, C.L., Caldwell, E.S., Cox, C.E., et al. Development and validation of a mortality prediction model for patients receiving 14 days of mechanical ventilation. Crit Care Med 2015;43(11):2339–45.CrossRefGoogle ScholarPubMed
Chelluri, L., Mendelsohn, A.B., Belle, S.H., et al. Hospital costs in patients receiving prolonged mechanical ventilation: does age have an impact? Crit Care Med 2003;31(6):1746–51.CrossRefGoogle ScholarPubMed
Kahn, J.M. Improving outcomes in prolonged mechanical ventilation: a road map. Lancet Respir Med 2015;3(7):501–2.CrossRefGoogle ScholarPubMed
Damuth, E., Mitchell, J.A., Bartock, J.L., Roberts, B.W., Trzeciak, S. Long-term survival of critically ill patients treated with prolonged mechanical ventilation: a systematic review and meta-analysis. Lancet Respir Med 2015;3(7):544–53.CrossRefGoogle ScholarPubMed
Hughes, M., MacKirdy, F.N., Norrie, J. Grant, I.S. Outcome of long-stay intensive care patients. Intensive Care Med 2001;27(4):779–82.CrossRefGoogle ScholarPubMed
Kramer, A.A., Zimmerman, J.E. A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay. BMC Med Inform Decis Mak 2010;10:27.CrossRefGoogle ScholarPubMed
Arabi, Y., Venkatesh, S., Haddad, S., Al Shimemeri, A. Al Malik, S. A prospective study of prolonged stay in the intensive care unit: predictors and impact on resource utilization. Int J Qual Health Care 2002;14(5):403–10.CrossRefGoogle ScholarPubMed
Girard, K., Raffin, T.A. The chronically critically ill: to save or let die? Respir Care 1985;30(5):339–47.Google ScholarPubMed
Iwashyna, T.J., Hodgson, C.L., Pilcher, D., et al. Timing of onset and burden of persistent critical illness in Australia and New Zealand: a retrospective, population-based, observational study. Lancet Respir Med 2016;4(7):566–73.CrossRefGoogle ScholarPubMed
Cox, C.E., Carson, S.S., Holmes, G.M., Howard, A. Carey, T.S. Increase in tracheostomy for prolonged mechanical ventilation in North Carolina, 1993–2002. Crit Care Med 2004;32(11):2219–26.CrossRefGoogle ScholarPubMed
Scheinhorn, D.J., Hassenpflug, M.S., Votto, J.J., et al. Ventilator-dependent survivors of catastrophic illness transferred to 23 long-term care hospitals for weaning from prolonged mechanical ventilation. Chest 2007;131(1):7684.CrossRefGoogle ScholarPubMed
Hollander, J.M., Mechanick, J.I. Nutrition support and the chronic critical illness syndrome. Nutr Clin Pract 2006;21(6):587604.CrossRefGoogle ScholarPubMed
Nelson, J.E., Tandon, N., Mercado, A.F., et al. Brain dysfunction: another burden for the chronically critically ill. Arch Intern Med 2006;166(18):1993–9.CrossRefGoogle ScholarPubMed
Van den Berghe, G., de Zegher, F., Veldhuis, J.D., et al. The somatotropic axis in critical illness: effect of continuous growth hormone (GH)-releasing hormone and GH-releasing peptide-2 infusion. J Clin Endocrinol Metab 1997;82(2):590–9.Google ScholarPubMed
Van den Berghe, G., de Zegher, F., Veldhuis, J.D., et al. Thyrotrophin and prolactin release in prolonged critical illness: dynamics of spontaneous secretion and effects of growth hormone-secretagogues. Clin Endocrinol (Oxf) 1997;47(5):599612.CrossRefGoogle ScholarPubMed
Scheinhorn, D.J., Hassenpflug, M.S., Votto, J.J., et al. Post-ICU mechanical ventilation at 23 long-term care hospitals: a multicenter outcomes study. Chest 2007;131(1):8593.CrossRefGoogle ScholarPubMed
Carasa, M., Polycarpe, M. Caring for the chronically critically ill patient: establishing a wound- healing program in a respiratory care unit. Am J Surg 2004;188(1A Suppl):1821.CrossRefGoogle Scholar
Carson, S.S., Kahn, J.M., Hough, C.L., et al. A multicenter mortality prediction model for patients receiving prolonged mechanical ventilation. Crit Care Med 2012;40(4):1171–6.CrossRefGoogle ScholarPubMed
Carson, S.S., Garrett, J., Hanson, L.C., et al. A prognostic model for one-year mortality in patients requiring prolonged mechanical ventilation. Crit Care Med 2008;36(7):2061–9.CrossRefGoogle ScholarPubMed
Su, Y.Y., Li, X., Li, S.J., et al. Predicting hospital mortality using APACHE II scores in neurocritically ill patients: a prospective study. J Neurol 2009;256(9):1427–33.CrossRefGoogle ScholarPubMed
Su, Y., Wang, M., Liu, Y., et al. Module modified acute physiology and chronic health evaluation II: predicting the mortality of neuro-critical disease. Neurol Res 2014;36(12):1099–105.CrossRefGoogle ScholarPubMed
Navarrete-Navarro, P., Rivera-Fernandez, R., Lopez-Mutuberria, M.T., et al. Outcome prediction in terms of functional disability and mortality at 1 year among ICU-admitted severe stroke patients: a prospective epidemiological study in the south of the European Union (Evascan Project, Andalusia, Spain). Intensive Care Med 2003;29(8):1237–44.CrossRefGoogle ScholarPubMed
Huang, K.B., Ji, Z., Wu, Y.M., et al. The prediction of 30-day mortality in patients with primary pontine hemorrhage: a scoring system comparison. Eur J Neurol 2012;19(9):1245–50.CrossRefGoogle ScholarPubMed
Tsai, C.L., Chu, H., Peng, G.S., et al. Preoperative APACHE II and GCS scores as predictors of outcomes in patients with malignant MCA infarction after decompressive hemicraniectomy. Neurol India 2012;60(6):608–12.Google ScholarPubMed
Szklener, S., Melges, A., Korchut, A., et al. Predictive model for patients with poor-grade subarachnoid haemorrhage in 30-day observation: a 9-year cohort study. BMJ Open 2015;5(6):e007795.CrossRefGoogle ScholarPubMed
Claassen, J., Bernardini, G.L., 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(9):2012–20.CrossRefGoogle Scholar
Lantigua, H., Ortega-Gutierrez, S., Schmidt, J.M., et al. Subarachnoid hemorrhage: who dies, and why? Crit Care 2015;19:309.CrossRefGoogle ScholarPubMed
Teasdale, G.M., Drake, C.G., Hunt, W., et al. A universal subarachnoid hemorrhage scale: report of a committee of the World Federation of Neurosurgical Societies. J Neurol Neurosurg Psychiatry 1988;51(11):1457.CrossRefGoogle ScholarPubMed
Schuiling, W.J., Dennesen, P.J., Rinkel, G.J. Extracerebral organ dysfunction in the acute stage after aneurysmal subarachnoid hemorrhage. Neurocrit Care 2005;3(1):110.CrossRefGoogle ScholarPubMed
Gruber, A., Reinprecht, A., Gorzer, H., et al. Pulmonary function and radiographic abnormalities related to neurological outcome after aneurysmal subarachnoid hemorrhage. J Neurosurg 1998;88(1):2837.CrossRefGoogle ScholarPubMed
Lee, V.H., Oh, J.K., Mulvagh, S.L., Wijdicks, E.F. Mechanisms in neurogenic stress cardiomyopathy after aneurysmal subarachnoid hemorrhage. Neurocrit Care 2006;5(3):243–9.CrossRefGoogle ScholarPubMed
Claassen, J., Vu, A., Kreiter, K.T., et al. Effect of acute physiologic derangements on outcome after subarachnoid hemorrhage. Crit Care Med 2004;32(3):832–8.CrossRefGoogle ScholarPubMed
Park, S.K., Chun, H.J., Kim, D.W., et al. Acute Physiology and Chronic Health Evaluation II and Simplified Acute Physiology Score II in predicting hospital mortality of neurosurgical intensive care unit patients. J Korean Med Sci 2009;24(3):420–6.CrossRefGoogle Scholar
Gao, Q., Yuan, F., Yang, X.A., et al. Development and validation of a new score for predicting functional outcome of neurocritically ill patients: the INCNS score. CNS Neurosci Ther 2020;26(1):21–9.CrossRefGoogle Scholar
Balestreri, M., Czosnyka, M., Chatfield, D.A., et al. Predictive value of Glasgow Coma Scale after brain trauma: change in trend over the past ten years. J Neurol Neurosurg Psychiatry 2004;75(1):161–2.Google ScholarPubMed
Marshall, L.F., Gautille, T., Klauber, M.R., et al.: The outcome of severe closed head injury. J Neurosurg (Suppl) 75:2836, 1991.CrossRefGoogle Scholar
Tsao, J.W., Hemphill, J.C., 3rd, Johnston, S.C., Smith, W.S., Bonovich, D.C. Initial Glasgow Coma Scale score predicts outcome following thrombolysis for posterior circulation stroke. Arch Neurol 2005;62(7):1126–9.CrossRefGoogle ScholarPubMed
Hemphill, J.C., 3rd, Bonovich, D.C., Besmertis, L., Manley, G.T., Johnston, S.C. The ICH score: a simple, reliable grading scale for intracerebral hemorrhage. Stroke 2001;32(4):891–7.CrossRefGoogle Scholar
Lahiri, S., Mayer, S.A., Fink, M.E., et al. Mechanical ventilation for acute stroke: a multi-state population-based study. Neurocrit Care 2015;23(1):2832.CrossRefGoogle ScholarPubMed
Roch, A., Michelet, P., Jullien, A.C., et al. Long-term outcome in intensive care unit survivors after mechanical ventilation for intracerebral hemorrhage. Crit Care Med 2003;31(11):2651–6.CrossRefGoogle ScholarPubMed
Lerolle, N., Trinquart, L., Bornstain, C., et al. Increased intensity of treatment and decreased mortality in elderly patients in an intensive care unit over a decade. Crit Care Med 2010;38(1):5964.CrossRefGoogle Scholar
Heyland, D.K., Garland, A., Bagshaw, S.M., et al. Recovery after critical illness in patients aged 80 years or older: a multi-center prospective observational cohort study. Intensive Care Med 2015;41(11):1911–20.CrossRefGoogle ScholarPubMed
Rockwood, K., Song, X., MacKnight, C., et al. A global clinical measure of fitness and frailty in elderly people. CMAJ 2005;173(5):489–95.CrossRefGoogle ScholarPubMed
Rajabali, N., Rolfson, D., Bagshaw, S.M. Assessment and utility of frailty measures in critical illness, cardiology, and cardiac surgery. Can J Cardiol 2016;32(9):1157–65.CrossRefGoogle ScholarPubMed
Evans, S.J., Sayers, M., Mitnitski, A., Rockwood, K. The risk of adverse outcomes in hospitalized older patients in relation to a frailty index based on a comprehensive geriatric assessment. Age Ageing 2014;43(1):127–32.CrossRefGoogle ScholarPubMed
Bagshaw, M., Majumdar, S.R., Rolfson, D.B., et al. A prospective multicenter cohort study of frailty in younger critically ill patients. Crit Care 2016;20(1):175.CrossRefGoogle ScholarPubMed
Muscedere, J., Waters, B., Varambally, A., et al. The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis. Intensive Care Med 2017;43(8):1105–22.CrossRefGoogle ScholarPubMed
Joseph, B., Pandit, V., Zangbar, B., et al. Superiority of frailty over age in predicting outcomes among geriatric trauma patients: a prospective analysis. JAMA Surg 2014;149(8):766–72.CrossRefGoogle ScholarPubMed
Robinson, T.N., Eiseman, B., Wallace, J.I., et al. Redefining geriatric preoperative assessment using frailty, disability and co-morbidity. Ann Surg 2009;250(3):449–55.CrossRefGoogle ScholarPubMed
Leng, S., Chaves, P., Koenig, K., Walston, J. Serum interleukin-6 and hemoglobin as physiological correlates in the geriatric syndrome of frailty: a pilot study. J Am Geriatr Soc 2002;50(7):1268–71.CrossRefGoogle ScholarPubMed
Chen, X., Mao, G., Leng, S.X. Frailty syndrome: an overview. Clin Interv Aging 2014;9:433–41.Google ScholarPubMed
Hubbard, R.E., O’Mahony, M.S., Savva, G.M., Calver, B.L., Woodhouse, K.W. Inflammation and frailty measures in older people. J Cell Mol Med 2009;13(9B):3103–9.CrossRefGoogle ScholarPubMed
Collerton, J., Martin-Ruiz, C., Davies, K., et al. Frailty and the role of inflammation, immunosenescence and cellular ageing in the very old: cross-sectional findings from the Newcastle 85+ Study. Mech Ageing Dev 2012;133(6):456–66.CrossRefGoogle Scholar
Leng, S.X., Tian, X., Matteini, A., et al. IL-6-independent association of elevated serum neopterin levels with prevalent frailty in community-dwelling older adults. Age Ageing 2011;40(4):475–81.CrossRefGoogle ScholarPubMed
De Fanis, U., Wang, G.C., Fedarko, N.S., et al. T-lymphocytes expressing CC chemokine receptor-5 are increased in frail older adults. J Am Geriatr Soc 2008;56(5):904–8.CrossRefGoogle ScholarPubMed
Qu, T., Yang, H., Walston, J.D., Fedarko, N.S., Leng, S.X. Upregulated monocytic expression of CXC chemokine ligand 10 (CXCL-10) and its relationship with serum interleukin-6 levels in the syndrome of frailty. Cytokine 2009;46(3):319–24.CrossRefGoogle ScholarPubMed
Schmaltz, H.N., Fried, L.P., Xue, Q.L., et al. Chronic cytomegalovirus infection and inflammation are associated with prevalent frailty in community-dwelling older women. J Am Geriatr Soc 2005;53(5):747–54.CrossRefGoogle ScholarPubMed
Jeejeebhoy, K.N. Malnutrition, fatigue, frailty, vulnerability, sarcopenia and cachexia: overlap of clinical features. Curr Opin Clin Nutr Metab Care 2012;15(3):213–19.CrossRefGoogle ScholarPubMed
Baldwin, M.R., Reid, M.C., Westlake, A.A., et al. The feasibility of measuring frailty to predict disability and mortality in older medical intensive care unit survivors. J Crit Care 2014;29(3):401–8.CrossRefGoogle ScholarPubMed
Fried, L.P., Tangen, C.M., Walston, J., et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56(3):M146–56.CrossRefGoogle ScholarPubMed
Mitnitski, A.B., Graham, J.E., Mogilner, A.J., Rockwood, K. Frailty, fitness and late-life mortality in relation to chronological and biological age. BMC Geriatr 2002;2:1.CrossRefGoogle ScholarPubMed
Rockwood, K., Andrew, M., Mitnitski, A. A comparison of two approaches to measuring frailty in elderly people. J Gerontol A Biol Sci Med Sci 2007;62(7):738–43.CrossRefGoogle ScholarPubMed
Vina, J., Tarazona-Santabalbina, F.J., Perez-Ros, P., et al. Biology of frailty: modulation of ageing genes and its importance to prevent age-associated loss of function. Mol Aspects Med 2016;50:88108.CrossRefGoogle ScholarPubMed
Leng, S.X., Cappola, A.R., Andersen, R.E., et al. Serum levels of insulin-like growth factor-I (IGF-I) and dehydroepiandrosterone sulfate (DHEA-S), and their relationships with serum interleukin-6, in the geriatric syndrome of frailty. Aging Clin Exp Res 2004;16(2):153–7.CrossRefGoogle ScholarPubMed
Puts, M.T., Visser, M., Twisk, J.W., Deeg, D.J., Lips, P. Endocrine and inflammatory markers as predictors of frailty. Clin Endocrinol (Oxf) 2005;63(4):403–11.CrossRefGoogle ScholarPubMed
Cawthon, P.M., Ensrud, K.E., Laughlin, G.A., et al. Sex hormones and frailty in older men: the osteoporotic fractures in men (MrOS) study. J Clin Endocrinol Metab 2009;94(10):3806–15.CrossRefGoogle ScholarPubMed
Joseph, C., Kenny, A.M., Taxel, P., et al. Role of endocrine-immune dysregulation in osteoporosis, sarcopenia, frailty and fracture risk. Mol Aspects Med 2005;26(3):181201.CrossRefGoogle ScholarPubMed
Travison, T.G., Nguyen, A.H., Naganathan, V., et al. Changes in reproductive hormone concentrations predict the prevalence and progression of the frailty syndrome in older men: the concord health and ageing in men project. J Clin Endocrinol Metab 2011;96(8):2464–74.CrossRefGoogle ScholarPubMed
Cesari, M., Penninx, B.W., Pahor, M., et al. Inflammatory markers and physical performance in older persons: the InCHIANTI study. J Gerontol A Biol Sci Med Sci 2004;59(3):242–8.CrossRefGoogle ScholarPubMed
Barzilay, J.I., Blaum, C., Moore, T., et al. Insulin resistance and inflammation as precursors of frailty: the Cardiovascular Health Study. Arch Intern Med 2007;167(7):635–41.CrossRefGoogle ScholarPubMed
Calvani, R., Marini, F., Cesari, M., et al. Biomarkers for physical frailty and sarcopenia: state of the science and future developments. J Cachexia Sarcopenia Muscle 2015;6(4):278–86.CrossRefGoogle ScholarPubMed
Brummel, N.E., Bell, S.P., Girard, T.D., et al. Frailty and subsequent disability and mortality among patients with critical illness. Am J Respir Crit Care Med 2017;196(1):6472.CrossRefGoogle ScholarPubMed
Jackson, J.C., Pandharipande, P.P., Girard, T.D., et al. Depression, post-traumatic stress disorder, and functional disability in survivors of critical illness in the BRAIN-ICU study: a longitudinal cohort study. Lancet Respir Med 2014;2(5):369–79.CrossRefGoogle ScholarPubMed
Bagshaw, S.M., Stelfox, H.T., McDermid, R.C., et al. Association between frailty and short- and long-term outcomes among critically ill patients: a multicentre prospective cohort study. CMAJ 2014;186(2):E95102.CrossRefGoogle Scholar
Bagshaw, S.M., Stelfox, H.T., Johnson, J.A., et al. Long-term association between frailty and health-related quality of life among survivors of critical illness: a prospective multicenter cohort study. Crit Care Med 2015;43(5):973–82.CrossRefGoogle ScholarPubMed
Le Maguet, P., Roquilly, A., Lasocki, S., et al. Prevalence and impact of frailty on mortality in elderly ICU patients: a prospective, multicenter, observational study. Intensive Care Med 2014;40(5):674–82.Google ScholarPubMed
Flaatten, H., De Lange, D.W., Morandi, A., et al. The impact of frailty on ICU and 30-day mortality and the level of care in very elderly patients (>/= 80 years). Intensive Care Med 2017;43(12):1820–8.CrossRefGoogle ScholarPubMed
Kizilarslanoglu, M.C., Civelek, R., Kilic, M.K., et al. Is frailty a prognostic factor for critically ill elderly patients? Aging Clin Exp Res 2017;29(2):247–55.CrossRefGoogle ScholarPubMed
Hope, A.A., Gong, M.N., Guerra, C., Wunsch, H. Frailty before critical illness and mortality for elderly Medicare beneficiaries. J Am Geriatr Soc 2015;63(6):1121–8.CrossRefGoogle ScholarPubMed
Hemphill, J.C., 3rd, Farrant, M., Neill, T.A., Jr. Prospective validation of the ICH score for 12-month functional outcome. Neurology 2009;73(14):1088–94.CrossRefGoogle ScholarPubMed
Herridge, M.S., Cheung, A.M., Tansey, C.M., et al. One-year outcomes in survivors of the acute respiratory distress syndrome. N Engl J Med 2003;348(8):683–93.CrossRefGoogle ScholarPubMed
Herridge, M.S., Tansey, C.M., Matte, A., et al. Functional disability 5 years after acute respiratory distress syndrome. N Engl J Med 2011;364(14):1293–304.CrossRefGoogle ScholarPubMed
Ehlenbach, W.J., Hough, C.L., Crane, P.K., et al. Association between acute care and critical illness hospitalization and cognitive function in older adults. JAMA 2010;303(8):763–70.Google ScholarPubMed
McDermid, R.C., Stelfox, H.T., Bagshaw, S.M. Frailty in the critically ill: a novel concept. Crit Care 2011;15(1):301.CrossRefGoogle ScholarPubMed
Dudek, F.E., Tasker, J.G., Wuarin, J.P. Intrinsic and synaptic mechanisms of hypothalamic neurons studied with slice and explant preparations. J Neurosci Methods 1989;28(1–2):5969.CrossRefGoogle ScholarPubMed
Latham, N.K., Harris, B.A., Bean, J.F., et al. Effect of a home-based exercise program on functional recovery following rehabilitation after hip fracture: a randomized clinical trial. JAMA 2014;311(7):700–8.CrossRefGoogle ScholarPubMed
Abizanda, P., Lopez, M.D., Garcia, V.P., et al. Effects of an oral nutritional supplementation plus physical exercise intervention on the physical function, nutritional status, and quality of life in frail institutionalized older adults: the ACTIVNES study. J Am Med Dir Assoc 2015;16(5):439 e9e16.CrossRefGoogle ScholarPubMed
Fragala, M.S., Dam, T.T., Barber, V., et al. Strength and function response to clinical interventions of older women categorized by weakness and low lean mass using classifications from the Foundation for the National Institute of Health sarcopenia project. J Gerontol A Biol Sci Med Sci 2015;70(2):202–9.CrossRefGoogle ScholarPubMed
Schweickert, W.D., Pohlman, M.C., Pohlman, A.S., et al. Early physical and occupational therapy in mechanically ventilated, critically ill patients: a randomised controlled trial. Lancet 2009;373(9678):1874–82.CrossRefGoogle ScholarPubMed
Burtin, C., Clerckx, B., Robbeets, C., et al. Early exercise in critically ill patients enhances short-term functional recovery. Crit Care Med 2009;37(9):2499–505.CrossRefGoogle ScholarPubMed
Segers, J., Hermans, G., Bruyninckx, F., et al. Feasibility of neuromuscular electrical stimulation in critically ill patients. J Crit Care 2014;29(6):1082–8.CrossRefGoogle ScholarPubMed
Kho, M.E., Truong, A.D., Zanni, J.M., et al. Neuromuscular electrical stimulation in mechanically ventilated patients: a randomized, sham-controlled pilot trial with blinded outcome assessment. J Crit Care 2015;30(1):32–9.CrossRefGoogle ScholarPubMed
Puthucheary, Z.A., Rawal, J., McPhail, M., et al. Acute skeletal muscle wasting in critical illness. JAMA 2013;310(15):1591–600.CrossRefGoogle ScholarPubMed
Cohen, S., Nathan, J.A., Goldberg, A.L. Muscle wasting in disease: molecular mechanisms and promising therapies. Nat Rev Drug Discov 2015;14(1):5874.CrossRefGoogle ScholarPubMed
Leitner, L.M., Wilson, R.J., Yan, Z. Godecke, A. Reactive oxygen species/nitric oxide mediated inter-organ communication in skeletal muscle wasting diseases. Antioxid Redox Signal 2017;26(13):700–17.CrossRefGoogle ScholarPubMed
Haidet, A.M., Rizo, L., Handy, C., et al. Long-term enhancement of skeletal muscle mass and strength by single gene administration of myostatin inhibitors. Proc Natl Acad Sci U S A 2008;105(11):4318–22.CrossRefGoogle ScholarPubMed
Doig, G.S., Simpson, F., Sweetman, E.A., et al. Early parenteral nutrition in critically ill patients with short-term relative contraindications to early enteral nutrition: a randomized controlled trial. JAMA 2013;309(20):2130–8.CrossRefGoogle ScholarPubMed
Hermans, G., Casaer, M.P., Clerckx, B., et al. Effect of tolerating macronutrient deficit on the development of intensive-care unit acquired weakness: a subanalysis of the EPaNIC trial. Lancet Respir Med 2013;1(8):621–9.CrossRefGoogle ScholarPubMed
Takala, J., Ruokonen, E., Webster, N.R., et al. Increased mortality associated with growth hormone treatment in critically ill adults. N Engl J Med 1999;341(11):785–92.CrossRefGoogle ScholarPubMed
Schulman, R.C., Mechanick, J.I. Metabolic and nutrition support in the chronic critical illness syndrome. Respir Care 2012;57(6):958–77; discussion 77–8.CrossRefGoogle ScholarPubMed
Van den Berghe, G. Novel insights into the neuroendocrinology of critical illness. Eur J Endocrinol 2000;143(1):113.CrossRefGoogle ScholarPubMed

References

Burke Rehabilitation. Physical therapy. Available at: www.burke.org/inpatient/admissions/what-is-acute-rehabGoogle Scholar
Columbia University Department of Rehabilitation and Regenerative Medicine. Rehabilitation and regenerative medicine. Available at : www.cumc.columbia.edu/rehab/patient-resources/subacute-inpatient-rehabilitationGoogle Scholar
Vigor Physical Therapy. Comparison between acute and subacute care rehab.Google Scholar
New York State Department of Health. NYS Health Profiles. About Certified Home Health Agencies (CHHAs). Available at: https://profiles.health.ny.gov/home_care/pages/chhaGoogle Scholar
A Place for Mom. Home care vs. home health care: what’s the difference? www.aplaceformom.com/planning-and-advice/articles/home-health-vs-home-careGoogle Scholar
Muldoon, SR. Why LTAC hospitals are a choice for critically ill patients. Kindred Hospitals. 2020. Available at: www.kindredhealthcare.com/resources/blog-kindred-continuum/2020/03/19/why-ltac-hospitals-are-often-the-right-choice-for-critically-ill-patientsGoogle Scholar
Vitas Health Care. What are the differences and commonalities between hospice and palliative care? Available at: www.vitas.com/hospice-and-palliative-care-basics/about-palliative-care/hospice-vs-palliative-care-whats-the-differenceGoogle Scholar
Caring.com. Nursing home costs and ways to pay. Available at: www.caring.com/senior-living/nursing-homes/how-to-pay/Google Scholar
Derose, KP, Escarce, JJ, Lurie, N. Immigrants and health care: sources of vulnerability. Health Aff (Millwood). 2007;26(5):258–68.CrossRefGoogle ScholarPubMed
Lee, H, Zakhary, BL, Firek, MA, et al. The prevalence and impact of diabetes mellitus among undocumented immigrants in an indigent care program in Riverside, California. Diabetes. 2018;67(supplement 1):1625-P.CrossRefGoogle Scholar
Capps, R, Fix, M, VanHook, J, Bachmeier, JD. A Demographic, Socioeconomic, and Health Coverage Profile of Unauthorized Immigrants in the United States. Washington, DC; Migration Policy Institute, 2013. Available at: www.migrationpolicy.org/sites/default/files/publications/CIRbrief-Profile-Unauthorized_1.pdf.Google Scholar
Fruth, S. Medical repatriation: the intersection of mandated emergency care, immigration consequences, and international obligations. J Leg Med. 2015;36(1):4572;.CrossRefGoogle ScholarPubMed
Patel, H, Yirdaw, E. Improving early discharge using a team-based structure for discharge multidisciplinary rounds. Prof Case Manag. 2019;24(2):83–9.CrossRefGoogle ScholarPubMed
DeMartino, ES, Dudzinski, DM, Doyle, CK, et al. Who decides when a patient can’t? Statutes on alternate decision makers. New Engl J Med. 2017;376(15):1478–82.CrossRefGoogle Scholar

References

Lewis, A. A survey of multidenominational rabbis on death by neurologic criteria. Neurocrit Care. 2019;31(2):411–18.CrossRefGoogle ScholarPubMed
Gostin, L.O. Deciding life and death in the courtroom. JAMA. 1997;278(18);1523.CrossRefGoogle ScholarPubMed
Pew Research Center 2014 U.S. religious landscape study. Pew Research Center. 2014.Google Scholar
Setta, S.M., Shemie, S.D.. An explanation and analysis of how world religions formulate their ethical decisions on withdrawing treatment and determining death. Philos Ethics Humanit Med. 2015;10:6.CrossRefGoogle ScholarPubMed
Dorff, E.N. End-of-life: Jewish perspectives. Lancet. 2005;366(9488):862–5.CrossRefGoogle ScholarPubMed
Kassim, P., Alias, F.. Religious, ethical, and legal consideration in end-of-life issues: fundamental requisites for medical decision making. J Relig Health. 2016;55:119–34.Google Scholar
Engelhardt, H.T. Jr, Iltis, A.S.. End-of-life: the traditional Christian view. Lancet. 2005;366(9490):1045–9.Google ScholarPubMed
Markwell, H. End-of-life: a Catholic view. Lancet, 2005 366(9491):1132–5.CrossRefGoogle ScholarPubMed
Bulow, H.H., Sprung, C.L, Reinhart, K, et al. The world’s major religions’ points of view on end-of-life decisions in the intensive care unit. Intensive Care Med. 2008;34(3):423–30.CrossRefGoogle ScholarPubMed
Sacred Congregation for the Doctrine of the Faith.Declaration on Euthanasia. May 5, 1980.Google Scholar
Paul, Pope John II, Address to the Participants in the International Congress on Life-Sustaining Treatment and Vegetative State: Scientific Advances and Ethical Dilemmas.” March 20, 2004.Google Scholar
Carson, T. The New Catholic Encylopedia. 2nd ed. Gale Research, 2002.Google Scholar
Sachedina, A. End-of-life: the Islamic view. Lancet. 2005;366(9487):774–9.CrossRefGoogle ScholarPubMed
Young, K.K. The discourses of Hindu medical ethics. In Baker, R.B, McCullough, L.B., editors. The Cambridge World History of Medical Ethics. Cambridge: Cambridge University Press, 2009; 175–84.Google Scholar
Firth, S. End-of-life: a Hindu view. Lancet. 2005;366(9486):682–6.CrossRefGoogle ScholarPubMed
Keown, D. End of life: the Buddhist view. Lancet. 2005;366(9489):952–5.CrossRefGoogle ScholarPubMed
Pope, T.M. Legal aspects in palliative and end-of-life care in the United States. UptoDate. 2019. Last updated September 22, 2023.Google Scholar
Schwartz, J.L., Caplan, A.L.. Vaccination refusal: ethics, individual rights, and the common good. Prim Care. 2011;38(4):717–28, ix.CrossRefGoogle ScholarPubMed
Breeden, A. Hours after French patient is taken off life support, a court orders it be restored. New York Times. May 20, 2019.Google Scholar
Ollove, M. Palliative sedation, an end-of-life practice that is legal everywhere. Stateline, an initiative of the Pew Charitable Trusts, 2018.Google Scholar
Lewis, A., Scheyer, O.. Legal objections to use of neurologic criteria to declare death in the United States: 1968 to 2017. Chest. 2019;155(6):1234–45.CrossRefGoogle ScholarPubMed
In re Callaway, Montana Ninth Judicial District Court, Pondera County, no. DG-16–08. 2016.Google Scholar
In re Lawson, No. CL16-2358 (City of Richmond Cir. Ct., Va., June 10, 2016) (order).Google Scholar
Choong, K.A., Rady, M.Y.. Re A (A Child) and the United Kingdom Code of Practice for the Diagnosis and Confirmation of Death: should a secular construct of death override religious values in a pluralistic society? HEC Forum. 2018;30(1):7189.CrossRefGoogle Scholar
McKitty v. Hayani, in ONSC 4015 (CanLII). 2018.Google Scholar
Lewis, A. The legacy of Jahi McMath. Neurocrit Care. 2018;29(3):519–20.CrossRefGoogle ScholarPubMed
Bosslet, G.T., Pope, T.M, Rubenfeld, G.D, et al. An official ATS/AACN/ACCP/ESICM/SCCM policy statement: responding to requests for potentially inappropriate treatments in intensive care units. Am J Respir Crit Care Med. 2015;191(11):1318–30.CrossRefGoogle ScholarPubMed

References

Michie, D, Spiegelhalter, DJ, Taylor, C. Machine Learning. Neural and Statistical Classification, self-published, 1994;13.Google Scholar
Goodfellow, I, Bengio, Y, Courville, A. Deep Learning. Cambridge, MA: MIT Press, 2016.Google Scholar
Mohri, M, Rostamizadeh, A, Talwalkar, A. Foundations of Machine Learning: Cambridge, MA: MIT Press, 2018.Google Scholar
Schwartz, WB. Medicine and the computer: the promise and problems of change. In Use and Impact of Computers in Clinical Medicine. New York: Springer, 1970;321–35.Google Scholar
Greene, JA, Lea, AS. Digital futures past – the long arc of big data in medicine. N Engl J Med 2019;381(5):480–5.CrossRefGoogle Scholar
Nash, F. Differential diagnosis, an apparatus to assist the logical faculties. Lancet 1954;266:8745.Google ScholarPubMed
Shortliffe, EH. Computer-Based Medical Consultations: MYCIN. New York: Elsevier, 1976.Google Scholar
Miller, RA, McNeil, MA, Challinor, SM, Masarie, FE, Myers, JD. The INTERNIST-1/QUICK MEDICAL REFERENCE project – status report. West J Med 1986;145:816.Google ScholarPubMed
Shwe, MA, Middleton, B, Heckerman, DE, et al. Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. Methods Inf Med 1991;30:241–55.Google ScholarPubMed
BLUM, RL. Computer-assisted design of studies using routine clinical data: analyzing the association of prednisone and cholesterol. Ann Int Me 1986;104:858868.CrossRefGoogle ScholarPubMed
Papik, K, Molnar, B, Schaefer, R, et al. Application of neural networks in medicine-a review. Med Sci Monitor 1998;4:MT538–MT546.Google Scholar
Penny, W, Frost, D. Neural networks in clinical medicine. Med Decis Making 1996;16:386–98.CrossRefGoogle ScholarPubMed
Crevier, D. AI: The Tumultuous History of the Search for Artificial Intelligence. New York: Basic Books, 1993.Google Scholar
Pedregosa, F, Varoquaux, G, Gramfort, A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.Google Scholar
Dabbish, L, Stuart, C, Tsay, J, Herbsleb, J. Social coding in GitHub: transparency and collaboration in an open software repository. Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work. 2012: ACM: 1277–86.CrossRefGoogle Scholar
Tegmark, M. Life 3.0: Being Human in the Age of Artificial Intelligence. New York: Knopf, 2017.Google Scholar
Rajkomar, A, Dean, J, Kohane, I. Machine learning in medicine. N Engl J Med 2019;380:1347–58.CrossRefGoogle ScholarPubMed
Esteva, A, Kuprel, B, Novoa, RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115.CrossRefGoogle ScholarPubMed
De Fauw, J, Ledsam, JR, Romera-Paredes, B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018;24:1342–50.CrossRefGoogle ScholarPubMed
Hannun, AY, Rajpurkar, P, Haghpanahi, M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med 2019;25:65–9.Google ScholarPubMed
Hemphill, JC 3rd, White, DB. Clinical nihilism in neuroemergencies. Emerg Med Clin North Am 2009;27:2737, viiviii.CrossRefGoogle ScholarPubMed
Breiman, L. Classification and Regression Trees. New York: Routledge, 2017.CrossRefGoogle Scholar
Breiman, L. Random forests. Machine Learn 2001;45:532.CrossRefGoogle Scholar
Bertsimas, D, Dunn, J. Optimal classification trees. Machine Learn 2017;106:1039–82.CrossRefGoogle Scholar
Cover, T, Hart, P. Nearest neighbor pattern classification. IEEE Trans Inf Theory 1967;13:21–7.CrossRefGoogle Scholar
Haykin, S. Neural Networks: A Comprehensive Foundation. Englewood Cliffs: Prentice-Hall, 1994.Google Scholar
Hastie, T, Tibshirani, R, Friedman, J, Franklin, J. The elements of statistical learning: data mining, inference and prediction. Math Intell 2005;27:83–5.Google Scholar
Lip, GY, Nieuwlaat, R, Pisters, R, Lane, DA, Crijns, HJ. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the Euro Heart Survey on Atrial Fibrillation. Chest 2010;137:263–72.CrossRefGoogle ScholarPubMed
Rothwell, P, Giles, M, Flossmann, E, et al. A simple score (ABCD) to identify individuals at high early risk of stroke after transient ischaemic attack. Lancet 2005;366:2936.CrossRefGoogle ScholarPubMed
Koton, S, Rothwell, P. Performance of the ABCD and ABCD2 scores in TIA patients with carotid stenosis and atrial fibrillation. Cerebrovasc Dis 2007;24:231–5.CrossRefGoogle ScholarPubMed
Shariff, N, Aleem, A, Singh, M, Li, YZ, Smith, SJ. AF and venous thromboembolism – pathophysiology, risk assessment and CHADS-VASc score. J Atr Fibrillation 2012;5:649.Google ScholarPubMed
Keegan, MT, Gajic, O, Afessa, B. Severity of illness scoring systems in the intensive care unit. Crit Care Med 2011;39:163–9.CrossRefGoogle ScholarPubMed
Wong, A, Young, AT, Liang, AS, et al. Development and validation of an electronic health record–based machine learning model to estimate delirium risk in newly hospitalized patients without known cognitive impairment. JAMA Netw Open 2018;1:e181018–e181018.CrossRefGoogle ScholarPubMed
Douglas, VC, Hessler, CS, Dhaliwal, G, et al. The AWOL tool: derivation and validation of a delirium prediction rule. J Hosp Med 2013;8:493–9.CrossRefGoogle ScholarPubMed
Heo, J, Yoon, JG, Park, H, et al. Machine learning–based model for prediction of outcomes in acute stroke. Stroke 2019;50:1263–5.CrossRefGoogle ScholarPubMed
Ntaios, G, Faouzi, M, Ferrari, J, et al. An integer-based score to predict functional outcome in acute ischemic stroke: the ASTRAL score. Neurology 2012;78:1916–22.CrossRefGoogle ScholarPubMed
Asadi, H, Dowling, R, Yan, B, Mitchell, P. Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PloS One 2014;9:e88225.CrossRefGoogle ScholarPubMed
Liu, J, Xu, H, Chen, Q, et al. Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine. EBioMedicine 2019;43:454–9.CrossRefGoogle ScholarPubMed
Van Os, HJ, Ramos, LA, Hilbert, A, et al. Predicting outcome of endovascular treatment for acute ischemic stroke: potential value of machine learning algorithms. Front Neurol 2018;9:784.CrossRefGoogle ScholarPubMed
Drotár, P, Mekyska, J, Rektorová, I, et al. Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson’s disease. Artif Intell Med 2016;67:3946.CrossRefGoogle ScholarPubMed
Pirlo, G, Diaz, M, Ferrer, MA, et al. Early diagnosis of neurodegenerative diseases by handwritten signature analysis. In International Conference on Image Analysis and Processing. New York: Springer, 2015; 290–7.Google Scholar
Zhang, H-H, Yang, L, Liu, Y, et al. Classification of Parkinson’s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples. Biomed Eng Online 2016;15:122.CrossRefGoogle ScholarPubMed
Li, Y, Yang, L, Wang, P, et al. Classification of Parkinson’s disease by decision tree based instance selection and ensemble learning algorithms. J Med Imaging Health Inform 2017;7:444–52.CrossRefGoogle Scholar
Tu, M, Berisha, V, Liss, J. Interpretable objective assessment of dysarthric speech based on deep neural networks. Proc Interspeech 2017:1849–53.CrossRefGoogle Scholar
Krizhevsky, A, Sutskever, I, Hinton, GE. ImageNet classification with deep convolutional neural networks. Comm ACM 2017;60:8490.CrossRefGoogle Scholar
Griffis, JC, Allendorfer, JB, Szaflarski, JP. Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans. J Neurosci Methods 2016;257:97108.CrossRefGoogle ScholarPubMed
Kamnitsas, K, Ledig, C, Newcombe, VF, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 2017;36:6178.CrossRefGoogle ScholarPubMed
Thornhill, RE, Lum, C, Jaberi, A, et al. Can shape analysis differentiate free-floating internal carotid artery thrombus from atherosclerotic plaque in patients evaluated with CTA for stroke or transient ischemic attack? Acad Radiol 2014;21:345–54.CrossRefGoogle ScholarPubMed
Dhar, R, Chen, Y, An, H, Lee, J-M. Application of machine learning to automated analysis of cerebral edema in large cohorts of ischemic stroke patients. Front Neurol 2018;9:687.CrossRefGoogle ScholarPubMed
Sheth, SA, Lopez-Rivera, V, Barman, A, et al. Machine learning–enabled automated determination of acute ischemic core from computed tomography angiography. Stroke 2019;50:30933100.CrossRefGoogle ScholarPubMed
Albers, GW, Wald, MJ, Mlynash, M, et al. Automated calculation of Alberta Stroke Program early CT score: validation in patients with large hemispheric infarct. Stroke 2019;50:3277–9.CrossRefGoogle ScholarPubMed
Forkert, ND, Verleger, T, Cheng, B, et al. Multiclass support vector machine-based lesion mapping predicts functional outcome in ischemic stroke patients. PLoS One 2015;10:e0129569.CrossRefGoogle ScholarPubMed
Nielsen, A, Hansen, MB, Tietze, A, Mouridsen, K. Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Stroke 2018;49:13941401.CrossRefGoogle ScholarPubMed
Rehme, AK, Volz, LJ, Feis, D-L, et al. Identifying neuroimaging markers of motor disability in acute stroke by machine learning techniques. Cereb Cortex 2014;25:3046–56.Google ScholarPubMed
Bentley, P, Ganesalingam, J, Jones, ALC, et al. Prediction of stroke thrombolysis outcome using CT brain machine learning. Neuroimage Clin 2014;4:635–40.CrossRefGoogle ScholarPubMed
Strbian, D, Engelter, S, Michel, P, et al. Symptomatic intracranial hemorrhage after stroke thrombolysis: the SEDAN score. Ann Neurol 2012;71:634–41.CrossRefGoogle ScholarPubMed
Lou, M, Safdar, A, Mehdiratta, M, et al. The HAT score: a simple grading scale for predicting hemorrhage after thrombolysis. Neurology 2008;71:1417–23.CrossRefGoogle Scholar
Yu, Y, Guo, D, Lou, M, Liebeskind, D, Scalzo, F. Prediction of hemorrhagic transformation severity in acute stroke from source perfusion MRI. IEEE Trans Biomed Eng 2017;65:2058–65.Google ScholarPubMed
Takahashi, N, Lee, Y, Tsai, D-Y, et al. An automated detection method for the MCA dot sign of acute stroke in unenhanced CT. Radiol Phys Technol 2014;7:7988.CrossRefGoogle ScholarPubMed
Lee, H, Yune, S, Mansouri, M, et al. An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng 2019;3:173.CrossRefGoogle ScholarPubMed
Gunter, NB, Schwarz, CG, Graff-Radford, J, et al. Automated detection of imaging features of disproportionately enlarged subarachnoid space hydrocephalus using machine learning methods. Neuroimage Clin 2019;21:101605.CrossRefGoogle ScholarPubMed
Ramos, LA, van der Steen, WE, Barros, RS, et al. Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage. J Neurointerv Surg 2019;11:497502.CrossRefGoogle ScholarPubMed
Ribeiro, MT, Singh, S, Guestrin, C. “Why should I trust you?”: explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016: 1135–44.CrossRefGoogle Scholar
Shrot, S, Salhov, M, Dvorski, N, et al. Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme. Neuroradiology 2019;61:757–65.CrossRefGoogle ScholarPubMed
Liao, X, Cai, B, Tian, B, et al. Machine‐learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time. J Cell Mol Med 2019;23:4375–85.CrossRefGoogle ScholarPubMed
Peeken, JC, Goldberg, T, Pyka, T, et al. Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme. Cancer Med 2019;8:128–36.CrossRefGoogle ScholarPubMed
Zhang, B, Chang, K, Ramkissoon, S, et al. Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neurooncol 2016;19:109–17.Google ScholarPubMed
Akkus, Z, Ali, I, Sedlář, J, et al. Predicting deletion of chromosomal arms 1p/19q in low-grade gliomas from MR images using machine intelligence. J Digit Imaging 2017;30:469–76.CrossRefGoogle ScholarPubMed
Zhou, H, Chang, K, Bai, HX, et al. Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low-and high-grade gliomas. J Neurooncol 2019;142:299307.CrossRefGoogle ScholarPubMed
Fischl, B. FreeSurfer. Neuroimage 2012;62:774–81.CrossRefGoogle ScholarPubMed
Lu, D, Popuri, K, Ding, GW, Balachandar, R, Beg, MF. Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer’s disease using structural MR and FDG-PET images. Sci Rep 2018;8:5697.CrossRefGoogle ScholarPubMed
Young, J, Modat, M, Cardoso, MJ, et al. Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment. Neuroimage Clin 2013;2:735–45.CrossRefGoogle ScholarPubMed
An, L, Adeli, E, Liu, M, et al. A hierarchical feature and sample selection framework and its application for Alzheimer’s disease diagnosis. Sci Rep 2017;7:45269.CrossRefGoogle ScholarPubMed
Suk, H-I, Lee, S-W, Shen, D, Initiative AsDN. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuromage 2014;101:569–82.Google ScholarPubMed
Moradi, E, Pepe, A, Gaser, C, Huttunen, H, Tohka, J, Initiative AsDN. Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage 2015;104:398412.CrossRefGoogle ScholarPubMed
Liu, K, Chen, K, Yao, L, Guo, X. Prediction of mild cognitive impairment conversion using a combination of independent component analysis and the Cox model. Front Hum Neurosci 2017;11:33.CrossRefGoogle ScholarPubMed
Cheng, B, Liu, M, Zhang, D, Munsell, BC, Shen, D. Domain transfer learning for MCI conversion prediction. IEEE Trans Biomed Eng 2015;62:1805–17.CrossRefGoogle ScholarPubMed
Kaufmann, T, van der Meer, D, Doan, NT, et al. Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nat Neurosci 2019;22:1617–23.CrossRefGoogle ScholarPubMed
Puntmann, V. How-to guide on biomarkers: biomarker definitions, validation and applications with examples from cardiovascular disease. Postgrad Med J 2009;85:538–45.CrossRefGoogle Scholar
Muller, E, Shock, JP, Bender, A, et al. Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness. Comput Biol Med 2019;107:145–52.CrossRefGoogle ScholarPubMed
Peacock, WF IV, Van Meter, TE, Mirshahi, N, et al. Derivation of a three biomarker panel to improve diagnosis in patients with mild traumatic brain injury. Front Neurol 2017;8:641.CrossRefGoogle ScholarPubMed
Tanioka, S, Ishida, F, Nakano, F, et al. Machine learning analysis of matricellular proteins and clinical variables for early prediction of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage. Mol Neurobiol 2019;56:7128–35.CrossRefGoogle Scholar
Olar, A, Wani, KM, Sulman, EP, et al. Mitotic index is an independent predictor of recurrence‐free survival in meningioma. Brain Pathol 2015;25:266–75.CrossRefGoogle ScholarPubMed
Chang, P, Malone, H, Bowden, S, et al. A multiparametric model for mapping cellularity in glioblastoma using radiographically localized biopsies. Am Journal of Neuroradiol 2017;38:890–8.CrossRefGoogle ScholarPubMed
Orringer, DA, Pandian, B, Niknafs, YS, et al. Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy. Nature Biomed Eng 2017;1:0027.CrossRefGoogle ScholarPubMed
Huang, X, Liu, H, Li, X, et al. Revealing Alzheimer’s disease genes spectrum in the whole-genome by machine learning. BMC Neurol 2018;18:5.CrossRefGoogle ScholarPubMed
Zafeiris, D, Rutella, S, Ball, GR. An artificial neural network integrated pipeline for biomarker discovery using Alzheimer’s disease as a case study. Comput Struct Biotechnol J 2018;16:7787.CrossRefGoogle ScholarPubMed
Mika, S, Ratsch, G, Weston, J, Scholkopf, B, Mullers, K-R. Fisher discriminant analysis with kernels. Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No. 98TH8468). Madison, WI, 1999 41–8.Google Scholar
Crowgey, EL, Marsh, AG, Robinson, KG, Yeager, SK, Akins, RE. Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy. BMC Bioinformatics 2018;19:225.CrossRefGoogle ScholarPubMed
Bahado-Singh, RO, Vishweswaraiah, S, Aydas, B, et al. Deep learning/artificial intelligence and blood-based DNA epigenomic prediction of cerebral palsy. Int J Mol Sci 2019;20:2075.CrossRefGoogle ScholarPubMed
Aref-Eshghi, E, Rodenhiser, DI, Schenkel, LC, et al. Genomic DNA methylation signatures enable concurrent diagnosis and clinical genetic variant classification in neurodevelopmental syndromes. Am J Hum Genet 2018;102:156–74.CrossRefGoogle ScholarPubMed
Benghanem, S, Paul, M, Charpentier, J, et al. Value of EEG reactivity for prediction of neurologic outcome after cardiac arrest: insights from the Parisian registry. Resuscitation 2019;142:168–74.CrossRefGoogle ScholarPubMed
Ruijter, BJ, Tjepkema‐Cloostermans, MC, Tromp, SC, et al. Early EEG for outcome prediction of postanoxic coma: a prospective cohort study. Ann Neurol 2019;86:203–14.CrossRefGoogle ScholarPubMed
Admiraal, MM, Anne-Fleur van Rootselaar, M, Hofmeijer, J, et al. Electroencephalographic reactivity as predictor of neurological outcome in postanoxic coma: a multicenter prospective cohort study. Ann Neurol 2019;86:1727.CrossRefGoogle Scholar
Mayer, SA, Claassen, J, Lokin, J, et al. Refractory status epilepticus: frequency, risk factors, and impact on outcome. Arch Neurol 2002;59:205–10.CrossRefGoogle ScholarPubMed
Johnson, EL, Martinez, NC, Ritzl, EK. EEG characteristics of successful burst suppression for refractory status epilepticus. Neurocrit Care 2016;25:407–14.CrossRefGoogle ScholarPubMed
Hirsch, L, LaRoche, S, Gaspard, N, et al. American Clinical Neurophysiology Society’s standardized critical care EEG terminology: 2012 version. J Clin Neurophysiol 2013;30:127.CrossRefGoogle ScholarPubMed
Cruse, D, Chennu, S, Chatelle, C, et al. Bedside detection of awareness in the vegetative state: a cohort study. Lancet 2011;378:2088–94.CrossRefGoogle ScholarPubMed
Goldfine, AM, Bardin, JC, Noirhomme, Q, et al. Reanalysis of “Bedside detection of awareness in the vegetative state: a cohort study.Lancet 2013;381:289–91.CrossRefGoogle ScholarPubMed
Henriques, J, Gabriel, D, Grigoryeva, L, et al. Protocol design challenges in the detection of awareness in aware subjects using EEG signals. Clin EEG Neurosci 2016;47:266–75.CrossRefGoogle ScholarPubMed
Höller, Y, Bergmann, J, Thomschewski, A, et al. Comparison of EEG-features and classification methods for motor imagery in patients with disorders of consciousness. PloS One 2013;8:e80479.CrossRefGoogle ScholarPubMed
Claassen, J, Doyle, K, Matory, A, et al. Detection of brain activation in unresponsive patients with acute brain injury. N Engl J Med 2019;380:24972505.CrossRefGoogle ScholarPubMed
Edlow, BL, Chatelle, C, Spencer, CA, et al. Early detection of consciousness in patients with acute severe traumatic brain injury. Brain 2017;140:23992414.CrossRefGoogle ScholarPubMed
Pan, J, Xie, Q, He, Y, et al. Detecting awareness in patients with disorders of consciousness using a hybrid brain–computer interface. J Neural Eng 2014;11:056007.CrossRefGoogle ScholarPubMed
King, J-R, Sitt, JD, Faugeras, F, et al. Information sharing in the brain indexes consciousness in noncommunicative patients. Curr Biol 2013;23:1914–19.CrossRefGoogle ScholarPubMed
Geurts, P, Ernst, D, Wehenkel, L. Extremely randomized trees. Mach Learn 2006;63:342.CrossRefGoogle Scholar
Engemann, DA, Raimondo, F, King, J-R, et al. Robust EEG-based cross-site and cross-protocol classification of states of consciousness. Brain 2018;141:3179–92.CrossRefGoogle ScholarPubMed
Emami, A, Kunii, N, Matsuo, T, et al. Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images. Neuroimage Clin 2019;22:101684.CrossRefGoogle ScholarPubMed
Schirrmeister, RT, Springenberg, JT, Fiederer, LDJ, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 2017;38:53915420.CrossRefGoogle ScholarPubMed
Acharya, UR, Oh, SL, Hagiwara, Y, Tan, JH, Adeli, H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 2018;100:270–8.CrossRefGoogle ScholarPubMed
LeCun, Y, Bengio, Y, Hinton, G. Deep learning. Nature 2015; 521:436.CrossRefGoogle ScholarPubMed
Varsavsky, A, Mareels, I, Cook, M. Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction. Boca Raton: CRC Press, 2016.CrossRefGoogle Scholar
Holzinger, A. Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inform 2016;3:119–31.CrossRefGoogle ScholarPubMed
Zou, J, Schiebinger, L. Design AI so that it’s fair. Nature 2018;559:324–6.Google Scholar
Bouton, CE, Shaikhouni, A, Annetta, NV, et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature 2016;533:247.CrossRefGoogle Scholar
Farina, D, Vujaklija, I, Sartori, M, et al. Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation. Nat Biomed Eng 2017;1:0025.CrossRefGoogle Scholar
Beam, AL, Kohane, IS. Big data and machine learning in health care. JAMA 2018;319:1317–18.CrossRefGoogle ScholarPubMed

References

Mozaffarian, D, Benjamin, EJ, Go, AS, et al. Heart disease and stroke statistics – 2015 update: a report from the American Heart Association. Circulation. 2015;131:e29322.Google ScholarPubMed
May, TL, Ruthazer, R, Riker, RR, et al. Early withdrawal of life support after resuscitation from cardiac arrest is common and may result in additional deaths. Resuscitation 2019;139:308–13.CrossRefGoogle ScholarPubMed
Stammet, P. Blood biomarkers of hypoxic-ischemic brain injury after cardiac arrest. Semin Neurol. 2017;37:7580.Google ScholarPubMed
Ramont, L, Thoannes, H, Volondat, A, et al. Effects of hemolysis and storage condition on neuron-specific enolase (NSE) in cerebrospinal fluid and serum: implications in clinical practice. Clin Chem Lab Med. 2005;43:1215–17.CrossRefGoogle ScholarPubMed
Kaiser, E, Kuzmits, R, Pregant, P, Burghuber, O, Worofka, W. Clinical biochemistry of neuron specific enolase. Clin Chim Acta. 1989;183:1331.CrossRefGoogle ScholarPubMed
Wijdicks, EF, Hijdra, A, Young, GB, Bassetti, CL, Wiebe, S ; QSSotAAo. Practice parameter: prediction of outcome in comatose survivors after cardiopulmonary resuscitation (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2006;67:203–10.Google Scholar
Fugate, JE, Wijdicks, EF, Mandrekar, J, et al. Predictors of neurologic outcome in hypothermia after cardiac arrest. Ann Neurol. 2010;68:907–14.CrossRefGoogle ScholarPubMed
Reisinger, J, Höllinger, K, Lang, W, et al. Prediction of neurological outcome after cardiopulmonary resuscitation by serial determination of serum neuron-specific enolase. Eur Heart J. 2007;28:52–8.CrossRefGoogle ScholarPubMed
Steffen, IG, Hasper, D, Ploner, CJ, et al. Mild therapeutic hypothermia alters neuron specific enolase as an outcome predictor after resuscitation: 97 prospective hypothermia patients compared to 133 historical non-hypothermia patients. Crit Care. 2010;14:R69.CrossRefGoogle ScholarPubMed
Streitberger, KJ, Leithner, C, Wattenberg, M, et al. Neuron-specific enolase predicts poor outcome after cardiac arrest and targeted temperature management: a multicenter study on 1,053 patients. Crit Care Med. 2017;45:1145–51.CrossRefGoogle ScholarPubMed
Wiberg, S, Hassager, C, Stammet, P, et al. Single versus serial measurements of neuron-specific enolase and prediction of poor neurological outcome in persistently unconscious patients after out-of-hospital cardiac arrest – a TTM-trial substudy. PLoS One. 2017;12:e0168894.CrossRefGoogle ScholarPubMed
Rossetti, AO, Carrera, E, Oddo, M. Early EEG correlates of neuronal injury after brain anoxia. Neurology. 2012;78:796802.CrossRefGoogle ScholarPubMed
Oddo, M, Rossetti, AO. Early multimodal outcome prediction after cardiac arrest in patients treated with hypothermia. Crit Care Med. 2014;42:1340–7.CrossRefGoogle ScholarPubMed
Donato, R. Functional roles of S100 proteins, calcium-binding proteins of the EF-hand type. Biochim Biophys Acta. 1999;1450:191231.CrossRefGoogle ScholarPubMed
Böttiger, BW, Möbes, S, Glätzer, R, et al. Astroglial protein S-100 is an early and sensitive marker of hypoxic brain damage and outcome after cardiac arrest in humans. Circulation. 2001;103:2694–8.CrossRefGoogle ScholarPubMed
Shinozaki, K, Oda, S, Sadahiro, T, et al. Serum S-100B is superior to neuron-specific enolase as an early prognostic biomarker for neurological outcome following cardiopulmonary resuscitation. Resuscitation. 2009;80:870–5.CrossRefGoogle ScholarPubMed
Stammet, P, Dankiewicz, J, Nielsen, N, et al. Protein S100 as outcome predictor after out-of-hospital cardiac arrest and targeted temperature management at 33 °C and 36 °C. Crit Care. 2017;21:153.CrossRefGoogle Scholar
Sandroni, C, Cariou, A, Cavallaro, F, et al. Prognostication in comatose survivors of cardiac arrest: an advisory statement from the European Resuscitation Council and the European Society of Intensive Care Medicine. Resuscitation. 2014;85:1779–89.CrossRefGoogle ScholarPubMed
Samaniego, EA, Persoon, S, Wijman, CA. Prognosis after cardiac arrest and hypothermia: a new paradigm. Curr Neurol Neurosci Rep. 2011;11:111–19.CrossRefGoogle ScholarPubMed
Kuhle, J, Barro, C, Andreasson, U, et al. Comparison of three analytical platforms for quantification of the neurofilament light chain in blood samples: ELISA, electrochemiluminescence immunoassay and Simoa. Clin Chem Lab Med. 2016;54:1655–61.CrossRefGoogle ScholarPubMed
Moseby-Knappe, M, Mattsson, N, Nielsen, N, et al. Serum neurofilament light chain for prognosis of outcome after cardiac arrest. JAMA Neurol. 2019;76:6471.CrossRefGoogle ScholarPubMed
Sun, P, Liu, DZ, Jickling, GC, Sharp, FR, Yin, KJ. MicroRNA-based therapeutics in central nervous system injuries. J Cereb Blood Flow Metab. 2018;38:1125–48.CrossRefGoogle ScholarPubMed
Devaux, Y, Dankiewicz, J, Salgado-Somoza, A, et al. Association of circulating microRNA-124-3p levels with outcomes after out-of-hospital cardiac arrest: a substudy of a randomized clinical trial. JAMA Cardiol. 2016;1:305–13.CrossRefGoogle ScholarPubMed
Boyd, JG, Smithson, LJ, Howes, D, Muscedere, J, Kawaja, MD ; Group CCCTB. Serum proteomics as a strategy to identify novel biomarkers of neurologic recovery after cardiac arrest: a feasibility study. Intensive Care Med Exp. 2016;4:9.CrossRefGoogle ScholarPubMed
Tagnaouti, N, Loebrich, S, Heisler, F, et al. Neuronal expression of muskelin in the rodent central nervous system. BMC Neurosci. 2007;8:28.CrossRefGoogle ScholarPubMed
Foote, M, Zhou, Y. 14–3–3 proteins in neurological disorders. Int J Biochem Mol Biol. 2012;3:152–64.Google ScholarPubMed
Berg, D, Holzmann, C, Riess, O. 14–3–3 proteins in the nervous system. Nat Rev Neurosci. 2003;4:752762.CrossRefGoogle ScholarPubMed
Qi, Z, Zhang, Q, Liu, B, Shao, F, Li, C. Presepsin as a biomarker for evaluating prognosis and early innate immune response of out-of-hospital cardiac arrest patients after return of spontaneous circulation. Crit Care Med. 2019;47:e538e546.CrossRefGoogle ScholarPubMed
van Asch, CJ, Luitse, MJ, Rinkel, GJ, et al. Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis. Lancet Neurol. 2010;9:167–76.CrossRefGoogle ScholarPubMed
Pinho, J, Costa, AS, Araújo, JM, Amorim, JM, Ferreira, C. Intracerebral hemorrhage outcome: A comprehensive update. J Neurol Sci. 2019;398:5466.CrossRefGoogle ScholarPubMed
Yang, G, Hu, R, Zhang, C, et al. A combination of serum iron, ferritin and transferrin predicts outcome in patients with intracerebral hemorrhage. Sci Rep. 2016;6:21970.CrossRefGoogle ScholarPubMed
Garton, ALA, Gupta, VP, Christophe, BR, Connolly, ES. Biomarkers of functional outcome in intracerebral hemorrhage: interplay between clinical metrics, CD163, and ferritin. J Stroke Cerebrovasc Dis. 2017;26:1712–20.CrossRefGoogle ScholarPubMed
Zhao, N, Zhang, AS, Enns, CA. Iron regulation by hepcidin. J Clin Invest. 2013;123:2337–43.CrossRefGoogle ScholarPubMed
Mehdiratta, M, Kumar, S, Hackney, D, Schlaug, G, Selim, M. Association between serum ferritin level and perihematoma edema volume in patients with spontaneous intracerebral hemorrhage. Stroke. 2008;39:1165–70.CrossRefGoogle ScholarPubMed
Xiong, XY, Chen, J, Zhu, WY, et al. Serum hepcidin concentrations correlate with serum iron level and outcome in patients with intracerebral hemorrhage. Neurol Sci. 2015;36:1843–9.CrossRefGoogle ScholarPubMed
Li, W, Pan, R, Qi, Z, Liu, KJ. Current progress in searching for clinically useful biomarkers of blood-brain barrier damage following cerebral ischemia. Brain Circ. 2018;4:145–52.Google ScholarPubMed
Tiedt, S, Duering, M, Barro, C, et al. Serum neurofilament light: A biomarker of neuroaxonal injury after ischemic stroke. Neurology. 2018;91:e1338e1347.CrossRefGoogle Scholar
Branco, JP, Oliveira, S, Sargento-Freitas, J, et al. S100β protein as a predictor of poststroke functional outcome: a prospective study. J Stroke Cerebrovasc Dis. 2018;27:1890–6.CrossRefGoogle ScholarPubMed
Klimiec, E, Pasinska, P, Kowalska, K, et al. The association between plasma endotoxin, endotoxin pathway proteins and outcome after ischemic stroke. Atherosclerosis. 2018;269:138–43.CrossRefGoogle ScholarPubMed
Gan, ZS, Stein, SC, Swanson, R, et al. Blood biomarkers for traumatic brain injury: a quantitative assessment of diagnostic and prognostic accuracy. Front Neurol. 2019;10:446.CrossRefGoogle ScholarPubMed
Rodríguez-Rodríguez, A, Egea-Guerrero, JJ, Gordillo-Escobar, E, et al. S100B and Neuron-Specific Enolase as mortality predictors in patients with severe traumatic brain injury. Neurol Res. 2016;38:130–7.CrossRefGoogle ScholarPubMed
Cheng, F, Yuan, Q, Yang, J, Wang, W, Liu, H. The prognostic value of serum neuron-specific enolase in traumatic brain injury: systematic review and meta-analysis. PLoS One. 2014;9:e106680.CrossRefGoogle ScholarPubMed
Thelin, EP, Jeppsson, E, Frostell, A, et al. Utility of neuron-specific enolase in traumatic brain injury; relations to S100B levels, outcome, and extracranial injury severity. Crit Care. 2016;20:285.CrossRefGoogle ScholarPubMed
Al Nimer, F, Thelin, E, Nyström, H, et al. Comparative assessment of the prognostic value of biomarkers in traumatic brain injury reveals an independent role for serum levels of neurofilament light. PLoS One. 2015;10:e0132177.CrossRefGoogle ScholarPubMed
Shahim, P, Gren, M, Liman, V, et al. Serum neurofilament light protein predicts clinical outcome in traumatic brain injury. Sci Rep. 2016;6:36791.CrossRefGoogle ScholarPubMed
Hol, EM, Pekny, M. Glial fibrillary acidic protein (GFAP) and the astrocyte intermediate filament system in diseases of the central nervous system. Curr Opin Cell Biol. 2015;32:121–30.CrossRefGoogle ScholarPubMed
Shemilt, M, Boutin, A, Lauzier, F, et al. Prognostic value of glial fibrillary acidic protein in patients with moderate and severe traumatic brain injury: a systematic review and meta-analysis. Crit Care Med. 2019;47:e522e529.CrossRefGoogle ScholarPubMed
de Oliveira Manoel, AL, Macdonald, RL. Neuroinflammation as a target for intervention in subarachnoid hemorrhage. Front Neurol. 2018;9:292.CrossRefGoogle ScholarPubMed
Choi, BR, Cho, WH, Kim, J, et al. Increased expression of the receptor for advanced glycation end products in neurons and astrocytes in a triple transgenic mouse model of Alzheimer’s disease. Exp Mol Med. 2014;46:e75.CrossRefGoogle Scholar
Tang, SC, Yeh, SJ, Tsai, LK, et al. Cleaved but not endogenous secretory RAGE is associated with outcome in acute ischemic stroke. Neurology. 2016;86:270–6.CrossRefGoogle Scholar
Yang, DB, Dong, XQ, Du, Q, et al. Clinical relevance of cleaved RAGE plasma levels as a biomarker of disease severity and functional outcome in aneurysmal subarachnoid hemorrhage. Clin Chim Acta. 2018;486:335–40.CrossRefGoogle ScholarPubMed
Burmester, T, Weich, B, Reinhardt, S, Hankeln, T. A vertebrate globin expressed in the brain. Nature. 2000;407:520–3.CrossRefGoogle ScholarPubMed
Ding, CY, Kang, DZ, Wang, ZL, et al. Serum Ngb (neuroglobin) is associated with brain metabolism and functional outcome of aneurysmal subarachnoid hemorrhage. Stroke. 2019;50:1887–90.CrossRefGoogle ScholarPubMed
Miller, BA, Turan, N, Chau, M, Pradilla, G. Inflammation, vasospasm, and brain injury after subarachnoid hemorrhage. Biomed Res Int. 2014;2014:384342.CrossRefGoogle ScholarPubMed
Chou, SH, Feske, SK, Atherton, J, et al. Early elevation of serum tumor necrosis factor-α is associated with poor outcome in subarachnoid hemorrhage. J Investig Med. 2012;60:1054–8.CrossRefGoogle ScholarPubMed
Fragata, I, Bustamante, A, Penalba, A, et al. Venous and arterial TNF-R1 predicts outcome and complications in acute subarachnoid hemorrhage. Neurocrit Care. 2019;31:107–15.CrossRefGoogle ScholarPubMed
Adhikari, NK, Fowler, RA, Bhagwanjee, S, Rubenfeld, GD. Critical care and the global burden of critical illness in adults. Lancet. 2010;376:1339–46.CrossRefGoogle ScholarPubMed
Inoue, S, Hatakeyama, J, Kondo, Y, et al. Post-intensive care syndrome: its pathophysiology, prevention, and future directions. Acute Med Surg. 2019;6:233–46.CrossRefGoogle ScholarPubMed
van den Boogaard, M, Kox, M, Quinn, KL, et al. Biomarkers associated with delirium in critically ill patients and their relation with long-term subjective cognitive dysfunction; indications for different pathways governing delirium in inflamed and noninflamed patients. Crit Care. 2011;15:R297.CrossRefGoogle ScholarPubMed
Maciel, M, Benedet, SR, Lunardelli, EB, et al. Predicting long-term cognitive dysfunction in survivors of critical illness with plasma inflammatory markers: a retrospective cohort study. Mol Neurobiol. 2019;56:763–7.CrossRefGoogle ScholarPubMed
Wang, S, Hammes, J, Khan, S, et al. Improving Recovery and Outcomes Every Day after the ICU (IMPROVE): study protocol for a randomized controlled trial. Trials. 2018;19:196.CrossRefGoogle ScholarPubMed

References

Sandroni, C, Cariou, A, Cavallaro, F, et al. Prognostication in comatose survivors of cardiac arrest: an advisory statement from the European Resuscitation Council and the European Society of Intensive Care Medicine. Resuscitation 2014;85(12):1779–89.CrossRefGoogle ScholarPubMed
Samaniego, EA, Mlynash, M, Caulfield, AF, Eyngorn, I, Wijman, CA. Sedation confounds outcome prediction in cardiac arrest survivors treated with hypothermia. Neurocrit Care 2011;15(1):113119.CrossRefGoogle ScholarPubMed
Greer, DM, Yang, J, Scripko, PD, et al. Clinical examination for prognostication in comatose cardiac arrest patients. Resuscitation 2013;84(11):1546–51.CrossRefGoogle ScholarPubMed
Geocadin, RG, Callaway, CW, Fink, EL, et al. Standards for studies of neurological prognostication in comatose survivors of cardiac arrest: a scientific statement from the American Heart Association. Circulation 2019;140(9):e517e542.CrossRefGoogle ScholarPubMed
Lorusso, R. Extracorporeal life support and neurologic complications: still a long way to go. J Thorac Dis 2017;9(10):e954e956.CrossRefGoogle Scholar
Aaslid, R, Markwalder, TM, Nornes, H. Noninvasive transcranial Doppler ultrasound recording of flow velocity in basal cerebral arteries. J Neurosurg 1982;57(6):769–74.CrossRefGoogle ScholarPubMed
Yang, Q, Tong, X, Schieb, L, et al. Vital signs: recent trends in stroke death rates – United States, 2000–2015. MMWR Morbid Mortal Wkly Rep 2017;66(35):933–9.Google ScholarPubMed
Centers for Disease Control and Prevention. Surveillance report of traumatic brain injury-related emergency department visits, hospitalizations, and deaths – United States, 2014. US Department of Health and Human Services, 2019. Available at: www.cdc.gov/traumaticbraininjury/pdf/TBI-Surveillance-Report-FINAL_508.pdf.Google Scholar
Sinha, N, Parnia, S. Monitoring the brain after cardiac arrest: a new era. Curr Neurol Neurosci Rep 2017;17(8):62.CrossRefGoogle ScholarPubMed
Lim, C, Alexander, MP, LaFleche, G, Schnyer, DM, Verfaellie, M. The neurological and cognitive sequelae of cardiac arrest. Neurology 2004;63(10):1774–8.CrossRefGoogle ScholarPubMed
Benjamin, EJ, Blaha, MJ, Chiuve, SE, et al. Heart disease and stroke statistics-2017 update: a report from the American Heart Association. Circulation 2017;135(10):e146e603.CrossRefGoogle ScholarPubMed
Howard, G, Goff, DC. Population shifts and the future of stroke: forecasts of the future burden of stroke. Ann N Y Acad Sci 2012;1268:1420.CrossRefGoogle ScholarPubMed
Ovbiagele, B, Goldstein, LB, Higashida, RT, et al. Forecasting the future of stroke in the United States: a policy statement from the American Heart Association and American Stroke Association. Stroke 2013;44(8):2361–75.CrossRefGoogle ScholarPubMed
Macdonald, RL, Schweizer, TA. Spontaneous subarachnoid haemorrhage. Lancet 2017;389(10069):655–66.CrossRefGoogle ScholarPubMed
Findlay, JM, Nisar, J, Darsaut, T. Cerebral vasospasm: a review. Can J Neurol Sci 2016 43(1):1532.CrossRefGoogle ScholarPubMed
Munoz-Sanchez, MA, Murillo-Cabezas, F, Egea-Guerrero, JJ, et al. [Emergency transcranial doppler ultrasound: predictive value for the development of symptomatic vasospasm in spontaneous subarachnoid hemorrhage in patients in good neurological condition]. Med Intensiva 2012;36(9):611–18.Google ScholarPubMed
Mascia, L, Fedorko, L, terBrugge, K, et al. The accuracy of transcranial Doppler to detect vasospasm in patients with aneurysmal subarachnoid hemorrhage. Intensive Care Med 2003;29(7):1088–94.CrossRefGoogle ScholarPubMed
Rigamonti, A, Ackery, A, Baker, AJ. Transcranial Doppler monitoring in subarachnoid hemorrhage: a critical tool in critical care. Can J Anaesth 2008;55(2):112–23.CrossRefGoogle ScholarPubMed
Lau, VI, Arntfield, RT. Point-of-care transcranial Doppler by intensivists. Crit Ultrasound J 2017;9(1):21.CrossRefGoogle ScholarPubMed
Connolly, ES Jr, Rabinstein, AA, Carhuapoma, JR, et al. Guidelines for the management of aneurysmal subarachnoid hemorrhage: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2012;43(6):1711–37.CrossRefGoogle ScholarPubMed
Kumar, G, Shahripour, RB, Harrigan, MR. Vasospasm on transcranial Doppler is predictive of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis. J Neurosurg 2016;124(5):1257–64.CrossRefGoogle ScholarPubMed
Schmidt, B, Gunawardene, M, Krieg, D, et al. A prospective randomized single-center study on the risk of asymptomatic cerebral lesions comparing irrigated radiofrequency current ablation with the cryoballoon and the laser balloon. J Cardiovasc Electrophysiol 2013;24(8):869–74.CrossRefGoogle Scholar
Lang, EW, Diehl, RR, Mehdorn, HM. Cerebral autoregulation testing after aneurysmal subarachnoid hemorrhage: the phase relationship between arterial blood pressure and cerebral blood flow velocity. Crit Care Med 2001;29(1):158–63.CrossRefGoogle ScholarPubMed
D’Andrea, A, Conte, M, Scarafile, R, et al. Transcranial Doppler ultrasound: physical principles and principal applications in neurocritical care unit. J Cardiovasc Echogr 2016;26(2):2841.CrossRefGoogle ScholarPubMed
Pinto, VL, Tadi, P, Adeyinka, A. Increased Intracranial Pressure. StatPearls. Treasure Island (FL): StatPearls Publishing, 2020.Google Scholar
Marinoni, M, Cianchi, G, Trapani, S, et al. Retrospective analysis of transcranial doppler patterns in veno-arterial extracorporeal membrane oxygenation patients: feasibility of cerebral circulatory arrest diagnosis. Asaio J 2018;64(2):175–82.CrossRefGoogle ScholarPubMed
Ducrocq, X, Hassler, W, Moritake, K, et al. Consensus opinion on diagnosis of cerebral circulatory arrest using Doppler-sonography: task force group on cerebral death of the Neurosonology Research Group of the World Federation of Neurology. J Neurol Sci 1998;159(2):145–50.CrossRefGoogle Scholar
Chang, JJ, Tsivgoulis, G, Katsanos, AH, Malkoff, MD, Alexandrov, AV. Diagnostic accuracy of transcranial Doppler for brain death confirmation: systematic review and meta-analysis. Am J Neuroradiol 2016;37(3):408–14.CrossRefGoogle ScholarPubMed
Xu, B, Qiao, Q, Chen, M, et al. Relationship between neurological complications, cerebrovascular and cerebral perfusion following off-pump coronary artery bypass grafting. Neurol Res 2015;37(5):421–6.CrossRefGoogle ScholarPubMed
van Dijk, D, Spoor, M, Hijman, R, et al. Cognitive and cardiac outcomes 5 years after off-pump vs on-pump coronary artery bypass graft surgery. JAMA 2007;297(7):701–8.CrossRefGoogle ScholarPubMed
Palmerini, T, Savini, C, Di Eusanio, M. Risks of stroke after coronary artery bypass graft – recent insights and perspectives. Interv Cardiol 2014;9(2):7783.CrossRefGoogle Scholar
Thudium, M, Heinze, I, Ellerkmann, RK, Hilbert, T. Cerebral function and perfusion during cardiopulmonary bypass: a plea for a multimodal monitoring approach. Heart Surg Forum 2018;21(1):E028E035.CrossRefGoogle ScholarPubMed
Bismuth, J, Garami, Z, Anaya-Ayala, JE, et al. Transcranial Doppler findings during thoracic endovascular aortic repair. J Vasc Surg 2011;54(2):364–9.CrossRefGoogle ScholarPubMed
Russell, D, Brucher, R. Embolus detection and differentiation using multifrequency transcranial Doppler. Stroke 2006;37(2):340–1; author reply 341–2.CrossRefGoogle ScholarPubMed
Dittrich, R, Ringelstein, EB. Occurrence and clinical impact of microembolic signals (MES) in patients with chronic cardiac diseases and atheroaortic plaques–a systematic review. Curr Vasc Pharmacol 2008;6(4):329–34.CrossRefGoogle ScholarPubMed
Melmed, KR, Schlick, KH, Rinsky, B, et al. Assessing cerebrovascular hemodynamics using transcranial Doppler in patients with mechanical circulatory support devices. J Neuroimaging 2020;30(3):297302.CrossRefGoogle ScholarPubMed
Salna, M, Ikegami, H, Willey, JZ, et al. Transcranial Doppler is an effective method in assessing cerebral blood flow patterns during peripheral venoarterial extracorporeal membrane oxygenation. J Card Surg 2019;34(6):447–52.CrossRefGoogle ScholarPubMed
O’Brien, NF, Hall, MW. Extracorporeal membrane oxygenation and cerebral blood flow velocity in children. Pediatr Crit Care Med 2013;14(3):e126–34.CrossRefGoogle ScholarPubMed
Bazan, R, Luvizutto, GJ, Braga, GP, et al. Relationship of spontaneous microembolic signals to risk stratification, recurrence, severity, and mortality of ischemic stroke: a prospective study. Ultrasound J 2020;12(1):6.CrossRefGoogle ScholarPubMed
Dahl, A, Lindegaard, KF, Russell, D, et al. A comparison of transcranial Doppler and cerebral blood flow studies to assess cerebral vasoreactivity. Stroke 1992;23(1):1519.CrossRefGoogle ScholarPubMed
Wolf, ME. Functional TCD: regulation of cerebral hemodynamics – cerebral autoregulation, vasomotor reactivity, and neurovascular coupling. Front Neurol Neurosci 2015;36:4056.CrossRefGoogle ScholarPubMed
Silvestrini, M, Vernieri, F, Pasqualetti, P, et al. Impaired cerebral vasoreactivity and risk of stroke in patients with asymptomatic carotid artery stenosis. JAMA 2000;283(16):2122–7.CrossRefGoogle ScholarPubMed
Yonas, H, Smith, HA, Durham, SR, Pentheny, SL, Johnson, DW. Increased stroke risk predicted by compromised cerebral blood flow reactivity. J Neurosurg 1993;79(4):483–9.CrossRefGoogle ScholarPubMed
Gur, AY, Bova, I, Bornstein, NM. Is impaired cerebral vasomotor reactivity a predictive factor of stroke in asymptomatic patients? Stroke 1996;27(12):2188–90.CrossRefGoogle ScholarPubMed
Silvestrini, M, Viticchi, G, Falsetti, L, et al. The role of carotid atherosclerosis in Alzheimer’s disease progression. J Alzheimers Dis 2011;25(4):719–26.CrossRefGoogle ScholarPubMed
Wessels, T, Harrer, JU, Jacke, C, Janssens, U, Klotzsch, C. The prognostic value of early transcranial Doppler ultrasound following cardiopulmonary resuscitation. Ultrasound Med Biol 2006;32(12):1845–51.CrossRefGoogle ScholarPubMed
Rafi, S, Tadie, JM, Gacouin, A, et al. Doppler sonography of cerebral blood flow for early prognostication after out-of-hospital cardiac arrest: DOTAC study. Resuscitation 2019;141: 188–94.CrossRefGoogle ScholarPubMed
Lemiale, V, Huet, O, Vigue, B, et al. Changes in cerebral blood flow and oxygen extraction during post-resuscitation syndrome. Resuscitation 2008;76(1):1724.CrossRefGoogle ScholarPubMed
Blumenstein, J, Kempfert, J, Walther, T, et al. Cerebral flow pattern monitoring by transcranial Doppler during cardiopulmonary resuscitation. Anaesth Intensive Care 2010;38(2):376–80.CrossRefGoogle ScholarPubMed
Lewis, LM, Gomez, CR, Ruoff, BE, et al. Transcranial Doppler determination of cerebral perfusion in patients undergoing CPR: methodology and preliminary findings. Ann Emerg Med 1990;19(10):1148–51.CrossRefGoogle ScholarPubMed
Lin, JJ, Hsia, SH, Wang, HS, Chiang, MC, Lin, KL. Transcranial Doppler ultrasound in therapeutic hypothermia for children after resuscitation. Resuscitation 2015;89: 182–7.CrossRefGoogle ScholarPubMed
Baghshomali, S, Reynolds, P, Sarwal, A. Transcranial doppler to assess cerebral blood flow in patients on extra corporeal membrane oxygenation (P4.236). Neurology 2014;82(10 Supplement):236.CrossRefGoogle Scholar
Bouzat, P, Oddo, M, Payen, JF. Transcranial Doppler after traumatic brain injury: is there a role? Curr Opin Crit Care 2014;20(2):153160.CrossRefGoogle ScholarPubMed
Czosnyka, M, Smielewski, P, Kirkpatrick, P, Menon, DK, Pickard, JD. Monitoring of cerebral autoregulation in head-injured patients. Stroke 1996;27(10):1829–34.CrossRefGoogle ScholarPubMed
Coles, JP. Regional ischemia after head injury. Curr Opin Crit Care 2004;10(2):120–5.CrossRefGoogle ScholarPubMed
Rosner, MJ, Daughton, S. Cerebral perfusion pressure management in head injury. J Trauma 1990;30(8):933–40.CrossRefGoogle ScholarPubMed
Jaggi, JL, Obrist, WD, Gennarelli, TA, Langfitt, TW. Relationship of early cerebral blood flow and metabolism to outcome in acute head injury. J Neurosurg 1990;72(2):176–82.CrossRefGoogle ScholarPubMed
van Santbrink, H, Schouten, JW, Steyerberg, EW, Avezaat, CJ, Maas, AI. Serial transcranial Doppler measurements in traumatic brain injury with special focus on the early posttraumatic period. Acta Neurochir (Wien) 2002;144(11):1141–9.CrossRefGoogle ScholarPubMed
Zurynski, YA, Dorsch, NW, Fearnside, MR. Incidence and effects of increased cerebral blood flow velocity after severe head injury: a transcranial Doppler ultrasound study II. Effect of vasospasm and hyperemia on outcome. J Neurol Sci 1995;134(1-2):41–6.Google ScholarPubMed
Jaffres, P, Brun, J, Declety, P, et al. Transcranial Doppler to detect on admission patients at risk for neurological deterioration following mild and moderate brain trauma. Intensive Care Med 2005;31(6):785–90.CrossRefGoogle ScholarPubMed
Camerlingo, M L Casto, B Censori, MC, et al. Prognostic use of ultrasonography in acute non-hemorrhagic carotid stroke. Ital J Neurol Sci 1996;17(3):215–18.CrossRefGoogle ScholarPubMed
D’Andrea, A, Conte, M, Scarafile, R, et al. Transcranial Doppler ultrasound: physical principles and principal applications in neurocritical care unit. J Cardiovasc Echogr 2016;26(2):2841.CrossRefGoogle ScholarPubMed
Stolz, E, Cioli, F, Allendoerfer, J, et al. Can early neurosonology predict outcome in acute stroke?: a metaanalysis of prognostic clinical effect sizes related to the vascular status. Stroke 2008;39(12):3255–61.CrossRefGoogle Scholar
Alexandrov, AV, Grotta, JC. Arterial reocclusion in stroke patients treated with intravenous tissue plasminogen activator. Neurology 2002;59(6):862–7.CrossRefGoogle ScholarPubMed
Powers, WJ, Rabinstein, AA, Ackerson, T, et al. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 Guidelines for the Early Management of Acute Ischemic Stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2019;50(12):e344e418.CrossRefGoogle Scholar
Nogueira, RG, Jadhav, AP, Haussen, DC, et al. Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med 2018;378(1):1121.CrossRefGoogle ScholarPubMed
Goyal, M, Demchuk, AM, Menon, BK, et al. Randomized assessment of rapid endovascular treatment of ischemic stroke. N Engl J Med 2015;372(11):1019–30.CrossRefGoogle ScholarPubMed
Hong, L, Cheng, X, Lin, L, et al. The blood pressure paradox in acute ischemic stroke. Ann Neurol 2019;85(3):331–9.CrossRefGoogle ScholarPubMed
Gomez, J, Wolfe, S, Sarwal, A. Sonographic demonstration of a perfusion-dependent stroke with negative MRI and a flow-limiting stenosis. Neurocrit Care 2019;32(3):883–8.Google Scholar
Silvestrini, M, Troisi, E, Matteis, M, Razzano, C, Caltagirone, C. Correlations of flow velocity changes during mental activity and recovery from aphasia in ischemic stroke. Neurology 1998;50(1):191–5.CrossRefGoogle ScholarPubMed
Carney, N, Totten, AM, O’Reilly, C, et al. Guidelines for the Management of Severe Traumatic Brain Injury, Fourth Edition. Neurosurgery 2017;80(1):615.CrossRefGoogle ScholarPubMed
Kumar, G, Alexandrov, AV. Vasospasm surveillance with transcranial doppler sonography in subarachnoid hemorrhage. J Ultrasound Med 2015;34(8):1345–50.CrossRefGoogle ScholarPubMed

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