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Young MJ, Awad A, Andreev A, Bonkhoff AK, Schirmer MD, Dmytriw AA, Vranic JE, Rabinov JD, Doron O, Stapleton CJ, Das AS, Edlow BL, Singhal AB, Rost NS, Patel AB, Regenhardt RW. Characterizing coma in large vessel occlusion stroke. J Neurol 2024; 271:2658-2661. [PMID: 38366071 DOI: 10.1007/s00415-024-12199-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/07/2024] [Accepted: 01/14/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Coma is an unresponsive state of disordered consciousness characterized by impaired arousal and awareness. The epidemiology and pathophysiology of coma in ischemic stroke has been underexplored. We sought to characterize the incidence and clinical features of coma as a presentation of large vessel occlusion (LVO) stroke. METHODS Individuals who presented with LVO were retrospectively identified from July 2018 to December 2020. Coma was defined as an unresponsive state of impaired arousal and awareness, operationalized as a score of 3 on NIHSS item 1a. RESULTS 28/637 (4.4%) patients with LVO stroke were identified as presenting with coma. The median NIHSS was 32 (IQR 29-34) for those with coma versus 11 (5-18) for those without (p < 0.0001). In coma, occlusion locations included basilar (13), vertebral (2), internal carotid (5), and middle cerebral (9) arteries. 8/28 were treated with endovascular thrombectomy (EVT), and 20/28 died during the admission. 65% of patients not treated with EVT had delayed presentations or large established infarcts. In models accounting for pre-stroke mRS, basilar occlusion location, intravenous thrombolysis, and EVT, coma independently increased the odds of transitioning to comfort care during admission (aOR 6.75; 95% CI 2.87,15.84; p < 0.001) and decreased the odds of 90-day mRS 0-2 (aOR 0.12; 95% CI 0.03,0.55; p = 0.007). CONCLUSIONS It is not uncommon for patients with LVO to present with coma, and delayed recognition of LVO can lead to poor outcomes, emphasizing the need for maintaining a high index of suspicion. While more commonly thought to result from posterior LVO, coma in our cohort was similarly likely to result from anterior LVO. Efforts to improve early diagnosis and care of patients with LVO presenting with coma are crucial.
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Affiliation(s)
- Michael J Young
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 101 Merrimac Street, Suite 310, Boston, MA, 02114, USA.
| | - Amine Awad
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 101 Merrimac Street, Suite 310, Boston, MA, 02114, USA
| | - Alexander Andreev
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Anna K Bonkhoff
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 101 Merrimac Street, Suite 310, Boston, MA, 02114, USA
| | - Markus D Schirmer
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 101 Merrimac Street, Suite 310, Boston, MA, 02114, USA
| | - Adam A Dmytriw
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, USA
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Justin E Vranic
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, USA
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - James D Rabinov
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, USA
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Omer Doron
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Christopher J Stapleton
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Alvin S Das
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 101 Merrimac Street, Suite 310, Boston, MA, 02114, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Brian L Edlow
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 101 Merrimac Street, Suite 310, Boston, MA, 02114, USA
| | - Aneesh B Singhal
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 101 Merrimac Street, Suite 310, Boston, MA, 02114, USA
| | - Natalia S Rost
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 101 Merrimac Street, Suite 310, Boston, MA, 02114, USA
| | - Aman B Patel
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Robert W Regenhardt
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 101 Merrimac Street, Suite 310, Boston, MA, 02114, USA
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, USA
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Abstract
Neuroprognostication following acute brain injury (ABI) is a complex process that involves integrating vast amounts of information to predict a patient's likely trajectory of neurologic recovery. In this setting, critically evaluating salient ethical questions is imperative, and the implications often inform high-stakes conversations about the continuation, limitation, or withdrawal of life-sustaining therapy. While neuroprognostication is central to these clinical "life-or-death" decisions, the ethical underpinnings of neuroprognostication itself have been underexplored for patients with ABI. In this article, we discuss the ethical challenges of individualized neuroprognostication including parsing and communicating its inherent uncertainty to surrogate decision-makers. We also explore the population-based ethical considerations that arise in the context of heterogenous prognostication practices. Finally, we examine the emergence of artificial intelligence-aided neuroprognostication, proposing an ethical framework relevant to both modern and longstanding prognostic tools.
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Affiliation(s)
- India A Lissak
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Brian L Edlow
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Eric Rosenthal
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Michael J Young
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
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