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Tytuł:
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Mortality prediction and long-term outcomes for civilian cerebral gunshot wounds: A decision-tree algorithm based on a single trauma center.
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Autorzy:
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Kim LH; Department of Neurosurgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Quon JL; Department of Neurosurgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA.
Cage TA; Department of Neurosurgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Santa Clara Valley Medical Center, 751 S Bascom Ave, San Jose, CA 95128, USA.
Lee MB; Department of Neurosurgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Santa Clara Valley Medical Center, 751 S Bascom Ave, San Jose, CA 95128, USA.
Pham L; Santa Clara Valley Medical Center, 751 S Bascom Ave, San Jose, CA 95128, USA.
Singh H; Department of Neurosurgery, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Santa Clara Valley Medical Center, 751 S Bascom Ave, San Jose, CA 95128, USA. Electronic address: .
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Źródło:
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Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia [J Clin Neurosci] 2020 May; Vol. 75, pp. 71-79. Date of Electronic Publication: 2020 Mar 30.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Publication: <2000->: Edinburgh : Churchill Livingstone
Original Publication: Melbourne, Vic., Australia : Churchill Livingstone, c1994-
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MeSH Terms:
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Algorithms*
Clinical Decision Rules*
Decision Trees*
Wounds, Gunshot/*mortality
Adult ; Brain Injuries/etiology ; Brain Injuries/mortality ; Brain Injuries/pathology ; Craniocerebral Trauma/etiology ; Craniocerebral Trauma/mortality ; Craniocerebral Trauma/pathology ; Female ; Humans ; Male ; Middle Aged ; Retrospective Studies ; Trauma Centers ; Wounds, Gunshot/complications ; Wounds, Gunshot/pathology
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Contributed Indexing:
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Keywords: Decision tree algorithm; Gunshot wound; Mortality outcome; Traumatic brain injury
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Entry Date(s):
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Date Created: 20200404 Date Completed: 20200916 Latest Revision: 20200916
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Update Code:
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20240105
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DOI:
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10.1016/j.jocn.2020.03.027
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PMID:
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32241644
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Gunshot wounds (GSW) are one of the most lethal forms of head trauma. The lack of clear guidelines for civilian GSW complicates surgical management. We aimed to develop a decision-tree algorithm for mortality prediction and report long-term outcomes on survivors based on 15-year data from our level 1 trauma center. We retrospectively reviewed 96 consecutive patients who presented with cerebral GSWs between 2003 and 2018. Clinical information from our trauma database, EMR, and relevant imaging scans was reviewed. A decision-tree model was constructed based on variables showing significant differences between survivors and non-survivors. After excluding patients who died at arrival, 54 patients with radiologically confirmed intracranial injury were included. Compared to survivors (51.9%), non-survivors (48.1%) were significantly more likely to have perforating (entry and exit wound), as opposed to penetrating (entry wound only), injuries. Bi-hemispheric and posterior fossa involvement, cerebral herniation, and intraventricular hemorrhage were more commonly present in non-survivors. Based on the decision-tree, Glasgow Coma Scale (GCS) > 8 and penetrating, uni-hemispheric injury predicted survival. Among patients with GCS ≤ 8 and normal pupillary response, lack of 1) posterior fossa involvement, 2) cerebral herniation, 3) bi-hemispheric injury, and 4) intraventricular hemorrhage, were associated with survival. Favorable long-term outcomes (mean follow-up 34.4 months) were possible for survivors who required neurosurgery and stable patients who were conservatively managed. We applied clinical and radiological characteristics that predicted survival to construct a decision-tree to facilitate surgical decision-making for GSW. Further validation of the algorithm in a large patient setting is recommended.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2020 Elsevier Ltd. All rights reserved.)
