Venous thromboembolism prophylaxis in patients with intracranial hemorrhage: a systematic review of considerations for neurosurgical management
Background: Patients with intracranial hemorrhage (ICH), whether traumatic brain injury (TBI)-induced or spontaneous, present a significant challenge for neurosurgeons, who must carefully balance the risks of both venous thromboembolism (VTE) and hemorrhagic progression. Methods: To summa...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Academia.edu Journals
2025-03-01
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| Series: | Academia Medicine |
| Online Access: | https://www.academia.edu/128324514/Venous_thromboembolism_prophylaxis_in_patients_with_intracranial_hemorrhage_a_systematic_review_of_considerations_for_neurosurgical_management |
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| Summary: | Background: Patients with intracranial hemorrhage (ICH), whether traumatic brain injury (TBI)-induced or spontaneous, present a significant challenge for neurosurgeons, who must carefully balance the risks of both venous thromboembolism (VTE) and hemorrhagic progression. Methods: To summarize considerations of ICH and VTE prophylaxis in a neurosurgical setting, a systematic search of original research was run in PubMed, Embase, Scopus, and Web of Science. Results: Of the 257 articles reviewed, 43 were included. Only four randomized controlled trials (RCTs) were identified, as a majority of the articles were cohort studies. Discussion: LMWH is generally preferred over UH for its efficacy and safety, but UH’s greater reversibility creates ambiguity, especially in unstable patients. Few studies have explored DOACs and AVKs in neurosurgery, particularly in balancing VTE and ICH risks. Beyond drug type, dosing schedules also matter, though standardization is often not feasible, such as with drug initiation timing post-injury. In such cases, quantitative visuals aid interpretation. Monitoring tools (implantable devices or bioassays) and computational algorithms (regression models) help neurosurgical teams navigate complex decisions. Advanced machine learning algorithms with model transparency offers further advancements. |
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| ISSN: | 2994-435X |