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Ballistic peripheral nerve injuries: basic concepts, controversies, and proposal for a management strategy.

Laurent MathieuMelody GoncalvesJames Charles MurisonGeorges PfisterChristophe OberlinZoubir Belkheyar
Published in: European journal of trauma and emergency surgery : official publication of the European Trauma Society (2022)
Ballistic injuries to peripheral nerves are devastating injuries frequently encountered in modern conflicts and civilian trauma centers. Such injuries often produce lifelong morbidity, mainly in the form of function loss and chronic pain. However, their surgical management still poses significant challenges concerning indication, timing, and type of repair, particularly when they are part of high-energy multi-tissue injuries. To help trauma surgeons, this article first presents basic ballistic concepts explaining different types of missile nerve lesions, described using the Sunderland classification, as well as their usual associated injuries. Current controversies regarding their surgical management are then described, including nerve exploration timing and neurolysis's relevance as a treatment option. Finally, based on anecdotal evidence and a literature review, a standardized management strategy for ballistic nerve injuries is proposed. This article emphasizes the importance of early nerve exploration and provides a detailed method for making a diagnosis in both acute and sub-acute periods. Direct suturing with joint flexion is strongly recommended for sciatic nerve defects and any nerve defect of limited size. Conversely, large defects require conventional nerve grafting, and proximal injuries may require nerve transfers, especially at the brachial plexus level. Additionally, combined or early secondary tendon transfers are helpful in certain injuries. Finally, ideal timing for nerve repair is proposed, based on the defect length, associated injuries, and risk of infection, which correlate intimately to the projectile velocity.
Keyphrases
  • peripheral nerve
  • chronic pain
  • liver failure
  • machine learning
  • intensive care unit
  • acute respiratory distress syndrome