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Review demonstrates hyaluronidase improves pain relief in spinal interventions, suggesting enhanced outcomes.
Hyaluronidase is an enzyme that degrades hyaluronic acid within the extracellular matrix, thereby increasing tissue permeability and facilitating the dispersion of injected agents. Hyaluronidase has been used as an adjunct in spinal interventions to improve drug delivery, enhance adhesiolysis, and overcome therapeutic limitations imposed by epidural fibrosis and scar tissue. This review summarizes the pharmacological properties, clinical evidence, safety considerations, and practical applications of hyaluronidase in spinal pain management.The available evidence suggests that hyaluronidase enhances the distribution of local anesthetics, corticosteroids, and other therapeutic agents within fibrotic or compartmentalized tissues. Clinical studies have demonstrated improved pain relief and functional outcomes when hyaluronidase was incorporated into lumbar interlaminar, caudal, and transforaminal epidural injections, particularly in patients with failed back surgery syndrome. However, evidence regarding its role in percutaneous epidural neuroplasty and adhesiolysis remains inconsistent, with some studies reporting sustained benefits, whereas others have demonstrated significant independent effects beyond mechanical adhesiolysis. In trigger point injections for myofascial pain syndrome, hyaluronidase has been associated with a faster onset and longer duration of analgesia. Although generally well-tolerated, rare hypersensitivity reactions, including anaphylaxis, have been reported, necessitating careful patient selection and monitoring.Current evidence supports the use of hyaluronidase as a valuable adjunct rather than a stand-alone therapeutic option in selected spinal interventions. Future well-designed prospective studies are needed to clarify its independent therapeutic contribution, optimize dosing strategies, and establish evidence-based clinical guidelines for its use in interventional pain management.
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Jae Hun Kim (2026) studied this question.
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