Enzymatic Management of Facial Overfilled Syndrome: A Case Series and Narrative Review

面部过度填充综合征的酶疗法:病例系列及叙述性综述

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Abstract

BACKGROUND: Facial overfilled syndrome (FOS) is an increasingly recognized complication of injectable aesthetic procedures, characterized by undesirable facial volume and contour irregularities. While hyaluronidase is well-established for managing hyaluronic acid (HA) filler complications, treating FOS caused by non-HA fillers, such as fat and silicone, remains challenging. OBJECTIVE: This study aims to present the results of a novel enzymatic therapy combining hyaluronidase, collagenase, and lipase for treating FOS in five patients with complications from HA, fat, and silicone fillers. Additionally, we conducted a narrative literature review of studies published between 2015 and 2024, focusing on FOS and its management. METHODS: We performed a narrative literature review using PubMed and Google Scholar databases, identifying six key papers that specifically address FOS. Following this, we present a case series of five patients treated with a combination of 1.5 mL collagenase, 1.5 mL hyaluronidase (HASA), and 1.5 mL lipase (total volume: 4.5 mL). Enzymatic therapy was administered via cannula under ultrasound guidance, and outcomes were assessed through clinical evaluation and patient-reported satisfaction. RESULTS: All five patients significantly improved facial volume and contour restoration after a single treatment session. The enzymatic combination effectively addressed complications from HA, fat, and silicone fillers, with no significant adverse events reported. Patient satisfaction improved significantly, highlighting the psychological benefits of the treatment. CONCLUSION: Combining hyaluronidase, collagenase, and lipase represents a safe and effective approach for managing FOS caused by various filler types. Our study, supported by a narrative review of six key papers on FOS, underscores the importance of tailored enzymatic therapies in aesthetic medicine and calls for further research to optimize treatment protocols and expand the evidence base.

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