ReviewMaxillofacial plastic and reconstructive surgery2025
Management and outcomes of facial nerve injury following rhytidectomy: a systematic review.
Review in Maxillofacial plastic and reconstructive surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
4 authors.
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Abstract
BACKGROUND AND
aimFacial nerve injury is a critical complication of rhytidectomy, affecting patient outcomes and satisfaction. Despite its importance, standardized management strategies remain limited. This systematic review evaluates current evidence on the management, outcomes, and prevention of facial nerve injuries in rhytidectomy, with stratification by injury severity to enhance clinical applicability.
methodsIn this study, PubMed, Embase, and the Cochrane Library were searched from inception to July 2025, identifying 20 studies that met the inclusion criteria. The quality of the studies was assessed using AMSTAR 2 and the Newcastle-Ottawa Scale. Additionally, the review was conducted in accordance with the PRISMA guidelines to ensure transparency and accuracy in reporting the results.
resultsThe incidence of facial nerve injury ranged from 0.5% to 5%, with 70% of patients achieving full recovery within six months through conservative treatments (corticosteroids, physiotherapy). Management and outcomes varied by injury severity: neuropraxia (80-90% of cases) typically resolved conservatively, while axonotmesis or neurotmesis required surgical interventions (e.g., nerve repair) or adjunct therapies (e.g., botulinum toxin). Preventive measures, such as meticulous surgical techniques and awareness of facial danger zones, were effective. Intraoperative nerve monitoring showed potential but needs further validation.
conclusionsConservative management suffices for most cases, particularly neuropraxia, yet 10% of patients experience persistent deficits, underscoring the need for severity-stratified approaches. Prospective multicenter registries with standardized outcome measures, individual patient data meta-analyses, and Bayesian hierarchical modeling are essential to address evidence gaps and enhance clinical practice.
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