Processed nerve allograft for digital nerve reconstruction: A systematic review and pooled outcome analysis

Background: Digital nerve injuries can result in significant sensory impairment, and reconstruction of segmental defects remains challenging when tension-free primary repair is not feasible. Processed nerve allograft (PNA) provides a biological scaffold without donor-site morbidity; however, reported outcomes vary, and there is limited focused evidence for digital nerve reconstruction. This systematic review and pooled analysis evaluated sensory and functional outcomes following digital nerve reconstruction using PNA. Methods: PubMed, Embase, and Scopus were searched for studies reporting clinical outcomes of digital nerve reconstruction using PNA. Eligible studies included human digital nerve reconstructions with quantitative sensory outcomes such as static two-point discrimination (s2PD), moving two-point discrimination (m2PD), or meaningful sensory recovery. Data regarding demographics, defect characteristics, outcomes, and complications were extracted. Pooled analyses for continuous and proportional outcomes were performed using random-effects models. Results: Sixteen studies, with 664 patients and 886 allografts, demonstrated consistent sensory improvement after PNA reconstruction. Pooled s2PD averaged approximately 7-8 mm, while m2PD averaged 5-6 mm. Meaningful sensory recovery was achieved in over 80 % of reconstructions. Complication and infection rates were low, with no significant graft-related adverse events reported. Although clinical outcomes were favourable, notable heterogeneity was observed in study design, outcome measures, and follow-up duration. Conclusion: A processed nerve allograft provides generally favourable sensory recovery in most reported series, in digital nerve gap reconstruction, with low complication rates, and offers an effective alternative to autograft when primary repair is not possible. Standardised outcome measures and prospective comparative studies are needed to refine patient selection and define predictors of optimal recovery.