Optimal Treatment of Digital Nerve Injury: A Systematic Review and Bayesian Network Meta-Analysis.

Abstract

Digital nerve injuries are the most prevalent form of peripheral nerve injuries. We aimed to conduct a systematic review and Bayesian network meta-analysis to determine the optimal technique for sensory restoration in patients undergoing digital nerve repair. An electronic search was conducted from inception to September 2025. Thirteen studies were included comparing neurorrhaphy, allograft, autograft, and conduit. Outcomes analyzed included static 2-point discrimination (S2PD) and moving 2-point discrimination (M2PD), presented as mean difference (MD) with 95% confidence intervals (CIs) or as surface under the cumulative ranking curve (SUCRA) scores and mean rank estimates. Sensitivity analyses were also performed by excluding neurorrhaphy. A P value <0.05 was considered statistically significant. The pooled analysis demonstrated that allograft was the best technique for S2PD (SUCRA: 70.39, mean rank: 1.89), whereas neurorrhaphy ranked highest for M2PD (SUCRA: 70.71, mean rank: 1.88). Traditional meta-analysis also confirmed allograft's superiority for S2PD (MD = -1.44, 95% CI: -2.57 to -0.32; P = 0.01) and neurorrhaphy for M2PD (MD = -0.50, 95% CI: -0.87 to -0.13; P = 0.007). However, sensitivity analyses excluding neurorrhaphy demonstrated that allograft also ranked highest for M2PD (SUCRA: 69.91, mean rank: 1.60). Conduit repair was significantly worse than other techniques for both outcomes (S2PD : P = 0.002; M2PD : P = 0.004). These findings highlight allograft as the most effective techniques for optimizing sensory recovery in digital nerve injuries, especially in cases where neurorrhaphy is not feasible.