Complications following intramedullary nailing of proximal humerus and humeral shaft fractures: a systematic review

Hypothesis: The purpose of this study was to systematically review complications arising from intramedullary nailing (IMN) of proximal and humeral shaft fractures. This study hypothesized that there would be a low rate of complications and revision among patients treated with IMN for humerus fractures. Methods: Two independent reviewers performed a literature search in the PubMed database based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies were included if they reported on outcomes following the use of intramedullary nails for proximal humerus fractures or humeral shaft fractures. Variables that were collected included complications, visual analog scale pain scores and revision operations. Results: Overall, 179 studies met the inclusion criteria, with 7984 shoulders. The average age of patients in this study was 55.2 years and 60.7% of patients were female. The mean follow-up was 16.6 months. The overall complication rate for all fractures treated with intramedullary nails was 18.9%, and the overall revision rate was 6.8%. Among the complications were fracture complications (7.5%), hardware complications (7.2%), soft tissue complications (1.8%), neurovascular complications (1.6%), and infection (0.8%). Four-part proximal humerus fractures (52.9%) and open fractures (36.7%) had the highest rates of complication. Among the reasons for revision were hardware removal or replacement (5.0%), conversion to arthroplasty (0.6%), and other (1.2%). The mean visual analog scale pain score at last follow-up was 1.6. Conclusion: Overall, there was a moderate rate of complications but low rate of revision following IMN of humerus fractures. Open fractures and 4-part proximal humerus fractures had the highest complication rates. Level of evidence: Level IV; Systematic Review (c) 2024 Journal of Shoulder and Elbow Surgery Board of Trustees. All rights are reserved, including those for text and data mining, AI training, and similar technologies.