Abstract
Introduction
IM (Intra Medullary) nail fixation is the standard treatment for diaphyseal femur fractures and also for certain types of proximal and distal femur fractures. Despite the advances in the tribology for the same, cases of failed IM nail fixation continue to be encountered routinely in clinical practice. Common causes are poor alignment or reduction, insufficient fixation and eventual implant fatigue and failure. This study was devised to study such patients presenting to our practice and develop a predictive model for eventual failure.
Materials and Methods
57 patients who presented with failure of IM nail fixation (± infection) between Jan 2011 – Jun 2020 were included in the study and hospital records and imaging reviewed. Those fixed with any other kinds of metalwork were excluded. Classification for failure of IM nails – Type 1: Failure with loss of contact of lag screw threads in the head due to backing out and then rotational instability, Type 2A: Failure of the nail at the nail and lag screw junction, Type 2B: Failure of the screws at the nail lag screw junction, Type 3: Loosening at the distal locking sites with or without infection. X-rays reviewed and causes/site of failure noted.
Results
Total patients - 57. Demography - Average age - 58.9 years, 22 Males and 35 females. Eleven patients were noted to have an infection at the fracture site that needed oral or IV antibiotics.16 patients - at least 1 cerclage wire for fracture reduction and fixation + IM Nail. Subtrochanteric fractures (42/57) were the most common to fail. In those fractures with postero-medial comminution, locking of the lag screw in position thus preventing backout can prevent failure. In type 2 failures, preventing varus fixation by early open reduction and temporary fixation with plates and screws can achieve improved results. Those with type 3 failures with periosteal reaction should be considered to be infected until proven otherwise.
Conclusions
This classification for failure of IM nails in the femur can be used as a predictive model for failures and allow early recognition and intervention to tackle them.
