Optimizing post-operative imaging: a retrospective cohort study comparing two methods of lateral hip radiography after cephalomedullary nail surgery

Abstract

Abstract Background Currently, there is no consensus on the most appropriate technique for obtaining lateral hip radiographs after cephalomedullary nail (CMN) surgery. The aim of this study was to investigate the distribution of two commonly used postoperative lateral hip radiographic methods (classic lateral view and modified lateral view) and try to find out which one is better suited for this situation. Methods A retrospective analysis was conducted on 146 patients who underwent surgical fixation for extracapsular hip fractures between January 2018 and June 2022. The main outcome measured was the angle between the straight part of the CMN and the lag screw/blade on hip lateral X-rays (CMNA). The lateral hip radiographs were categorized into two groups based on different lateral hip radiographic methods. CMNA, patient age, gender, fracture classification based on the 2018 AO classification, nail length (short/long), surgical side (left/right), height, weight, BMI, preoperative waiting time, postoperative imaging interval were collected and compared between the two groups. Results The distribution trend of CMNA significantly differs between two types of hip joint lateral radiographic methods. Specifically, the classic lateral method exhibits a significantly bimodal and skewed distribution with a median (p25, p75) of -21.6° (-31.2°, -8°), whereas the modified lateral method presents a normal distribution with a mean ± SD of +7.57° ± 14.4°. The difference in the Mean Rank between the classic (47.10) and the modified (102.96) lateral methods is statistically significant (P < 0.001). Conclusions The CMNA method is an excellent tool for studying the lateral distribution.We recommend using the modified lateral view as the preferred option for obtaining lateral hip radiographs after CMN surgery due to its superior distribution of CMNA and greater patient-friendliness.