The anterior mini-invasive (MI) approach to performing total hip arthroplasty (THA) is associated with less soft tissue damage and shorter postoperative recovery than other methods. Our hospital recently abandoned the traditional lateral Hardinge (LH) approach in favour of this new method. We compared the first 100 patients operated after the changeover to the new method (MI group) to the last 100 patients operated using the traditional method (LH group). Clinical and radiological parameters and complications were recorded pre- and postoperatively and the collected data of the two groups were statistically compared. There were no statistically significant differences between either group with regard to patient demographics or procedural data, placement of the femur component, postoperative leg discrepancy, prosthesis dislocation, blood transfusion, or postoperative dislocation of the components. The MI group had a significantly longer operating time, more bleeding, higher rate of nerve damage, and a higher percentage of acetabular component malposition whilst having a significantly shorter hospital stay and significantly fewer infections of the operative site in comparison to the LH group. Additionally, and perhaps most worrying was the clinically significant increase in intraoperative femur fractures in the MI group. The changeover to the anterior mini-invasive approach, which was the surgeons' initial experience with the MI technique, resulted in a drastic increase in the number of overall complications accompanied by less soft tissue damage and a shorter period of rehabilitation. Our results suggest that further analysis of this surgical MI technique will be needed before it can be recommended for widespread adoption.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.