The Impact of Leg Length and Offset Change on Dislocation Risk Following Primary Total Hip Arthroplasty.

INTRODUCTION: Soft tissue management in total hip arthroplasty (THA) includes appropriate restoration and/or alteration of leg length and offset to re-establish natural hip biomechanics. The purpose of this study was to evaluate the effect of leg length and offset-derived variables in a multivariable survival model for dislocation. METHODS: Clinical, surgical, and radiographic data was retrospectively acquired for 12,582 patients undergoing primary THA at a single institution from 1998 to 2018. There were twelve variables derived from preoperative and postoperative radiographs related to leg length and offset that were measured using a validated automated algorithm. These measurements, as well as other modifiable and non-modifiable surgical, clinical, and demographic factors, were used to determine hazard ratios (HR) for dislocation risk. RESULTS: None of the leg length or offset variables conferred significant risk or protective benefit for dislocation risk. By contrast, all other variables included in the multivariable model demonstrated a statistically significant effect on dislocation risk with a minimum effect size of 28% (range 0.72 to 1.54) (sex, surgical approach, acetabular liner type, femoral head size, neurologic disease, spine disease, and prior spine surgery). CONCLUSION: Contrary to traditional teaching and our hypothesis, operative changes in leg length and offset did not demonstrate any clinically or statistically significant effect in this large and well-characterized cohort. This does not imply that these variables are not important in individual cases, but rather suggests the overall impact of leg length and offset changes is relatively minor for dislocation risk compared to other variables that were found to be highly clinically and statistically significant in this population. These results may also suggest that surgeons do a good job of restoring native leg length and offset for patients, which may mitigate their analyzed impact.

A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence.

The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.