Can Machine Learning Algorithms Contribute to the Initial Screening of Hip Prostheses and Early Identification of Outliers?

Abstract

Registries have significant roles in assessing the comparative performance of devices. Ideally, early identification of outliers should use a time-to-event outcome while reducing the confounding effects of other components in the device and patient characteristics. Machine learning (ML), which contains self-learning algorithms, is one approach to consider many variables simultaneously to reduce the impact of confounding. The principal objective of this study was to investigate the effectiveness of using either random survival forest (RSF) or regularised/unregularised Cox regression to account for patient and associated device confounding factors in comparison with current standard techniques. This study evaluated RSF and regularised/unregularised Cox regression using data from the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) to detect outlier devices among 213 individual primary total hip components performed in 163,356 primary procedures from 1 January 2015 to the end of 2019. Device components and patient characteristics were the inputs, and time to first revision surgery was the primary outcome treated as a censored case for death. The effectiveness of the ML approaches was assessed based on the ability to detect the outliers identified by the AOANJRR standard approach. In the study cohort, the standardised AOANJRR approach identified three acetabular components and seven femoral stems as outliers. The ML approaches identified some but not all the outliers detected by the AOANJRR. Both the methods identified three of the same femoral stems, and the RSF identified the other five components, including two of the same acetabular cups and three of the same femoral stems. In addition, both the RSF and Cox techniques detected a number of additional device components that were not previously identified by the standard approach. The results showed that ML may be able to offer a supplementary approach to enhance the early identification of outlier devices. Random survival forest was a more comparable technique to the AOANJRR standard than the Cox regression, but further studies are required to better understand the potential of ML to improve the early identification of outliers.

Keywords

• total hip replacement
• outlier
• AOANJRR
• machine learning
• random survival
• Cox