Prediction of implant failure risk due to periprosthetic femoral fracture after primary elective total hip arthroplasty

Listen to Simon and Amy discuss the paper 'Prediction of implant failure risk due to periprosthetic femoral fracture after primary elective total hip arthroplasty' published in the January 2025 issue of Bone & Joint Research.

Click here to read the paper.

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Transcript

[00:00:00] Welcome back to another episode of AI Talks with Bone & Joint from the publishers of Bone & Joint Research. Today, we're discussing the paper 'Prediction of implant failure risk due to periprosthetic femoral fracture after primary elective total hip arthroplasty' published in January 2025 by Abdulhadi Alagha, J. Cobb, A. Liddle, H. Malchau, O. Rolfson, and M. Mohaddes. 

I'm Simon, and I'm joined by my co-host, Amy. 

Hello, Simon. This paper is quite fascinating as it delves into predicting periprosthetic femoral fractures, or PPFFs, which are a significant complication post total hip arthroplasty, or THA. So, Simon, why was this research carried out?

Well, Amy, while cementless fixation can offer benefits like shorter surgery times, there's a concern about its higher cost and the increased risk of PPFFs requiring revision surgeries. Predicting and potentially reducing these fractures could greatly improve patient outcomes. 

Given the rising number of [00:01:00] THA surgeries globally, accurately predicting these risks is crucial. The study aimed to develop and validate machine learning models to forecast the risk of PPFF post surgery. 

They used a dataset of 154,519 primary elective THAs from the Swedish Arthroplasty Register from 2008 to 2018. They analysed 21 features covering patient-specific, surgical-, and implant-specific factors.

That's quite a substantial dataset. The machine learning models included random forest, gradient boosting machine, penalized logistic regression and classification tree models, among others. These were compared to traditional logistic regression models. One key takeaway is that gradient boosting machine, GBM models, consistently performed the best at multiple time points 30 days, 60 days, 90 days and one year.

For instance, the GBM achieved an AUC of 0.86 for 60-day revision predictions, marginally outperforming the others. [00:02:00] They found that certain patient features like increasing age, higher American Society of Anesthesiologists or ASA grades and higher BMI categories significantly elevated risks. Additionally, implant-specific features like cementless femoral fixation and the size of femoral heads were important. 

Yes, those findings are crucial. For example, idiopathic necrosis increases the revision risk, and both extremely small and large femoral head sizes. were significant factors for higher risks. Their analysis showed that machine learning models have better predictive capabilities than traditional statistical methods. 

A key strength of this study is its use of a large, comprehensive national database which adds robustness to their findings. But there are limitations too, aren't there Simon? 

Yes. The study's retrospective nature, relying on a single national registry and missing data for pre-2008 ASA grades could limit its generalizability. Also, using revision as an endpoint might [00:03:00] not fully capture all scenarios of interest. Despite these, the study provides a solid foundation for future research and potential clinical applications.

It's evident that the predictive power of machine learning algorithms holds great promise in orthopaedic surgery. By integrating these models, surgeons can make more informed decisions about implant choices and patient management strategies. 

Absolutely, Amy. To summarise, the key insights are that machine learning models showed superior predictive accuracy for PPFF risk after THA, and patient-specific features and implant designs are critical risk factors. As you mentioned, future research could further validate these findings and explore broader applications. 

Indeed, Simon, this is a pivotal study with significant potential impacts on clinical practice. Thanks to everyone for tuning in. We'll be back next time to discuss more cutting edge research in bone and joint health.