Methodological Challenges in Predicting Periprosthetic Joint Infection Treatment Outcomes: A Narrative Review.
Elise Naufal, Marjan Wouthuyzen-Bakker, Sina Babazadeh and 3 others
PMID 36188976WHAT IT FOUND
Existing tools for predicting hip or knee infection outcomes are not ready for clinical use.
They were built on small samples, perform inconsistently when tested in new groups, and lack data on whether they actually help patients.
Key findings
01None of the most widely cited prediction tools for periprosthetic joint infection met standard sample size requirements, making them prone to overfitting.
02External validation of these tools has been rare and often based on small numbers of events, leading to potentially misleading estimates of accuracy.
03There is no evidence that using these prediction tools improves patient outcomes or reduces healthcare costs.
STILL TO COME
How it was doneWhat they found
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What it does not show
The review is narrative, not systematic, so the selection of the five tools may not represent all available options. The paper does not evaluate any new patients or interventions, so it cannot provide direct evidence on how to treat PJI. The assessment of model performance relies on published metrics, which may not fully capture real-world usability or clinical impact.
Declared interests
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The easy way to misread this
Do not assume these prediction tools are accurate or useful simply because they are widely cited. The review shows they were developed on insufficient data, perform poorly in new groups, and have never been proven to improve patient care.