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• W4376871308 abstract "Commentary Unicompartmental knee arthroplasty (UKA) appears to be the treatment of choice for managing single-compartment osteoarthritis. The traditional strict patient selection criteria, described by Kozinn and Scott1, have contributed to obtaining satisfactory clinical outcomes and low revision rates after medial UKA. With recent technical improvements, particularly the use of robotics, and modern implants, the indications for UKA have been extended2, including in a young and active population. Nevertheless, some studies leave a sword of Damocles hanging over young and active patients or patients with a high body mass index, with the threat involving early UKA revision for aseptic loosening. One of the options to reduce this risk of loosening is the use of uncemented implants allowing biological osseointegration, which are supposed to be more durable in the long term. The literature has revealed a lower risk of loosening for uncemented mobile-bearing UKAs compared with cemented mobile-bearing UKAs3. However, do uncemented mobile-bearing implants have superior survivorship compared with cemented fixed-bearing ones? Instead of remaining trapped in old fears, it is essential to be able to carry out solid studies, based on large databases, of current implants and techniques in order to advance our current indications and therapeutic choices. With the diversity of implant designs (fixed or mobile, cemented or uncemented), assessing UKA survival according to indications and patient demographics in single-center studies with small patient numbers remains difficult. Registers that include large amounts of data including implant design and fixation provide an alternative, but their data on the etiology of revisions remain imprecise. Therefore, analysis of registry data is likewise insufficient to understand the reasons for revisions and thus improve current indications for UKA and UKA implant selection. The study by Tay et al. has the advantage of including a large database (2,015 UKAs) covering several orthopaedic centers, long-term follow-up (8 years on average), and several implant designs and fixation methods, while providing more details on the causes and timing of revisions than a register. Tay et al. demonstrated a significantly higher revision risk with cemented mobile-bearing implants (implant survival: 80%) compared with cemented fixed-bearing implants (survival: 92%) or uncemented mobile-bearing implants (survival: 91%). The different causes of revision depending on the implant design and fixation are highlighted. Uncemented mobile-bearing implants had a definite advantage with respect to the risk of aseptic loosening, although they had a higher risk of bearing dislocation. This type of implant will therefore be of interest to young and active patients, a population described in this study as having a higher risk of loosening. The study suggested the potential advantage of using different types of implants and fixation in patient groups with different characteristics. It would have been very interesting to carry out analyses of the interaction between implant type and age (or any other demographic parameter), to determine the best implant in each patient category. One of the current essential issues in arthroplasty is which implant and which surgical technique will be the most appropriate for each specific patient: i.e., “How can I personalize the surgery to obtain the best result for my patient?” A randomized study has difficulties answering these questions. Randomized studies typically have several inclusion and exclusion criteria that make it challenging to extrapolate their results to the entire population that is of interest. Personalized surgery will find answers in the analysis of “big data.” The large databases that can provide the necessary data are presently being put in place, and it will be essential to create algorithms or predictive models based on them that can be used to personalize our indications and our surgical techniques for each patient. Indeed, an algorithm that would make it possible to determine which implant design and which fixation to use for each patient in a personalized way (according to their age, body mass index, bone deformity, etc.) would very likely be revolutionary in terms of implant survival and patient satisfaction. Machine learning-based predictive models are being developed to help surgeons by improving the decision-making process4. The analysis of these databases will pave the way for personalized surgery adjusted according to demographic data and preoperative status." @default.
• W4376871308 created "2023-05-18" @default.
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• W4376871308 date "2023-05-17" @default.
• W4376871308 modified "2023-09-26" @default.
• W4376871308 title "Personalized Surgery Based on Large Databases Is the Next Step to Be Taken" @default.
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• W4376871308 doi "https://doi.org/10.2106/jbjs.23.00224" @default.
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