How to choose treatment for patients who are eligible for both unicompartmental knee arthroplasty and high tibial osteotomy?

Osteoarthritis (OA) is highly prevalent among older adults, with most cases beginning as early unicompartmental arthritis. Surgical treatments, such as unicompartmental knee arthroplasty (UKA) or high tibial osteotomy (HTO), are chosen based on the patient's intra-articular or extra-articular deformity. However, there are overlapping 'gray areas' in the classic indications for these two surgical methods, making the decision between these two procedures less straightforward. Several recent studies have underscored the importance of refining these indications. For example, multiple long-term studies have demonstrated the durability of UKA in well-selected patients, while others suggest that HTO may offer superior functional outcomes in younger, active individuals. Research has increasingly focused on identifying key preoperative factors that could better predict the success of these procedures. For instance, preoperative exercise intensity, which reflects the patient's physical activity levels and joint adaptability, has been shown to significantly influence post-surgical outcomes for both UKA and HTO. This study aims to bridge the current gap in surgical decision-making by assessing a range of preoperative factors, including exercise intensity, meniscal status, anatomical structure, soft tissue laxity, bone marrow edema, spontaneous insufficiency fractures of the knee, osteoporosis, as well as basic demographic data such as gender, age, and weight. The goal is to develop more robust and scientifically grounded criteria for determining the most appropriate surgical treatment particularly for patients in the 'gray areas' of indications. By establishing these more refined standards, orthopedic surgeons will be better positioned to optimize patient outcomes and reduce the risk of revision surgeries. The Translational Potential of this Article: This study systematically evaluated a range of preoperative factors to enhance surgical decision-making for patients who fall within the "gray areas" of UKA and HTO indications. By refining patient selection, this approach aims to improve individualized treatment, optimize postoperative functional recovery, and reduce revision rates. In addition, it seeks to establish a scalable predictive model and decision-making framework, offering evidence-based support for the standardization and refinement of surgical strategies in osteoarthritis.