Grading medial compartment tightness among varus osteoarthritic knees during image-free robot-assisted total knee arthroplasty for optimized pre-resection gap balancing

Listen to Simon and Amy discuss the paper 'Grading medial compartment tightness among varus osteoarthritic knees during image-free robot-assisted total knee arthroplasty for optimized pre-resection gap balancing' published in the August 2025 issue of Bone & Joint Open.

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 Open. Today we're discussing the paper 'Grading medial compartment tightness among varus osteoarthritic knees during image-free robot-assisted total knee arthroplasty for optimized pre-resection gap balancing', published in August 2025 by K Eachempati and colleagues. I am Simon and I'm joined by my co-host Amy.

Hello Simon, lovely to be here. This study explores an essential aspect of total knee arthroplasty, particularly concerning various osteoarthritic knees. The research discusses optimizing gap balancing using an image-free robot-assisted method, which is rather cutting edge in orthopaedics today.

Achieving medial-lateral gap balance in both flexion and extension is a primary aim of total knee arthroplasty. Traditionally, various soft-tissue releases were required postpone resection. However, the advent of robot-assisted total knee arthroplasty has facilitated more precise intraoperative planning and multiplanar adjustments. [00:01:00] This study by Eachempati et al investigates grade and medial compartment tightness to better predict and optimize these soft-tissue releases preoperatively.

Exactly, the researchers aim to identify the degree of medial tightness that resolves independently during surgery. The extent of tightness necessitating extensive soft-tissue releases, and to develop a grading system for preoperative tightness to optimize gap balancing.

This is particularly pertinent for various osteoarthritic knees where achieving proper alignment and stability can present significant challenges. The study's methodology was divided into two parts. The first part involved 100 patients and concentrated on developing the grading system from medial compartment tightness.

The second part applied the system to another 200 patients to test its predictive ability. The surgeries employed the quarry image-free robot and included various types of soft-tissue releases based on the observed tightness.

The researchers recorded medial compartment tightness in both extension and flexion, categorizing it into three [00:02:00] grades: less than four millimeters, grade one, four to seven millimeters, grade two, and more than seven millimeters, grade three.

They found that tightness under four millimeters often resolved by itself, whereas tightness exceeding seven millimeters typically required extensive soft-tissue releases.

They also noted that over 80% of patients aligned with these categorizations both pre and post-surgery. This high level of consistency indicates that the grading system is fairly reliable for predicting the necessity for soft-tissue releases.

Another intriguing aspect is how this grading system helps avert overcorrection and the subsequent use of thicker polyethylene inserts, which can arise from complete pre-resection gap balancing.

Indeed, the study points out that complete pre-resection gap balancing might be unnecessary, as some tightness may resolve naturally with the removal of posterior osteophytes and other redundant structures.

This insight aids surgeons in avoiding unnecessary corrections, thus maintaining joint stability postoperatively. Moreover, [00:03:00] there's a positive linear relationship between preoperative tightness and the gap correction achieved. Essentially more severe initial tightness correlates with greater correction post-posterior structure removal. These findings can significantly enhance planning and execution of robot-assisted total knee arthroplasty procedures.

Quite right, Simon. While the study does acknowledge certain limitations, such as not examining the size of excised osteophytes, or the variability between surgical techniques, it still offers a robust grading system readily applicable in clinical practice. Future research could explore its potential use with alignment strategies beyond mechanical alignment.

In conclusion, the grading system proposed by Eachempati and colleagues provides a systematic approach for managing medial compartment tightness and robot-assisted total knee arthroplasty. It helps anticipate the necessary soft-tissue releases and optimizes gap balancing, ultimately leading to more successful surgical outcomes.

Absolutely. With the increasing prevalence of robot-assisted [00:04:00] systems, the insights from this study are likely to influence future practices and improve patient care. Thanks for tuning into AI talks with Bone & Joint. Do peruse the entire paper in Bone & Joint Open for a more detailed understanding.

Thank you Amy, and our thanks to all our listeners. Until next time, stay curious and stay well informed.