Robotic evaluation of articular laxity (REAL) classification: a new intraoperative knee soft-tissue laxity classification using ROSA robotic software.

Abstract

Purpose: Currently, there is no widely accepted method for measuring soft-tissue laxity and defining a balanced total knee arthroplasty (TKA). We aim to evaluate whether robotic technology can facilitate the categorization of intraoperative knee laxity.

Methods: Our study was conducted in two phases. A senior surgeon performed imageless robotically assisted TKAs (ra-TKAs) using functional alignment. The first phase included 120 patients. Following the surgical approach, the medial and lateral soft-tissue laxity was recorded in extension and 90° flexion. The distribution of the difference and sum of laxities in extension and 90° flexion was assessed to classify laxity phenotypes. The second phase validated the classification in 102 additional ra-TKAs. Laxity phenotypes were evaluated at the start and end of the procedure.

Results: Laxity difference followed a normal distribution, facilitating categorization into three groups, with a standard deviation of 2.5 mm. Three categories of mediolateral laxity severity difference were established: < 2.5 mm, 2.5-5 mm, and > 5 mm. These laxity groups were coded in extension as 1, 2, and 3 and in flexion as A, B, and C, respectively. Nine laxity phenotypes emerged from the combination of the extension and flexion categories (1A-C, 2A-C, and 3A-C). Phenotypes 1A and 1B were the most common at the operation' beginning, while phenotypes 3B and 1C were the rarest. At the end of the operation, 93% were categorized as class 1A and 1B, defining the "balanced area".

Conclusion: Our study recognized nine intraoperative soft-tissue knee laxity phenotypes, potentially laying the groundwork for a surgical consensus on knee balancing.