A three-dimensional scoring system for assessment of individual bony and laxity phenotype restoration (knee SIPR) in personalised TKA as a base for treatment guidance.

Purpose: Although personalised alignment has become popular in total knee arthroplasty (TKA), it is unclear which workflow and alignment strategy best restores the bony and laxity phenotype and whether this varies between knee phenotypes. The aim of this study was, therefore, to develop a three-dimensional (3D) scoring system which assesses bony anatomy, laxity and alignment parameters for TKA. This novel 3D scoring system was tested using a validated TKA simulator on three different knee phenotypes with various alignment workflows. 3D scores were compared between phenotypes and workflows.

Methods: In this 3D scoring system, analyses of bony resections of all six joint planes were included (maximum score for anatomical resections ± 1 mm) as well as joint laxity/gap analysis (maximum score for balanced extension/flexion gap, medial and lateral side ± 2 mm). Additional alignment parameters (hip-knee-ankle angle, medial proximal tibial angle, lateral distal femoral angle, Tibia slope and coronal plane alignment of the knee) were integrated. All data points were obtained from preoperative long leg x-rays, intraoperative gap analysis with CAS and intraoperative cartilage measurements. The maximum score for all categories was 27 points (12/10/5). The 3D scores were analysed for nine knees with three knee phenotypes (neutral, varus and valgus) with six different alignment workflows (mechanical alignment-femur first, adjusted mechanical alignment-femur first, unrestricted kinematic alignment, restricted kinematic alignment, inverse kinematic alignment and functional alignment-tibia first) using the Knee-computational alignment trainer simulator. Comparison between workflows in all phenotypes was performed for each category.

Results: In neutral phenotypes, all alignment workflows, including mechanical alignment, showed similar high mean scores. In varus and valgus phenotypes, personalised alignment workflows scored higher than systematic workflows. While in varus phenotypes, scoring of personalised alignment workflows was similarly high to that in straight knees phenotypes, it showed lower means in valgus phenotypes. Measured-resection workflows restored bony phenotypes in a higher percentage while gap-balanced workflows performed better in the category of laxity/gap balance. None of the personalised workflows performed best in all knees.

Conclusions: The new 3D scoring system for individual knee phenotype restoration in TKA allowed a quantitative analysis of the individual reconstruction of the bony and laxity anatomy in different knee phenotypes. First preliminary results show that personalised alignment workflows perform better than systematic mechanical alignment in varus and valgus phenotypes, while in neutral phenotypes, the difference was minimal. None of the personalised workflows scored best in all knees, showing the potential for a 3D phenotype workflow including more bony alignment and laxity parameters. Testing of this 3D scoring system in a larger series of cases is crucial to prove the concept and test correlations between 3D scores and clinical outcomes.

Level of evidence: Level IIa.