A retrospective comparison of mechanically vs. kinematically aligned unicompartmental knee arthroplasties

Background

Unicompartmental Knee Arthroplasties (UKAs) treat unicompartmental arthritic degeneration. Traditionally, they are placed in a mechanical alignment with bone cuts perpendicular to the leg's mechanical axis. Kinematic alignment, an alternative, considers the patient's pre-arthritic alignment.

Methods

A retrospective study at Medstar Washington Hospital Center from 2015 to 2022 identified 72 UKA patients. Among them, 53 had mechanical alignments, and 20 had kinematic alignments. Using the Forgotten Joint Score (FJS) and Oxford Knee Score (OKS), Patient Reported Outcome Measures for these surgeries were recorded. Individuals were additionally analyzed post-surgically for Coronal Plane Alignment of the Knee (CPAK). Analysis was performed within the post-operative joint imaging, where mechanical medial proximal tibial angle (MPTA) and lateral distal femoral angle (LDFA) were measured. The arithmetic hip knee ankle angle (aHKA) and joint line obliquity (JLO) were calculated according to the results of MPTA-LDFA and MPTA + LDFA respectively and grouped in accordance with the CPAK classification.

Results

Overall, there was a statistically significant response rate of 51% (>50%). According to the FJS, the mechanical cohort averaged a score of 39.1 (±33.8), while the kinematical cohort averaged 56.5 (±35.2). A two-sample t-test of this data demonstrated a statistically insignificant p-value of 0.1537. According to the OKS, the mechanical cohort averaged a score of 29.1 (±10) and the kinematical cohort averaged 38.4 (±8). A two-sample t-test of this data demonstrated a statistically significant p-value of <0.001. Of note, 7 patients had to undergo revisions in the mechanical alignment cohort compared to 0 in the kinematic alignment cohort due to aseptic loosening.

Conclusion

This study demonstrates the potential benefit in patient outcomes for individuals who undergo a kinematic rather than mechanical alignment of their UKAs. The results of the CPAK data with the significance of LDFA are consistent with the goals of the mechanical and kinematic alignment respectively.

Imageless robotic total knee arthroplasty determines similar coronal plane alignment of the knee (CPAK) parameters to long leg radiographs

BACKGROUND

The coronal plane alignment of the knee (CPAK) classification was first developed using long leg radiographs (LLR) and has since been reported using image-based and imageless robotic total knee arthroplasty (TKA) systems. However, the correspondence between imageless robotics and LLR-derived CPAK parameters has yet to be investigated. This study therefore examined the differences in CPAK parameters determined with LLR and imageless robotic navigation using either generic or optimized cartilage wear assumptions.

METHODS

Medial proximal tibial angle (MPTA) and lateral distal femoral angle (LDFA) were determined from the intraoperative registration data of 61 imageless robotic TKAs using either a generic 2 mm literature-based wear assumption (Navlit) or an optimized wear assumption (Navopt) found using an error minimization algorithm. MPTA and LDFA were also measured from preoperative LLR by two observers and intraclass correlation coefficients (ICCs) were calculated. MPTA, LDFA, joint line obliquity (JLO), and arithmetic hip-knee-ankle angle (aHKA) were compared between the robotic and the average LLR measurements over the two observers.

RESULTS

ICCs between observers for LLR were over 0.95 for MPTA, LDFA, JLO, and aHKA, indicating excellent agreement. Mean CPAK differences were not significant between LLR and Navlit (all differences within 0.6°, P > 0.1) or Navopt (all within 0.1°, P > 0.83). Mean absolute errors (MAE) between LLR and Navlit were: LDFA = 1.4°, MPTA = 2.0°, JLO = 2.1°, and aHKA = 2.7°. Compared to LLR, the generic wear classified 88% and the optimized wear classified 94% of knees within one CPAK group. Bland-Altman comparisons reported good agreement for LLR vs. Navlit and Navopt, with > 95% and > 91.8% of measurements within the limits of agreement across all CPAK parameters, respectively.

CONCLUSIONS

Imageless robotic navigation data can be used to calculate CPAK parameters for arthritic knees undergoing TKA with good agreement to LLR. Generic wear assumptions determined MPTA and LDFA with MAE within 2° and optimizing wear assumptions showed negligible improvement.

Distribution of coronal plane alignment of the knee classification does not change as knee osteoarthritis progresses: a longitudinal study from the Toei study

PURPOSE

The purpose of this study was to investigate the coronal plane alignment of the knee (CPAK) phenotypes of individuals with knee osteoarthritis (OA) progression. We hypothesized that distributions of CPAK phenotypes would be similar throughout OA progression, despite arithmetic hip-knee-ankle angle (aHKA) and joint line obliquity (JLO) changing.

METHODS

A total of 248 patients (79 men and 169 women) participated in the first study in 2012 and the fifth study in 2020. Patients with progression of knee OA for eight years were included. Knee OA progression was defined as advancement from KL grade 0-2 to KL grade 3 or 4. Alignment parameters, including the aHKA, JLO, hip-knee-ankle angle (HKA), lateral distal femur angle (LDFA), medial proximal tibial angle (MPTA), and joint line convergence angle (JLCA), were measured. Changes in distribution of CPAK classifications and alignment parameters were investigated. Alignment parameters were compared using a paired t-test. Statistical significance was defined as p < 0.05.

RESULTS

The study included 48 patients (60 knees). The distributions of all CPAK phenotypes were similar between 2012 and 2020. MPTA (83.7° ± 2.8° vs. 82.3° ± 4.8°, p < 0.01), aHKA (- 3.6° ± 3.8° vs. - 4.9° ± 6.2°, p = 0.01), and JLO (171.1° ± 4.6° vs. 169.5° ± 5.1°, p < 0.01) decreased significantly, and JLCA (1.17° ± 2.2° vs. 3.1° ± 4.7°, p < 0.01) and HKA (4.8° ± 3.9° vs. 8.0° ± 5.4°, p < 0.01) increased significantly. In contrast, LDFA (87.4° ± 3.2° vs. 87.2° ± 3.1°, p = n.s.) did not change significantly.

CONCLUSIONS

The CPAK classification system can predict constitutional alignment, even with knee OA progression, and enables surgeons to perform individualized preoperative alignment planning according to knee phenotypes.

Variation in knee balance as a function of hip-knee-ankle angle and joint line obliquity in robotic assisted total knee arthroplasty

PURPOSE

To describe the mediolateral (ML) gap balance in pre-resection arthritic knees undergoing robot-assisted (RA) total knee arthroplasty (TKA) within the nine phenotypes of the Coronal Plane Alignment of the Knee (CPAK) classification.

METHODS

A total of 1124 RA TKA cases were retrospectively reviewed. ML balance was calculated using a digital ligament tensioning device following tibial resection and prior to any femoral resection throughout flexion (10°, 40°, and 90°). Lateral distal femoral angle (LDFA) and medial proximal tibial angle (MPTA) were landmarked intra-operatively and adjusted for wear based on hip-knee-ankle angle (HKA). Knees were then subdivided into CPAK categories based on the arithmetic HKA (aHKA) and joint line obliquity (JLO). Differences in balance between CPAK phenotypes were compared.

RESULTS

For aHKA, a greater medial gap was observed in varus compared to neutral or valgus knees at 10° (5.2 ± 3.0 mm vs 3.3 ± 3.2 mm vs - 0.4 ± 5.0 mm respectively; p < 0.0001). A similar trend was observed for JLO in distal apex compared to neutral and proximal apex knees at 10° (3.4 ± 3.6 vs 1.1 ± 5.0 vs - 2.4 ± 6.3 respectively; p < 0.0001). The greatest ML balance differences were found when combining JLO and aHKA at 10° (type I vs type VI: ∆6.6 mm; p < 0.0001).

CONCLUSION

There is a high degree of variability in arthritic ML balance within and between each CPAK phenotype. A single alignment philosophy based on bony landmarks alone may not be sufficient to balance all knees. Utilisation of robotics and digital ligament tensioning devices serves as important adjuncts to characterize the patient-specific soft tissue profile.