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The Bone & Joint Journal
Vol. 106-B, Issue 10 | Pages 1059 - 1066
1 Oct 2024
Konishi T Hamai S Tsushima H Kawahara S Akasaki Y Yamate S Ayukawa S Nakashima Y

Aims. The Coronal Plane Alignment of the Knee (CPAK) classification has been developed to predict individual variations in inherent knee alignment. The impact of preoperative and postoperative CPAK classification phenotype on the postoperative clinical outcomes of total knee arthroplasty (TKA) remains elusive. This study aimed to examine the effect of postoperative CPAK classification phenotypes (I to IX), and their pre- to postoperative changes on patient-reported outcome measures (PROMs). Methods. A questionnaire was administered to 340 patients (422 knees) who underwent primary TKA for osteoarthritis (OA) between September 2013 and June 2019. A total of 231 patients (284 knees) responded. The ­Knee Society Score 2011 (KSS 2011), Knee injury and Osteoarthritis Outcome Score-12 (KOOS-12), and Forgotten Joint Score-12 (FJS-12) were used to assess clinical outcomes. Using preoperative and postoperative anteroposterior full-leg radiographs, the arithmetic hip-knee-ankle angle (aHKA) and joint line obliquity (JLO) were calculated and classified based on the CPAK classification. To investigate the impact on PROMs, multivariable regression analyses using stepwise selection were conducted, considering factors such as age at surgery, time since surgery, BMI, sex, implant use, postoperative aHKA classification, JLO classification, and changes in aHKA and JLO classifications from preoperative to postoperative. Results. The preoperative and postoperative CPAK classifications were predominantly phenotype I (155 knees; 55%) and phenotype V (73 knees; 26%), respectively. The change in the preoperative to postoperative aHKA classification was a significant negative predictive factor for KOOS-12 and FJS-12, while postoperative apex proximal JLO was a significant negative predictive factor for KSS 2011 and KOOS-12. Conclusion. In primary TKA for OA, preoperative and postoperative CPAK phenotypes were associated with PROMs. Alteration in varus/valgus alignment from preoperative to postoperative was recognized as a negative predictive factor for both KOOS-12 and FJS-12. Moreover, the postoperative apex proximal JLO was identified as a negative factor for KSS 2011 and KOOS-12. Determining the target alignment for each preoperative phenotype with reproducibility could improve PROMs. Cite this article: Bone Joint J 2024;106-B(10):1059–1066


The Bone & Joint Journal
Vol. 103-B, Issue 2 | Pages 329 - 337
1 Feb 2021
MacDessi SJ Griffiths-Jones W Harris IA Bellemans J Chen DB

Aims. A comprehensive classification for coronal lower limb alignment with predictive capabilities for knee balance would be beneficial in total knee arthroplasty (TKA). This paper describes the Coronal Plane Alignment of the Knee (CPAK) classification and examines its utility in preoperative soft tissue balance prediction, comparing kinematic alignment (KA) to mechanical alignment (MA). Methods. A radiological analysis of 500 healthy and 500 osteoarthritic (OA) knees was used to assess the applicability of the CPAK classification. CPAK comprises nine phenotypes based on the arithmetic HKA (aHKA) that estimates constitutional limb alignment and joint line obliquity (JLO). Intraoperative balance was compared within each phenotype in a cohort of 138 computer-assisted TKAs randomized to KA or MA. Primary outcomes included descriptive analyses of healthy and OA groups per CPAK type, and comparison of balance at 10° of flexion within each type. Secondary outcomes assessed balance at 45° and 90° and bone recuts required to achieve final knee balance within each CPAK type. Results. There was similar frequency distribution between healthy and arthritic groups across all CPAK types. The most common categories were Type II (39.2% healthy vs 32.2% OA), Type I (26.4% healthy vs 19.4% OA) and Type V (15.4% healthy vs 14.6% OA). CPAK Types VII, VIII, and IX were rare in both populations. Across all CPAK types, a greater proportion of KA TKAs achieved optimal balance compared to MA. This effect was largest, and statistically significant, in CPAK Types I (100% KA vs 15% MA; p < 0.001), Type II (78% KA vs 46% MA; p = 0.018). and Type IV (89% KA vs 0% MA; p < 0.001). Conclusion. CPAK is a pragmatic, comprehensive classification for coronal knee alignment, based on constitutional alignment and JLO, that can be used in healthy and arthritic knees. CPAK identifies which knee phenotypes may benefit most from KA when optimization of soft tissue balance is prioritized. Further, it will allow for consistency of reporting in future studies. Cite this article: Bone Joint J 2021;103-B(2):329–337


Aims

The aim of this study was to investigate the distribution of phenotypes in Asian patients with end-stage osteoarthritis (OA) and assess whether the phenotype affected the clinical outcome and survival of mechanically aligned total knee arthroplasty (TKA). We also compared the survival of the group in which the phenotype unintentionally remained unchanged with those in which it was corrected to neutral.

Methods

The study involved 945 TKAs, which were performed in 641 patients with primary OA, between January 2000 and January 2009. These were classified into 12 phenotypes based on the combined assessment of four categories of the arithmetic hip-knee-ankle angle and three categories of actual joint line obliquity. The rates of survival were analyzed using Kaplan-Meier methods and the log-rank test. The Hospital for Special Surgery score and survival of each phenotype were compared with those of the reference phenotype with neutral alignment and a parallel joint line. We also compared long-term survival between the unchanged phenotype group and the corrected to neutral alignment-parallel joint line group in patients with Type IV-b (mild to moderate varus alignment-parallel joint line) phenotype.


The Bone & Joint Journal
Vol. 106-B, Issue 12 | Pages 1363 - 1368
1 Dec 2024
Chen DB Wood JA Griffiths-Jones W Bellemans J Haddad FS MacDessi SJ

As advancements in total knee arthroplasty progress at an exciting pace, two areas are of special interest, as they directly impact implant design and surgical decision making. Knee morphometry considers the three-dimensional shape of the articulating surfaces within the knee joint, and knee phenotyping provides the ability to categorize alignment into practical groupings that can be used in both clinical and research settings. This annotation discusses the details of these concepts, and the ways in which they are helping us better understand the individual subtleties of each patient’s knee.

Cite this article: Bone Joint J 2024;106-B(12):1363–1368.


The Bone & Joint Journal
Vol. 106-B, Issue 6 | Pages 525 - 531
1 Jun 2024
MacDessi SJ van de Graaf VA Wood JA Griffiths-Jones W Bellemans J Chen DB

The aim of mechanical alignment in total knee arthroplasty is to align all knees into a fixed neutral position, even though not all knees are the same. As a result, mechanical alignment often alters a patient’s constitutional alignment and joint line obliquity, resulting in soft-tissue imbalance. This annotation provides an overview of how the Coronal Plane Alignment of the Knee (CPAK) classification can be used to predict imbalance with mechanical alignment, and then offers practical guidance for bone balancing, minimizing the need for soft-tissue releases.

Cite this article: Bone Joint J 2024;106-B(6):525–531.


The Bone & Joint Journal
Vol. 106-B, Issue 11 | Pages 1231 - 1239
1 Nov 2024
Tzanetis P Fluit R de Souza K Robertson S Koopman B Verdonschot N

Aims

The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population.

Methods

We used an active appearance model-based approach to segment the preoperative CT of 21 osteoarthritic patients, which identified the osteophyte-free surfaces and estimated cartilage from the segmented bones; these geometries were used to construct patient-specific musculoskeletal models of the pre-diseased knee. Subsequently, implantations were simulated using the MA method, and a previously developed optimization technique was employed to find the optimal implant position that minimized the root mean square deviation between pre-diseased and postoperative ligament strains and kinematics.