PURPOSE:Phenotypic variation is determined by both bony anatomy and ligamentous laxity, which are closely interrelated. Therefore, assessment of patient-specific laxity phenotypes should begin with characterisation of the underlying bony phenotype. The purpose of this manuscript is to develop a clinically applicable classification of knee laxity phenotypes and a corresponding treatment algorithm for total knee arthroplasty (TKA) balancing. METHODS:Patient-specific laxity phenotypes were evaluated in the context of the underlying bony phenotype. Based on coronal gap characteristics, three laxity phenotypes in extension and three in flexion were identified, resulting in a 3 × 3 classification system comprising nine distinct phenotypes. A treatment algorithm was subsequently developed to guide correction strategies according to the magnitude of gap imbalance. RESULTS:In flexion, the most common phenotype demonstrated lateral laxity (>60% of knees), followed by a balanced flexion gap (approximately 30%). A medially lax flexion phenotype was also identified and may be clinically relevant because of its association with potential TKA failure mechanisms. Only two of the nine laxity phenotypes (Types 4 and 5) demonstrated currently accepted gap balance criteria in both extension and flexion. The remaining seven phenotypes required correction towards either Types 4 or 5. CONCLUSION:A treatment algorithm is presented outlining different strategies to address imbalance based on the magnitude of the gap difference. Minimal differences (2-3 mm) may be managed through targeted bone cuts, moderate differences (3-5 mm) benefit from adding soft-tissue releases and specific insert types. Lastly, larger differences (>5 mm) may require the addition of further implant constraint. This work demonstrates that one standardised balancing goal will not be able to reconstruct patient-specific laxity phenotypes without imposing variable compromises on either bone reconstruction or soft-tissue balancing. LEVEL OF EVIDENCE:Level V.

Glucagon-like peptide-1 receptor agonists and related incretin-based therapies are rapidly changing the management of obesity and metabolic disease. Their increasing use also among patients with osteoarthritis has important implications for hip, knee and shoulder arthroplasty. Beyond weight loss, these drugs may influence systemic inflammation, glycemic control, osteoarthritic symptoms, perioperative complications and surgical candidacy. Recent retrospective studies suggest that preoperative GLP-1 receptor agonist use may be associated with lower risks of periprosthetic joint infection, hospital readmission and other adverse outcomes after total joint arthroplasty. Beyond the perioperative setting, GLP-1 receptor agonists have also been associated with reductions in blood pressure and significant improvements in cardiovascular risk among patients with chronic kidney disease or preexisting cardiovascular disease. However, the current evidence remains largely observational and is limited by residual confounding, indication bias, heterogeneity in treatment timing, and inconsistent reporting of drug class, dose, and duration. This editorial argues that GLP-1 receptor agonists should not be viewed simply as weight-loss agents before arthroplasty, but as part of a broader movement toward metabolic optimisation. Prospective studies are needed before these therapies can be incorporated into standardised arthroplasty optimisation pathways.

While robotic-assisted total knee arthroplasty (rTKA) has been proven to achieve highly precise and reproducible radiographic results, its accuracy remains highly dependent on correct intraoperative landmark acquisition. This technical guide aims to provide practical guidance for intraoperative recognition of landmarking and gap balance assessment errors during rTKA using the image-free VELYSTM system. Based on the authors' clinical experience with this system, the most common pitfalls as well as their intraoperative recognition are highlighted. Special focus is set on pin placement, bone reference checkpoints and landmark acquisition, with practical intraoperative cues that allow surgeons to detect these errors intraoperatively before bone resections are performed. The present article should support early adopters of the VELYSTM system, with the aim of preventing precision inaccuracies. Level of Evidence:Level V.

Total knee arthroplasty (TKA) has historically pursued a uniform neutral mechanical axis, an approach justified by early implant constraints but increasingly challenged by evidence that neutral alignment is not the constitutional norm. The recognition that meaningful personalisation requires prior systematic characterisation of the individual knee has driven the emergence of phenotyping as a structured framework integrating morphology, alignment, laxity, and kinematics. This article provides a practical, stepwise cookbook for state-of-the-art knee phenotyping in TKA, intended to guide surgeons through the application of currently available classifications within a coherent decision-making sequence. Step 1 addresses the bony Functional Knee Phenotype (B-FKP), describing how to measure the hip-knee-ankle angle, femoral mechanical angle, and tibial mechanical angle from weight-bearing long-leg radiographs to derive the two-dimensional phenotype, and how to extend the classification to a three-compartment model incorporating the posterior condylar angle and anterior trochlear angle when CT imaging is available. The phenotype obtained then informs the choice between mechanical, kinematic, functional, or fully personalised alignment strategies. Step 2 introduces the laxity Functional Knee Phenotype (L-FKP), explaining how standardised intraoperative varus and valgus stress testing at full extension and 90° of flexion, performed after dialling in patient-specific bony parameters on robotic platforms, generates a nine-group matrix that directly links laxity patterns to a structured management algorithm of bony cut adjustment, soft-tissue release, and constraint escalation. Step 3 presents the Dynamic Alignment of the Knee (DyAK) classification, which captures coronal alignment across the functional arc and addresses the limitations of static measurements, given that fewer than 15% of knees maintain consistent alignment throughout range of motion. Together, these layered phenotypes provide a reproducible framework in which alignment philosophies become tools matched to individual anatomy rather than competing doctrines, supporting safer and more personalised reconstruction.

Phenotyping of the knee joint has been established as an important pre- and postoperative measure to better describe the individual knee joint in terms of alignment, laxity, morphology, and kinematics. Therefore, this narrative review aims to provide a comprehensive analysis of the current literature on phenotyping to understand both the major benefits and the existing limitations. This narrative review was conducted according to the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. A total of 38 studies were included. The included papers were categorized into two main groups, CPAK and functional knee phenotyping (FKP), of which the second group could be further subdivided into alignment, laxity, and morphology. The advantages and limitations were noted. Understanding the underlying knee phenotype in OA knees is crucial for a more personalized approach in TKA. The standardized use of the current phenotype systems, whether CPAK or FKP is key. The advantage of CPAK is that it is easy to apply in daily practice, but it is of limited value because it may omit a lot of important information. FKP, on the other hand, contains many more parameters, which makes it difficult for the majority of surgeons to integrate into their routine planning. However, as the knee is defined by multiple parameters in three dimensions, it is of utmost importance to integrate as many factors as possible for an optimal alignment in TKA.

PURPOSE:While bony alignment phenotype reconstruction became an important part of personalised knee arthroplasty, the knowledge on laxity phenotypes (LPs) is still limited. This study aimed to calculate individual LPs and assess their changes based on bony decisions from different alignment workflows. METHODS:Radiographs and computer-assisted surgery data of 86 knees were imported into a validated knee alignment simulator. Individual bony parameters (medial proximal tibial angle, lateral distal femoral angle and posterior condyle axis) were first introduced. By that, the patient-specific bony phenotype (B-FKP) was implemented, and based on these simulations, the patient-specific laxity phenotype (L-FKP) was defined, calculated and analysed for the total group, as well as for all Coronal Plane Alignment of the Knee (CPAK) subgroups. CPAK I and IV were summarised as varus group; II and V as neutral, and III, VI and IX as valgus group. Identical calculations were then compared for the MA and L-FKP of both workflows. LP was calculated in both extension (L-FKPext) and flexion (L-FKPflex), and a pattern matrix was constructed for all possible L-FKP combinations, enabling a comprehensive distribution analysis. Statistical differences between subgroups and B-FKP and mechanical alignment (MA) workflows were calculated. RESULTS:B-FKP showed a minimal, non-significant difference for L-FKPext in all subgroups; however, a huge variability in L-FKP pattern analysis. In contrast, MA showed a significant difference for L-FKPext in all subgroups, with a high correlation between L-FKPext and hip-knee-ankle angle. While in MA, 98% of knees showed lateral laxity (L-FKPflex-latlax), in B-FKP, only 56% were L-FKPflex-latlax, with a large variability (31% L-FKPflex-neutr and 13% L-FKPflex-medlax). CONCLUSIONS:Personalised bony resections reduce gap differences for LPext and LPflex independent of the deformity. MA showed a high correlation between deformity and LPext in extension and a uniform lateral laxity in flexion. L-FKP analysis can help to understand the individuality of knees from a soft tissue aspect. LEVEL OF EVIDENCE:Level III.

Purpose Total knee arthroplasty has historically relied on mechanical alignment (MA); however, a systematic neutral target cannot accommodate the biological diversity of the knee. Based on functional knee phenotype analysis, large imaging series have demonstrated broad coronal variability even in young, non-osteoarthritic knees, with constructs corresponding to classic neutral alignment in only 4–6% of limbs. These data indicate that MA as a universal goal is insufficient and the data support a paradigm shift toward patient-specific alignment (PSA). Methods The study institution practices a tibia-first, gap-balanced, phenotype-based PSA within defined safe zones to respect joint line obliquity and soft tissue balance while avoiding outlier alignment. Targets are defined with reproducible metrics, namely hip–knee–ankle angle, femoral mechanical angle, tibial mechanical angle, and joint line convergence angle (JLCA). Results Current evidence shows high target attainment and reliable intra-operative balance, with balanced gaps in >90% of PSA and JLCA improving from 1.8 to 4.3° pre-operatively to 0.6–1.2° post-operatively (p ​< ​0.001). Moreover, results from related strategies (inverse kinematic alignment and functional alignment) consistently report outcomes at least equivalent, and in selected domains superior, to MA while requiring fewer soft tissue releases. Conclusions The next step for PSA is scalability: multicenter datasets, registry linkage, and long-term follow-up are needed to determine durability, to refine phenotype-specific safe zones, and to validate this strategy across implants and patient populations.

Total knee arthroplasty (TKA) has become one of the most widely performed procedures for end-stage osteoarthritis, yet patient satisfaction continues to lag behind that of hip replacement. Advances in robotics and artificial intelligence have increased surgical precision, but improved accuracy alone does not guarantee better outcomes. The challenge lies in defining appropriate alignment and balancing strategies that respect each patient's unique anatomy and soft-tissue characteristics rather than relying on uniform targets. Personalized alignment has emerged as a promising concept, aiming to restore individual bony geometry and preserve native laxity patterns across all planes. However, questions remain about what constitutes "normal" versus "pathologic," especially given anatomical and demographic differences between populations. Data derived largely from Caucasian cohorts may not be directly transferable to Asian patients, whose morphologies often differ substantially. This underscores the need for region-specific research, robust data collection, and harmonized workflows supported by advanced analytic systems. Only through global collaboration and objective evidence can reliable boundaries for personalized techniques be established. By integrating precision technology with truly individualized surgical planning, the next phase of TKA development seeks to raise functional outcomes and patient satisfaction worldwide.

Background:Accurate risk adjustment is critical for outcome prediction and quality improvement in total knee arthroplasty (TKA). While machine learning (ML) offers promising capabilities, most models rely solely on patient demographics and comorbidities. The American Association of Hip and Knee Surgeons (AAHKS) has proposed a set of nine risk factors to enhance current models. This study aimed to evaluate these factors using a machine learning model based on eXtreme Gradient Boosting (XGBoost). Methods:We retrospectively analyzed 783 patients who underwent primary TKA at a single academic center between January 2020 and December 2022. Preoperative clinical data and AAHKS-defined risk factors were used to train and evaluate an XGBoost model. The primary outcome measures were: (1) major complications requiring revision, (2) any complication (major or minor), and (3) residual pain at one year (Visual Analog Scale ≥4). Model performance was assessed using area under the curve (AUC), sensitivity, specificity, and accuracy. Feature importance was determined using SHapley Additive exPlanations (SHAP). Results:The model achieved moderate predictive accuracy for major complications (AUC = 0.68) and any complication (AUC = 0.65), but performed poorly in predicting residual pain (AUC = 0.53). Among AAHKS-defined risk factors, only "smoking" and "previous open reduction and internal fixation (ORIF) of the knee" showed high predictive value. Other proposed variables, such as angular deformity >15°, had limited impact. Conclusion:An XGBoost-based ML model incorporating AAHKS-defined risk factors showed moderate effectiveness in predicting postoperative complications following TKA. However, the model was unable to reliably predict residual pain. These findings underscore the need for broader inclusion of joint-specific variables and imaging data in future risk adjustment frameworks to enhance personalized care in knee arthroplasty.

Purpose:This study evaluated the relationship between functional outcomes and radiographic parameter changes in coronal and sagittal planes after robotic-assisted TKA in varus knees. Methods:Between May 2020 and December 2022, all patients who underwent robotic-assisted total knee arthroplasty (RaTKA) using Functional Alignment technique (Mako, Stryker) and met predefined inclusion and exclusion criteria were enrolled in this study. A total of 115 patients (mean age 69.7 years; BMI 27.9; ASA 2.1) with varus knee morphology (defined as an arithmetic hip-knee-ankle angle [aHKA] < 0°) and complete datasets were included in the final analysis.For analysis pre- and postoperative full-length weight-bearing radiographs and lateral knee views were obtained, and the Medial Proximal Tibial Angle (MPTA) and mechanical Lateral Distal Femoral Angle (mLDFA) in the coronal plane, as well as the Posterior Condylar Offset Ratio (PCOR) and Caton-Deschamps -Index in the sagittal plane were assessed. Based on the differences between pre- and postoperative values of the key parameters, patients were classified into two groups: Collective A, showing no deviation from preoperative alignment, and collective B, exhibiting a deviation in at least one parameter according to predefined thresholds.Clinical outcome, and patient satisfaction 2 months postoperatively using validated patient-reported outcome measures (PROMs) were compared between the collectives. Results:No significant differences were observed between the collectives regarding demographics and preoperative clinical parameters. 47 patients were assigned to Collective A, 68 to Collective B. Collective A showed significantly better OKS (26.2 vs. 29.9; p = 0.037∗), EQ-VAS (75.0 vs. 67.2; p = 0.014∗), EQ-5D (0.87 vs. 0.80; p = 0.005∗), and ROM (+4.7° vs. -9.2°; p = 0,007∗), whereas FJS was non-significantly higher in A (44.5 vs. 34.1; p = 0.062). Conclusion:Anatomic bony restoration in the coronal and sagittal plane was associated with better early outcomes. Even single-parameter deviations may impair results, underscoring the importance of accurate reconstruction in robotic TKA.

BACKGROUND:The purpose of the study was to assess whether patients who have different coronal alignment variations (functional knee phenotypes [FKP]) have distinctly different rotational alignment variations to justify an extension of the FKP concept to include rotational alignment parameters. The goals of the study were to: (1) determine the frequency of bony congruence between the anterior, distal, posterior femoral, and proximal tibial joint lines by using the extended FKP concept; and (2) connect these findings to clinical practice by simulating the impact of different alignment concepts on the most common FKP. METHODS:The posterior condylar angle (PCA) and anterior trochlear angle (ATA) were measured in 265 knees without osteoarthritic (OA). The PCA measurements of 2,692 knees with OA were extracted from the database. The patients were categorized into phenotypes based on these parameters. A phenotype represents an alignment variation of either the posterior (= PCA) or anterior femoral joint line (= ATA) in the axial plane. Rotational phenotypes (i.e., combination of alignment variations of the anterior and posterior femoral joint lines) were linked with the coronal phenotypes of these patients. The effect of three alignment concepts (mechanical, restricted, and unrestricted kinematic) on the most common FKPs was assessed. RESULTS:The distribution of the five most common coronal phenotypes did not differ among rotational phenotypes. The ATA and PCA were aligned parallel in 14.3% of the non-OA population. Distal femoral joint line (femoral mechanical angle), proximal tibial joint line (tibial mechanical angle), and PCA were aligned parallel in 17.0 and 11.2% of the non-OA and OA populations, respectively. All four joint lines (femoral mechanical angle, tibial mechanical angle, PCA, and ATA) were aligned in 2.3% of the non-OA population. CONCLUSIONS:It is crucial to emphasize that preoperative assessment of a patient's anatomy should include the anterior and posterior femoral joint lines. The extended FKP concept could aid in this assessment and help identify patients who are at risk of complications due to malalignment or those who are likely to benefit from a particular alignment concept.

Purpose:Laxity Phenotype (LP) analysis has revealed a wide range of variability among knees, showing three phenotypes in extension and three in flexion. Based on this variability recently a Laxity-Functional Knee Phenotype (L-FKP) matrix was created. This study aimed (1) to assess how well-standardised balancing goals reproduce individual LPs, and (2) to develop a treatment algorithm to convert critical L-FKP groups into optimal ones. Methods:Eighty-six knees were simulated using a validated alignment simulator to create and analyse the L-FKP matrix. Knees were classified as optimal (LP matched "all gaps equal" or lateral flexion laxity <6 mm), intermediate, and critical (presence of medial laxity in extension and/or flexion). A three-step treatment algorithm was developed for optimizing L-FKP; first, by adjusting bony anatomy within defined boundaries, second by targeting soft tissue releases if correction remained >1 mm, and third, by using additional constraint if option 1 and 2 were not sufficient. Results:Only 37 % of knees showed optimal L-FKP patterns after restoring the patient-specific B-FKP. Another third of the knees was classified as suboptimal, which could be transferred to optimal L-FKP matrix groups with minor bone adaptions, finally reaching 70 % of optimal L-FKP patterns. Still, 30 % remained within the critical L-FKP matrix groups. The algorithm enabled the structured conversion of these cases into optimal groups. Conclusions:Standardized balancing goals reconstruct optimal L-FKP groups in less than 40 % of knees, even if a personalized B-FKP workflow is applied. By minor bony modifications this amount can be increased to 70 %. A structured algorithm to restore optimal L-FKP is presented.

Purpose:Introduction of robotic systems and personalized alignment workflows have brought new challenges to Total Knee Arthroplasty (TKA) training. Simulator training is one promising option to reduce decision errors and increase efficiency. The aims of this study were (1) to analyse the effect of simulator training on quality and efficiency at different training courses using a TKA simulator, and (2) to measure the acceptance of simulator training. Methods:Over the last 3 years 35 training courses were performed and 638 surgeons trained on the Knee-CAT simulator (SOS GmbH; Regensburg; Germany). Twenty basic courses and 15 advanced alignment courses were included. All exercises were analysed for decision quality of all bone cuts, soft tissue management and balancing steps as well as for alignment. Every decision within 1 mm/degree of an optimal reference bone cut was rated as green, every decision deviating more than 1 and less than 2mm/degree was rated as yellow and every decision deviating more than 2mm/degree was rated as red. A single red decision was rated as failed exercise. Efficiency was measured by measuring time for each surgical step as well as for the entire procedure. Effect of training was measured by comparing exercise outcome data at the beginning and at the end of the training. Compliance was measured as the number of exercises performed by delegates. Results:Significant improvements for decision quality (51 %) and efficiency (62 %) were found. This positive effect was found in basic as well as in advanced TKA alignment courses. Only 36-51 % of delegates performed the training exercises at all, demonstrating a rather low compliance rate. However, delegates who completed at least one case, completed all cases in more than 85. Conclusions:Simulator training is a promising option for robotic and alignment training showing significant increase of decision quality and efficiency. However, a low compliance rate in the training courses has been observed. Future concepts may need to integrate additional training options such as video tutorials and virtual reality (VR) environment. However, proper trainee selection will remain crucial to achieve a higher compliance, as not every conventional TKA surgeon appears to be motivated in transitioning towards personalized robotic TKA surgery.

PurposeAlthough 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.MethodsIn 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.MethodsIn 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.ResultsIn 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. ConclusionsThe 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 EvidenceLevel IIa.

Background: Digital tools are being increasingly used worldwide in primary knee arthroplasty. This study aimed to analyze the utilization density of digital tools, the preferred alignment strategies, and the obstacles and benefits of implementing these technologies in German-speaking countries. Materials and methods: An online survey with 57 questions about digital tools in primary knee arthroplasty and their usage was conducted among members of the Arthroplasty Working Group (AE). The survey included questions on navigation, robotics, patient-specific instruments, individualized implants, and augmented reality. Results: The survey revealed that 18% of hospitals use navigation and 17% use robotic systems in primary total knee arthroplasty surgery. The main reasons for not implementing supportive technologies were high acquisition and ongoing costs, as well as longer surgical duration. Patient-specific instruments and individualized implants currently play a minor role. Patient-specific alignment strategies, such as kinematic (navigation: 35%; robotics: 44%) and functional alignment (navigation: 15%; robotics: 35%), are preferred in this context. With conventional instrumentation predominantly mechanical alignment was applied (79%). Discussion: The results indicate a relatively high utilization density of digital tools, which are mainly used to perform personalized alignment strategies in primary knee arthroplasty in German-speaking countries. This was particularly evident in high-volume hospitals. Economic aspects were the main reasons for not using these technologies. Future developments should aim to simplify the systems and thus achieve improved cost efficiency.

Purpose: One of the most pertinent questions in total knee arthroplasty (TKA) is: what could be considered normal coronal alignment? This study aims to define normal, neutral, deviant and aberrant coronal alignment using large data from a computed tomography (CT)-scan database and previously published phenotypes. Methods: Coronal alignment parameters from 11,191 knee osteoarthritis (OA) patients were measured based on three dimensional reconstructed CT data using a validated planning software. Based on these measurements, patients' coronal alignment was phenotyped according to the functional knee phenotype concept. These phenotypes represent an alignment variation of the overall hip knee ankle angle (HKA), femoral mechanical angle (FMA) and tibial mechanical angle (TMA). Each phenotype is defined by a specific mean and covers a range of +/- 1.5 degrees from this mean. Coronal alignment is classified as normal, neutral, deviant and aberrant based on distribution frequency. Mean values and distribution among the phenotypes are presented and compared between two populations (OA patients in this study and non-OA patients from a previously published study). Results: The arithmetic HKA (aHKA), combined normalised data of FMA and TMA, showed that 36.0% of knees were neutral within +/- 1 SD from the mean in both angles, 44.3% had either a TMA or a FMA within +/- 1-2 SD (normally aligned), 15.3% of the patients were deviant within +/- 2-3 SD and only 4.4% of them had an aberrant alignment (+/- 3-4 SD in 3.4% and >4 SD in 1.0% of the patients respectively). However, combining the normalised data of HKA, FMA and TMA, 15.4% of patients were neutral in all three angles, 39.7% were at least normal, 27.7% had at least one deviant angle and 17.2% had at least one aberrant angle. For HKA, the males exhibited 1 degrees varus and females were neutral. For FMA, the females exhibited 0.7 degrees more valgus in mean than males and grew 1.8 degrees per category (males grew 2.1 degrees per category). For TMA, the males exhibited 1.3 degrees more varus than females and both grew 2.3 degrees and 2.4 degrees (females) per category. Normal coronal alignment was 179.2 degrees +/- 2.8-5.6 degrees (males) and 180.5 > +/- 2.8-5.6 degrees (females) for HKA, 93.1 > +/- 2.1-4.2 degrees (males) and 93.8 > +/- 1.8-3.6 degrees (females) for FMA and 86.7 > +/- 2.3-4.6 degrees (males) and 88 > +/- 2.4-4.8 degrees (females) for TMA. This means HKA 6.4 varus or 4.8 degrees valgus (males) or 5.1 degrees varus to 6.1 degrees valgus was considered normal. For FMA HKA 1.1 varus or 7.3 degrees valgus (males) or 0.2 degrees valgus to 7.4 degrees valgus was considered normal. For TMA HKA 7.9 varus or 1.3 degrees valgus (males) or 6.8 degrees varus to 2.8 degrees valgus was considered normal. Aberrant coronal alignment started from 179.2 degrees +/- 8.4 degrees (males) and 180.5 > +/- 8.4 degrees (females) for HKA, 93.1 > +/- 6.3 degrees (males) 93.8 > +/- 5.4 degrees (females) for FMA and 86.7 > +/- 6.9 degrees (males) and 88 > +/- 7.2 degrees (females) for TMA. This means HKA > 9.2 degrees varus or 7.6 degrees valgus (males) or 7.9 degrees varus to 8.9 degrees valgus was considered aberrant. Conclusion: Definitions of neutrality, normality, deviance as well as aberrance for coronal alignment in TKA were proposed in this study according to their distribution frequencies. This can be seen as an important first step towards a safe transition from the conventional one-size-fits-all to a more personalised coronal alignment target. There should be further definitions combining bony alignment, joint surfaces' morphology, soft tissue laxities and joint kinematics.

Purpose The present study focuses on testing the capability of a restricted tibia-first, gap-balanced patient-specific alignment technique (PSA) to restore bony morphology and phenotypes. Methods Three-hundred and sixty-seven patients were treated with navigated total knee arthroplasty and tibia-first gap-balanced PSA technique. Boundaries for medial proximal tibial angle were 86(degrees)-92(degrees), mechanical lateral distal femoral angle 86(degrees)-92(degrees), and hip-knee-ankle angle 175(degrees)-183(degrees). Knees were classified by coronal plane alignment of the knee (CPAK), with subsequent analyses comparing pre- and postoperative distributions. Phenotype classification within CPAK groups assessed pre- and postoperative distributions. Results Preoperatively, the largest CPAK group was type II (30.8%), followed by type I (20.5%) and type V (17.8%). Postoperatively, type II remained the largest group (39%), followed by type V (30%). All groups with varus/valgus deformities (I, III, IV and VI) became smaller. While in straight legs (II, IV), the CPAK was restored in more than 70%-75%, in varus groups (I, IV) in 40%-50% and in valgus (III and VI) in 5%-18%. The joint line obliquity remained the same in the majority of knees (straight >75%; varus 63%-80%; valgus VI 95%), with the exception of CPAK III (40%). The phenotype analysis showed for straight legs a phenotype restoration of 85%, for varus 94% and for valgus 37%. Joint line convergence angle was reduced significantly in all groups from 1.8(degrees)-4.3(degrees) preoperatively to 0.6(degrees)-1.2(degrees) postoperatively. Conclusion PSA restores bony phenotypes and joint line obliquity in the majority of straight and varus knees, while most of the valgus and extreme varus knees are normalised.