Robotic-arm-assisted conversion of unicompartmental knee arthroplasty to total knee arthroplasty.

Introduction: The purpose of this study was to describe a novel robotic-arm-assisted UKA to TKA conversion technique and evaluate the patient reported and clinical outcomes in these patients. Methods: A retrospective review between 2017 and 2022 was conducted of patients that underwent robotic-arm-assisted UKA to TKA conversion. Charts were reviewed for patient demographics, indications for conversion from UKA to TKA, operative technique, implants used, postoperative complications, and patient-reported outcome measures (PROMs). The surgical technique resembles that of primary TKA, with the major exception of registering the robotic arm with retained UKA implants and removing the implants only when verification is complete. There were 44 robotic-arm-assisted UKAs in 41 patients were included in the study. Indications for UKA conversion to TKA included: 33 patients who had osteoarthritis progression (75%), 7 aseptic loosening (16%), 2 unspecified pain (4.5%), 1 polyethylene wear (2.3%), and 1 prosthetic joint infection (2.3%). Uncemented cruciate-retaining (CR) implants were used in 38 of the 44 robotic-arm-assisted TKAs (86.5%). The other six utilized cemented implants: four CR femurs (9.1%), six tibial baseplates (13.6%), four tibial stems (9.1%), and four medial tibial augments (9.1%). Results: The PROMs significantly improved at 1-year follow-up, with the average KOOS JR score increasing from 48.1 to 68.7 (P < 0.001), and the r-WOMAC score decreasing from 25.7 to 10.6 (P = 0.003). Two patients developed prosthetic joint infections (4.5%), one developed aseptic loosening of the femoral component (2.3%), and one developed a superficial surgical site infection requiring superficial irrigation and debridement (2.3%). Overall survivorship was 93.18% at 1.8 years, and aseptic survivorship was 97.73%. Conclusion: Robotic-arm-assisted UKA to TKA conversion exhibited improved patient-reported outcomes and low revision and complication rates. Improved implant placement achieved with robotic-arm-assistance may improve the functional and clinical outcomes following these surgeries.

A multicentre prospective assessment of the utility of robotic assisted total hip arthroplasty with virtual range of motion on intraoperative implant positioning.

BACKGROUND: The growing adoption of robotic-assistance during total hip arthroplasty (THA) has provided novel means through which a patient's anatomy and dynamic spinopelvic relationship can be incorporated into surgical planning. However, the impact of enhanced technologies on intraoperative decision-making and changes to component positioning has not yet been described. METHODS: A multicentre, prospective study included 105 patients (52% women) patients who underwent robotic-assisted THA with the integration of software that incorporates a patient's pelvic tilt (PT) and virtual range-of-motion (VROM) for impingement modeling. The primary outcome of the study was the percentage of patients who underwent changes to the preoperative plan for cup position after incorporating the data from the software. RESULTS: Utilising the intraoperative VROM information, the preoperative plan for cup position was changed from the default (40° inclination and 20° anteversion) in 82/105 (78%) cases. When stratifying by spinopelvic mobility, 64% were considered normal (change ⩾ 10° and ⩽30°), 27% were stiff (change < 10°), and 9% were hypermobile (change > 30°). For all cohorts, the majority of cases (78%) deviated from the 40° inclination and 20° version target. When evaluating the proportion of cases within the Lewinnek and Callanan safe zones based on spinopelvic mobility, 19% of cases within the normal group were planned outside of both zones compared to 39% of stiff cases and 10% of hypermobile cases. CONCLUSIONS: Utilising the latest version of robotic-assisted THA software, the preoperative plan for cup position was changed in the vast majority (78%) of patients, causing substantial deviations from traditional, generic cup targets.

Characterizing Osteophyte Formation in Knee Osteoarthritis: Application of Machine Learning Quantification of a Computerized Tomography Cohort: Implications for Treatment.

BACKGROUND: Osteophytes are commonly used to diagnose and guide knee osteoarthritis (OA) treatment, but their causes are unclear. Although they are not typically the focus of knee arthroplasty surgeons, they can predict case difficulty and length. Furthermore, their extent and location may yield much information about the knee joint status. The aims of this computed tomography-based study in patients awaiting total or partial knee arthroplasty were to: (1) measure osteophyte volume in anatomical subregions and relative change as total volume increases; (2) determine whether medial and/or lateral OA affects osteophyte distribution; and (3) explore relationships between osteophytes and OA severity. METHODS: Data were obtained from 4,928 computed tomography scans. Machine-learning-based imaging analyses enabled osteophyte segmentation and quantification, divided into anatomical regions. Mean three-dimensional joint space narrowing was assessed in medial and lateral compartments. A Bayesian model assessed the uniformity of osteophyte distribution. We correlated femoral osteophyte volumes with B-scores, a validated OA status measure. RESULTS: Total tibial (25%) and femoral osteophyte volumes (75%) within each knee correlated strongly (R2 = 0.85). Medial osteophytes (65.3%) were larger than lateral osteophytes (34.6%), with similar proportions in both the femur and tibia. Osteophyte growth was found in all compartments, and as total osteophyte volume increased, the relative distribution of osteophytes between compartments did not markedly change. No evidence of variation was found in the regional distribution of osteophyte volume between knees with medial, lateral, both, or no three-dimensional joint space narrowing in the femur or tibia. There was a direct relationship between osteophyte volume and OA severity. CONCLUSIONS: Osteophyte volume increased in both medial and lateral compartments proportionally with total osteophyte volume, regardless of OA location. The peripheral position of femoral osteophytes does not appear to contribute to load-bearing. This suggests that osteophytic growth represents a 'whole-knee'/global response. This work may have broad applications for knee OA, both surgically and nonoperatively.

Surgical factors play a critical role in predicting functional outcomes using machine learning in robotic-assisted total knee arthroplasty.

PURPOSE: Predictive models help determine predictive factors necessary to improve functional outcomes after total knee arthroplasty (TKA). However, no study has assessed predictive models for functional outcomes after TKA based on the new concepts of personalised surgery and new technologies. This study aimed to develop and evaluate predictive modelling approaches to predict the achievement of minimal clinically important difference (MCID) in patient-reported outcome measures (PROMs) 1 year after TKA. METHODS: Four hundred thirty robotic-assisted TKAs were analysed in this retrospective study. The mean age was 67.9 ± 7.9 years; the mean body mass index (BMI) was 32.0 ± 6.8 kg/m2. The following PROMs were collected preoperatively and 1-year postoperatively: knee injury and osteoarthritis outcome score for joint replacement, Western Ontario and McMaster Universities osteoarthritis index (WOMAC) Function, WOMAC Pain. Demographic data, preoperative CT scan, implant size, implant position on the robotic system and characteristics of the joint replacement procedure were selected as predictive variables. Four machine learning algorithms were trained to predict the MCID status at 1-year post-TKA for each PROM survey. 'No MCID' was chosen as the target. Models were evaluated by class discrimination (F1-score) and area under the receiver operating characteristic curve (ROC-AUC). RESULTS: The best-performing model was ridge logistic regression for WOMAC Function (area under the curve [AUC] = 0.80, F1 = 0.48, sensitivity = 0.79, specificity = 0.62). Variables most strongly contributing to not achieving MCID status were preoperative PROMs, high BMI and femoral resection depth (posterior and distal), supporting functional positioning principles. Conversely, variables contributing to a positive outcome (achieving MCID) were medial/lateral alignment of the tibial component, whether the procedure was an outpatient surgery and whether the patient received managed Medicare insurance. CONCLUSION: The most predictive variables included preoperative PROMs, BMI and surgical planning. The surgical predictive variables were valgus femoral alignment and femoral rotation, reflecting the benefits of personalised surgery. Including surgical variables in predictive models for functional outcomes after TKA should guide clinical and surgical decision-making for every patient. LEVEL OF EVIDENCE: Level III.

Evaluation of an Initial Robotic-Assisted Direct Anterior Approach Cohort Receiving a New Short Metaphyseal Filling Collared Femoral Implant.

BACKGROUND: Shorter, metaphyseal-filling collared stems have become popular with the direct anterior approach (DAA), based on their ease of broaching and insertion through less invasive surgical exposures. To aid with the DAA, robotic-assisted technology provides three-dimensional computed tomography (CT) preoperative planning and intraoperative guidance to accurately assess stem version. With other femoral stems, this has been shown to provide more accurate implant planning and improved patient outcomes. The purpose of this study was to understand femoral stem placement predictability and patient outcomes for a newly designed metaphyseal-filling collared stem system through a DAA in combination with a robotic-assisted system during a single surgeon's initial cases. MATERIALS AND METHODS: A single high-volume surgeon, experienced with robotic-assisted DAA total hip arthroplasty (THA), adopted the use of a metaphyseal-filling collared stem. Intraoperative data and patient outcomes up to six months postoperative were collected prospectively during the surgeon's first 123 cases. Student's t-tests (α=0.05) were used for statistical comparisons. Intraoperative and radiographic assessments were performed for all 123 cases. RESULTS: The estimated version with neck-cut view of the robotic-assisted system was 13.81 ± 3.81°. The final version measurement captured with the robotic-assisted system was 16.56 ± 6.61°. The difference between the estimated version and robotic-assisted measured version was, on average, 2.68 ± 5.7° (p<0.001). The femoral stem sat at the level of the calcar in all but five cases. There were no intraoperative or postoperative periprosthetic fractures. Patients reported significant improvements in reduced Hip dysfunction and Osteoarthritis Outcome Score for Joint Replacement (HOOS-JR), Patient-Reported Outcomes Measurement Information System (PROMIS 10), and Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores throughout their six-month recovery process with no patients reporting a periprosthetic joint infection, fracture, or dislocation. CONCLUSION: The use of a metaphyseal-filling collared stem with robotic-assisted DAA resulted in adequate stem version when assessed visually and with CT scan assessments. Version estimation values were improved upon over visual assessments when using the robotic system that maps out the proximal femur and the improvement was also demonstrated when compared to the prior literature. This may also be related to the stem's collar providing a visual guide during stem placement posterior fill of this triple-tapered stem design. Although further follow up is needed to assess longer-term outcomes, at six months postoperatively, patients had significant improvements in patient-reported outcomes with all patients reporting minimal to no restrictions with their THA.

Three-dimensional-based native alignment phenotype classification system: Description for use in planning for deformities during total knee arthroplasty.

Introduction: Total knee arthroplasty (TKA) is a complex surgical procedure that traditionally relies on two-dimensional radiographs for pre-operative planning. These radiographs may not capture the intricate details of individual knee anatomy, potentially limiting the precision of surgical interventions. With advancements in imaging technology, there is an opportunity to refine TKA outcomes. This study introduces the Native Alignment Phenotype classification system that is based on pre-operative 3-dimensional computed tomography (CT) scans, aiming to provide a more detailed understanding of knee deformities and their influence on characterizing knee osteoarthritis and planning for TKA procedures. Methods: There were 1406 pre-operative non-weight-bearing CT scans analyzed by a single surgeon experienced with robotically-assisted total knee arthroplasties. These scans were converted into three-dimensional models, focusing on the coronal and sagittal planes. Intraoperatively, the robotic system was used to capture native coronal and sagittal deformities for each patient. These values were captured with the patient's leg held in a non-stress, extension pose. A new classification system, 'The Native Alignment Phenotype', was developed to categorize the specific differences between individual knees. Results: There were four primary knee malalignments identified: varus deformity; valgus deformity; and two deformities in the sagittal plane. These malalignments were further categorized based on the degrees of deviation, creating groups with 5° coronal and sagittal ranges. A total of 77 phenotypic alignment patterns were found based on the analyzed cohort. In the coronal plane, varus HKA deformity between 6 and 10° was the most common, with 36.9% of the cases, followed by varus HKA alignment, which was between 0 and 5°, representing 34.3% of the cases. In the sagittal plane, neutral and flexion contracture deformities between 0 and 5° were the most common, with 32.6% of the cases, followed by a fixed flexion contracture alignment, which was between 6 and 10°, representing 28.7% of the cases. When combining coronal and sagittal planes, the most common alignment was the varus between 0 and 5° with a flexion contracture between 0 and 5° (12.5% of cases), closely followed by the varus between 6 and 10° with a flexion contracture between 6 and 10° (12.4% of cases). Conclusion: The Native Alignment Phenotype classification system offers a nuanced understanding of knee deformities based on three-dimensional (CT scan) assessments, potentially leading to improved surgical outcomes in TKA. By leveraging the detailed data from the CT scans, this system provides a more comprehensive view of the knee's anatomy, emphasizing the importance of individualized, data-driven approaches in knee surgery.

True Confessions of Neutral Mechanical Disciple-How I Learned to Love a Patient-Specific Target.

INTRODUCTION: Classical neutral mechanical alignment in total knee arthroplasty (TKA) has been a standard paradigm, while more recently, other alignment schemas, such as kinematic, individualized, and functional, have been explored. This study aimed to investigate the effect of three-dimensional (3D) computed tomography (CT)-based surgical robotics inputs on a classically trained surgeon's TKA component positions and alignment targets over time. MATERIALS AND METHODS: Data from 1,394 consecutive robotically-assisted TKAs by a single surgeon from 2016 to 2020 were analyzed. Metrics collected included pre-balance planned implant component positions, final planned implant component positions after soft tissue balancing, and constitutional alignment from CT scans. Joint line obliquity was plotted against the arithmetic hip-knee angle (aHKA) using coronal plane alignment of the knee (CPAK). Three categories of alignment strategy were defined: true mechanical alignment (tMA), adjusted mechanical alignment (aMA), and no mechanical alignment (noMA). RESULTS: A shift to overall varus component positioning was observed over the years. Joint line obliquity according to CPAK showed a wider spread in later years, and the distribution of tibial and femoral coronal alignment angles expanded over time. CONCLUSION: The study revealed a change in alignment targets and final positioning of components away from neutral biomechanical axes in a large volume of TKAs by a single, classically trained surgeon over five years of using a robotic arm-assisted TKA system with CT-based planning. The most dominant factor for this change was the use of 3D CT planning, allowing the surgeon to assess patient-specific anatomy and plan accordingly. Outcome data is needed to determine if this change in behavior and surgical technique was beneficial. In summary, using a CT scan-based robotically assisted technique led to a gradual and complete shift from tMA to predominantly a non-mechanically aligned philosophy in TKA.

Patient-reported and radiographic outcomes of a porous-coated acetabular cup in robotic assisted total hip arthroplasty at 2-year follow up.

Introduction: Several implant manufacturers have developed ultra-porous metal substrate acetabular components recently. Despite this, data on clinical and radiographic outcomes remain limited. Our study evaluated postoperative patient-reported outcome measures (PROMs) and radiographic analyses in patients fitted with a novel acetabular porous-coated component. Methods: A total of 152 consecutive patients underwent a total hip arthroplasty by a single orthopaedic surgeon. All patients underwent surgery utilizing the same CT-scan based robotic-assisted device with the same porous cementless acetabular shell. They received standardized postoperative physical therapy, rehabilitation, and pain protocols. Preoperatively, first postoperative visit, 6-months, 1-year, and 2-years, patients were evaluated based on Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain, physical function, and total scores; 2) Patient-Reported Outcomes Measurement Information System (PROMIS)-10 physical and mental scores; 3) Hip Disability and Osteoarthritis Outcome Score (HOOS)-Jr scores; as well as 4) acetabular component positions and 5) evidence of acetabular component loosening. Results: Significant improvements were observed by 6 months in WOMAC pain, physical function, and total scores (p < 0.05), maintained at 1 and 2 years. PROMIS-10 physical scores also improved significantly from preoperative to 6 months postoperative and remained so at 1 and 2 years postoperative (p < 0.05). No significant changes were found in PROMIS-10 mental scores. HOOS-Jr scores significantly improved from preoperative to 6 months postoperative and remained so through 2 years (p < 0.05). At 6 months, slight changes were noted in abduction angle and horizontal and vertical offset. Radiolucencies, initially found in 3 shells, reduced to 1 shell with 2 new radiolucencies by 6 months, and remained stable with no subsequent operative interventions. At 1 year and 2 years, no radiographic abnormalities were noted, including complete resolution of prior radiolucencies as well as stable components. Conclusion: This porous cementless acetabular shell, implanted with CT-scan-based robotic-assisted techniques, demonstrated excellent postoperative PROMs at 2 years. Stable radiolucencies suggest good component stability. The early stable clinical and radiographic results suggest promising long-term outcomes with this device. Level of evidence: III (retrospective cohort study).

Mako® Robotic-Arm Assisted Total Hip Arthroplasty: Avoiding Impingement with Updated THA Software.

The use of robotic-assisted total hip arthroplasty and three-dimensional computed tomography scan-based templating has become increasingly popular over the last 10 years. However, proper planning and execution are vital to producing optimal patient outcomes. In order to achieve these outcomes, the robotic-assisted system requires training, familiarity, and experience. The goal of this article is to provide clear and condensed examples of preoperative planning, as well as adjustments that one can make to avoid impingement. The surgical technique for robotic-assisted total hip arthroplasty is also briefly discussed. Examples will be given using the latest computed tomography (CT) scan-based robotic platform for osteoarthritic hips, with specific examples of various cases of impingement that might be encountered by the surgeon and how to ultimately avoid this problem when performing the arthroplasty. This article, through case histories, will discuss the various principles and adjustments that can be made to place components in the ideal location based on individual anatomy.

Mako™ Robotic-Arm Assisted Medial Unicompartmental Knee Arthroplasty: Surgical Technique from the Office to the Operating Room.

The utilization of robotic-arm assisted unicompartmental knee arthroplasty (UKA) or partial knee arthroplasty (PKA) for the treatment of medial unicompartmental osteoarthritis has continued to increase. This is in part due to the consistently reproducible accuracy and precision of implant planning, intra-operative ligament balancing software, tracking optimization software, robotic-arm assisted bone preparation, excellent survivorship rates, and improvements in many patient-reported outcomes compared to manual UKA, using the Stryker Mako™ Robotic Partial Knee System (Stryker, Mako Surgical Corp., Mahwah, New Jersey). Training in the utilization of robotic-arm assistance can be time-consuming and comes with an associated learning curve even after the in-person training and coursework is complete, like most procedures. Therefore, our aim was to describe the pre-operative planning and intra-operative surgical technique for using a robotic-arm assisted partial knee system for UKA/PKA in patients who have unicompartmental medial knee osteoarthritis. Specifically, we will discuss: 1) pre-operative planning; 2) operative setup; 3) intra-operative steps; 4) execution of the plan; and 5) trialing, implantation, and final assessments.

Variability of pre-operative functional pelvic tilt in total hip arthroplasty patients.

PURPOSE: Pelvic tilt (PT) is important to consider when planning total hip arthroplasty (THA) due to its dynamic impact on acetabular orientation. The degree of sagittal pelvic rotation varies during functional activities and can be difficult to measure without proper imaging. The purpose of this study was to evaluate PT variation in the supine, standing, and seated positions. METHODS: A multi-centre cross-sectional study was performed that included 358 THA patients who had preo-perative PT measured from supine CT scan and standing and upright seated lateral radiographs. Supine, standing, and seated PT and associated changes between functional positions were evaluated. Anterior PT was assigned a positive value. RESULTS: In the supine position, mean PT was 4° (range, -35° to 20°), where 23% had posterior PT and 69% anterior PT. In the standing position, mean PT was 1° (range, -23° to 29°), where 40% had posterior PT and 54% anterior PT. In the seated position, mean PT was -18° (range, -43° to 47°), where 95% had posterior PT and 4% anterior PT. From standing to seated, the pelvis rotated posteriorly in 97% of cases (maximum 60°) with 16% of cases considered stiff (change ≤ 10°) and 18% of cases considered hypermobile (change ≥ 30°). CONCLUSION: Patients undergoing THA have marked PT variation in the supine, standing, and seated positions. There was wide variability in PT change from standing to seated, with 16% of patients considered stiff and 18% considered hypermobile. Functional imaging should be performed on patients prior to THA to allow for more accurate planning.

Clinical Outcomes after Computed Tomography-Based Total Knee Arthroplasty: A Minimum 3-Year Analyses.

Computed tomography (CT) scan-based three-dimensional (3D) modeling operative technology has been shown to improve upon results of manual total knee arthroplasties (TKAs). Although there are many reports on superior precision of this CT-based technology, there has been continuing interest regarding extended clinical outcomes. The purpose of this study was to compare their clinical outcomes with manual TKAs at approximately 3-year follow-up. Specifically, we analyzed: (1) survivorship, (2) functional outcomes, (3) complications, and (4) radiographic outcomes (i.e., alignment, progressive radiolucencies). A total of 210 patients receiving CT-based TKAs performed by a single surgeon at a single center between July 1, 2016, and February 16, 2018, were compared with 210 manual TKAs completed by the same surgeon immediately preceding implementation of the CT-based technology. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) surveys were collected at ∼3 years postoperatively. Subgroup analyses of pain and physical function scores were performed. Follow-up radiographs were evaluated for alignment, loosening, and/or progressive radiolucencies. There was 100% survivorship at final follow-up. The postoperative mean pain scores for the CT-based cohort and manual cohort were 1 ± 2 (range, 0-14) and 2 ± 3 (range, 0-17), respectively (p < 0.05). The postoperative mean physical function scores for the CT-based cohort and manual cohort were 3 ± 4 (range, 0-18) and 5 ± 5 (range, 0-19), respectively (p < 0.05). The postoperative mean total WOMAC scores for the CT-based cohort and manual cohort were 5 ± 4 (range, 0-32) and 7 ± 8 (range, 0-35), respectively (p < 0.05). There were low numbers of postoperative complications at final follow-up in either cohort. None exhibited progressive radiolucencies by final follow-up. The 3-year postoperative clinical outcomes support excellent survivorship and radiographic outcomes, low complication rates, as well as improved pain, physical function, and total WOMAC scores for CT-based TKAs. Therefore, patients who undergo CT-based 3D modeling TKAs should expect to have superior long-term clinical outcomes.

Results of Robotic-Assisted Versus Manual Total Knee Arthroplasty at 2-Year Follow-up.

Robotic-assisted technology has been developed to optimize the consistency and accuracy of bony cuts, implant placements, and knee alignments for total knee arthroplasty (TKA). With recently developed designs, there is a need for the reporting longer than initial patient outcomes. Therefore, the purpose of this study was to compare manual and robotic-assisted TKA at 2-year minimum for: (1) aseptic survivorship; (2) reduced Western Ontario and McMaster Universities Osteoarthritis Index (r-WOMAC) pain, physical function, and total scores; (3) surgical and medical complications; and (4) radiographic assessments for progressive radiolucencies. We compared 80 consecutive cementless robotic-assisted to 80 consecutive cementless manual TKAs. Patient preoperative r-WOMAC and demographics (e.g., age, sex, and body mass index) were not found to be statistically different. Surgical data and medical records were reviewed for aseptic survivorship, medical, and surgical complications. Patients were administered an r-WOMAC survey preoperatively and at 2-year postoperatively. Mean r-WOMAC pain, physical function, and total scores were tabulated and compared using Student's t-tests. Radiographs were reviewed serially throughout patient's postoperative follow-up. A p < 0.05 was considered significant. The aseptic failure rates were 1.25 and 5.0% for the robotic-assisted and manual cohorts, respectively. Patients in the robotic-assisted cohort had significantly improved 2-year postoperative r-WOMAC mean pain (1 ± 2 vs. 2 ± 3 points, p = 0.02), mean physical function (2 ± 3 vs. 4 ± 5 points, p = 0.009), and mean total scores (4 ± 5 vs, 6 ± 7 points, p = 0.009) compared with the manual TKA. Surgical and medical complications were similar in the two cohorts. Only one patient in the manual cohort had progressive radiolucencies on radiographic assessment. Robotic-assisted TKA patients demonstrated improved 2-year postoperative outcomes when compared with manual patients. Further studies could include multiple surgeons and centers to increase the generalizability of these results. The results of this study indicate that patients who undergo robotic-assisted TKA may have improved 2-year postoperative outcomes.

Time-Based Learning Curve for Robotic-Assisted Total Knee Arthroplasty: A Multicenter Study.

Robotic-assisted total knee arthroplasty (RA-TKA) has been shown to improve the accuracy of bone resection, reduce radiographic outliers, and decrease iatrogenic injury. However, it has also been shown that RA-TKA surgical times can be longer than manual surgery during adoption. The purpose of this article was to investigate (1) the characteristics of the operative time curves and trends, noting the amount of surgeons who improved, for those who performed at least 12 cases (based on initial modeling); (2) the proportion of RA surgeons who achieved the same operative times for RA-TKA as compared with manual TKAs; and (3) the number of RA-TKA cases until a steady-state operative time was achieved. TKA operative times were collected from 30 hospitals for 146 surgeons between January 1, 2016, and December 31, 2019. A hierarchical Bayesian model was used to estimate the difference between the mean RA-TKA times by case interval and the weighted baseline for manual times. The learning curve was observed at the 12th case. Therefore, operative times were analyzed for each surgeon who performed at least 12 RA-TKA cases to determine the percentage of these surgeons who trended toward a decrease or increase in their times. These surgeons were further analyzed to determine the proportion who achieved the same operating times as manual TKAs. A further hierarchical Bayesian model was used to determine when these surgeons achieved steady-state operative times. There were 60 surgeons (82%) who had decreasing surgical times over the first 12 RA-TKA cases. The remaining 13 (18%) had increasing surgical times (mean increase of 0.59 minutes/case). Approximately two-thirds of the surgeons (64%) achieved the same operating times as manual cases. The steady-state time neutrality occurred between 15 and 20 cases and beyond. This study demonstrated the learning curve for a large cohort of RA-TKAs. This model demonstrated a learning curve between 15 and 20 cases and beyond. These are important findings for this innovative technology.

The Association of Patient and Procedural Factors on Improved Outcomes: A Cluster Analysis on 853 Total Knee Arthroplasty Patients.

Many studies involving robotic-assisted total knee arthroplasty (RATKA) have demonstrated superiority regarding soft tissue balance and consistency with alignment target achievement. However, studies investigating whether RATKA is associated with improved patient outcomes regarding physical function and pain are also important. Therefore, we performed a cluster analysis and examined factors that contributed to differences in patient-reported outcome measures (PROMs). Specifically, we analyzed: (1) reduced WOMAC (rWOMAC) scores regarding pain and function; (2) usage of RATKA; (3) common patient comorbidities; as well as (4) patient demographic factors. The rWOMAC score is an abbreviated PROM that includes pain and physical function domains. This study analyzed 853 patients (95 conventional and 758 robotic-assisted) who had completed preoperative, 6-month, and 1-year postoperative rWOMAC surveys. Two clusters were constructed using rWOMAC pain and function scores at 1 year. Cluster 1 included 753 patients who had better outcomes at 1 year (mean rWOMAC pain = 0.9, mean rWOMAC function = 1.4), and cluster 2 included 100 patients who had worse outcomes at 1 year (mean rWOMAC pain = 7.7, mean rWOMAC function = 10.4). The clusters were compared to determine (1) how scores improved and (2) what patient characteristics were significantly different between clusters. Cluster 1 demonstrated greater improvement from preoperative to 6 months or 1 year (p = 0.0013 for pain preoperative to 6 months, p< 0.0001 for other measures) and 6 months to 1 year (p< 0.0001). Comparisons demonstrated that cluster 1 had older patients (67 vs. 65 years, p = 0.0479) who had lower body mass index or BMIs (31.8 vs. 33.9 kg/m2, p = 0.0042) and no significant differences in sex (p = 0.7849). Cluster 1 also had a significantly higher percentage of RATKA patients (90 vs. 79%, p< 0.001). Cluster analyses provided differentiating factors which were associated with improved postoperative rWOMAC pain and function scores at 1 year. Patients undergoing robotic-assisted TKA were associated with better rWOMAC pain and function scores from preoperative to 6 months and 1 year.

Clinical results and patient-reported outcomes following robotic-assisted primary total knee arthroplasty : a multicentre study.

AIMS: The aim of this study was to report patient and clinical outcomes following robotic-assisted total knee arthroplasty (RA-TKA) at multiple institutions with a minimum two-year follow-up. METHODS: This was a multicentre registry study from October 2016 to June 2021 that included 861 primary RA-TKA patients who completed at least one pre- and postoperative patient-reported outcome measure (PROM) questionnaire, including Forgotten Joint Score (FJS), Knee Injury and Osteoarthritis Outcomes Score for Joint Replacement (KOOS JR), and pain out of 100 points. The mean age was 67 years (35 to 86), 452 were male (53%), mean BMI was 31.5 kg/m2 (19 to 58), and 553 (64%) cemented and 308 (36%) cementless implants. RESULTS: There were significant improvements in PROMs over time between preoperative, one- to two-year, and > two-year follow-up, with a mean FJS of 17.5 (SD 18.2), 70.2 (SD 27.8), and 76.7 (SD 25.8; p < 0.001); mean KOOS JR of 51.6 (SD 11.5), 85.1 (SD 13.8), and 87.9 (SD 13.0; p < 0.001); and mean pain scores of 65.7 (SD 20.4), 13.0 (SD 19.1), and 11.3 (SD 19.9; p < 0.001), respectively. There were eight superficial infections (0.9%) and four revisions (0.5%). CONCLUSION: RA-TKA demonstrated consistent clinical results across multiple institutions with excellent PROMs that continued to improve over time. With the ability to achieve target alignment in the coronal, axial, and sagittal planes and provide intraoperative real-time data to obtain balanced gaps, RA-TKA demonstrated excellent clinical outcomes and PROMs in this patient population.Cite this article: Bone Jt Open 2022;3(7):589-595.

Clinical Outcomes After Computed Tomography-Based Total Knee Arthroplasty: A Surgeon's First 1,000 Cases.

INTRODUCTION: Computed tomography scan (CT)-based three-dimensional (3D) modeling operative technologies have been shown to improve upon many perioperative results of manual total knee arthroplasties (TKAs). Although patient satisfaction has been reported for CT-based TKAs, studies evaluating large cohorts are limited. The purpose of this study was to compare the clinical outcomes of a surgeon's first 1,000 CT-based TKAs with manual TKAs during a minimum follow-up time of approximately six months. Specifically, we analyzed: (1) survivorship; (2) functional outcomes; (3) radiographic outcomes (i.e., alignment, progressive radiolucencies); and (4) complications. MATERIALS AND METHODS: A total of 1,000 consecutive primary CT-based total knee arthroplasty cases (988 patients) performed by a single surgeon at a single center between July 1, 2016 and July 1, 2021 were compared to a total of 1,000 consecutive manual TKAs (996 patients) completed by the same surgeon between May 18, 2013 and July 1, 2016. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) surveys were collected at approximately six months postoperatively. Subgroup analyses were performed on the physical function and pain scores. Follow-up radiographs were also examined for alignment, progressive radiolucencies, and/or loosenings. RESULTS: There was 100% survivorship at approximately six months follow up. The mean physical function score for the manual cohort and CT-based cohort were 10 ± 4 (range, 0 to 32) and 5 ± 4 (range, 0 to 22), respectively (p<0.05). The mean pain score for the manual cohort and CT-based cohort were 4 ± 4 (range, 0 to 20) and 3 ± 2 (range, 0 to 15), respectively (p<0.05). The mean total WOMAC score for the manual cohort and CT-based cohort were 13 ± 9 (range, 0 to 44) and 8 ± 7 (range, 0 to 33), respectively (p<0.05). None of the manual or CT-based cases exhibited progressive radiolucencies by final follow up. There were no postoperative medical and/or surgical complications at final follow up in the two cohorts. DISCUSSION/CONCLUSIONS: The 1,000 CT-based TKA clinical outcomes from this study continue to support great survivorship and radiographic outcomes, minimal complications, as well as improved physical function, pain, and total WOMAC scores. Therefore, those who undergo CT-based 3D modeling total knee arthroplasties should have the advantage of superior patient satisfaction.

Topic selection process in health technology assessment agencies around the world: a systematic review.

OBJECTIVE: The purpose of this study was to systematically review the process for topic selection by health technology assessment (HTA) agencies around the world to provide the knowledge base for the improvement of topic selection frameworks in HTA agencies. METHODS: A systematic search was conducted in PubMed and EMBASE to identify papers up to February 2019. Gray literature was identified by screening the Web sites of HTA agencies on the nonprofit member list of the International Network of Agencies for Health Technology Assessment (INAHTA). Data were extracted for each HTA agency and synthesized, with issues including general contextual information about each agency and the process of topic selection. RESULTS: Out of forty-nine nonprofit members of INAHTA, a total of seventeen HTA agencies with a framework for topic selection were identified from twenty-two included papers/documents. Multiple criteria were used for topic selection in all frameworks and agencies undertook multiple steps, which could include the specification of criteria for topic selection, identification of topics, short listing of potential topics, scoping of potential topics, scoring and ranking of potential topics, and deliberation and decision on final topics for HTA. Shortcomings were found in relation to methods of scoring and ranking as well as lack of monitoring and the evaluation of the process. CONCLUSIONS: Our study provides insights into the current practice of topic selection in HTA agencies. Multiple criteria decision analysis methodology appears highly relevant to these processes. A consensus approach for the development of methods of topic selection would be valuable for the HTA community.

Learning Curve of Robotic-Assisted Total Knee Arthroplasty for a High-Volume Surgeon.

The learning curve has been established for robotic-assisted total knee arthroplasty (RATKA) during the first month of use; however, there have been no studies evaluating this on a longer term. Therefore, the purpose of this study was to compare operative times for three cohorts during the first year following adoption of RATKA (initial, 6 months, and 1 year) and a prior cohort of manual TKA. We investigated both mean operative times and the variability of operative time in each cohort. This is a learning curve study comparing a single surgeon's experience using RAKTA. The study groups were made up of two cohorts of 60 cementless RATKAs performed at ∼6 months and 1 year of use. A learning curve was created based on the mean operative times and individual operative times were stratified into different cohorts for comparison. Study groups were compared with the surgeon's initial group of 20 cemented RATKAs and 60 cementless manual cases. Descriptive numbers were compiled and mean operative times were compared using Student's t-tests for significant differences with a p-value of < 0.05. The mean surgical times continued to decrease after 6 months of RATKA. In 1 year, the surgeon was performing 88% of the RATKA between 50 and 69 minutes. The initial cohort and 1-year robotic-assisted mean operative times were 81 and 62 minutes, respectively (p < 0.00001). Mean 6-month robotic-assisted operative times were similar to manual times (p = 0.12). A significant lower time was found between the mean operative times for the 1-year robotic-assisted and manual (p = 0.008) TKAs. The data show continued improvement of operative times at 6 months and 1 year when using this new technology. The results of this study are important because they demonstrate how the complexity of a technology which initially increases operative time can be overcome and become more time-effective than conventional techniques.

The Accuracy of Computed Tomography-Based, Three-Dimensional Implant Planning in Robotic-Assisted Total Knee Arthroplasty.

Advanced imaging used in robotic-assisted total knee arthroplasty (TKA), such as computed tomography (CT)-based three-dimensional (3D) planning, may provide an accurate means of implant sizing preoperatively. The purpose of this study was to examine preoperative CT-based implant planning accuracy for robotic-assisted TKA in patients who have (1) varus deformities, (2) valgus deformities, (3) neutral alignment, and (4) retained hardware. A total of 393 patients underwent a robotic-assisted TKA by a single surgeon received preoperative CT scans. The surgeon reviewed the CT-based model preoperatively and recorded the expected size of the components. The final implants used in each case were recorded and compared with the surgeon's preoperative plan. In all groups of patients, the surgeon's CT-based implant plan was within one size of the implant utilized 100% of the time for both the tibiae and femora. Overall, the surgeon was exactly matched in 319 (81%) and 315 (80%) cases for the femoral and tibial components, respectively. For the femoral component, the mean age for patients in whom the original plan was exactly matched was younger than those whose implants were upsized and older than patients those implants were downsized (p = 0.024). Other patient demographics and preoperative knee alignment were not associated with predictive accuracy for femoral or tibial components. Our results demonstrate how preoperative CT-based, 3D planning for robotic-assisted TKA is accurate to within one size of the components in every case (100%), and exactly matched in 80%. The results of this study are important because they demonstrate how CT-based preoperative implant planning for TKA is reliable and accurate across all native knee alignments and other patient-specific factors. In addition, they build on a previous study by the same single surgeon, demonstrating that predictive ability can improve over time. This may be important as we move toward more outpatient surgery with less ability for prostheses inventory at ambulatory sites.