Excessive posterior pelvic tilt (PT) may increase the risk of anterior instability after total hip arthroplasty (THA). The aim of this study was to investigate the changes in PT occurring from the preoperative supine to postoperative standing position following THA, and identify factors associated with significant changes in PT. Supine PT was measured on preoperative CT scans and standing PT was measured on preoperative and one-year postoperative standing lateral radiographs in 933 patients who underwent primary THA. Negative values indicate posterior PT. Patients with > 13° of posterior PT from preoperative supine to postoperative standing (ΔPT ≤ -13°) radiographs, which corresponds to approximately a 10° increase in functional anteversion of the acetabular component, were compared with patients with less change (ΔPT > -13°). Logistic regression analysis was used to assess preoperative demographic and spinopelvic parameters predictive of PT changes of ≤ -13°. The area under receiver operating characteristic curve (AUC) determined the diagnostic accuracy of the predictive factors.Aims
Methods
Acetabular component positioning is commonly referenced with the pelvis in the supine position in direct anterior approach THA. Changes in pelvic tilt (PT) from the pre-operative supine to the post-operative standing positions have not been well investigated and may have relevance to optimal acetabular component targeting for reduced risk of impingement and instability. The aims of this study were therefore to determine the change in PT that occurs from pre-operative supine to post-operative standing, and whether any factors are associated with significant changes in tilt ≥13° in posterior direction. 13° in a posterior direction was chosen as that amount of posterior rotation creates an increase in functional anteversion of the acetabular component of 10°. 1097 THA patients with pre-operative supine CT and standing lateral radiographic imaging and 1 year post-operative standing lateral radiographs (interquartile range 12–13 months) were reviewed. Pre-operative supine PT was measured from CT as the angle between the anterior pelvic plane (APP) and the horizontal plane of the CT device. Standing PT was measured on standing lateral x-rays as the angle between the APP and the vertical line. Patients with ≥13° change from supine pre-op to standing post-op (corresponding to a 10° change in cup anteversion) were grouped and compared to those with a <13° change using unpaired student's t-tests. Mean pre-operative supine PT (3.8±6.0°) was significantly different from mean post-operative standing PT (−3.5±7.1°, p<0.001), ie mean change of −7.3±4.6°. 10.4% (114/1097) of patients had posterior PT changes ≥13° supine pre-op to standing post-op. A significant number of patients, ie 1 in 10, undergo a clinically significant change in PT and functional anteversion from supine pre-op to standing post-op. Surgeons should be aware of these changes when planning component placement in THA.
Smartphone-based apps that measure step-count and patient reported outcomes (PROMs) are being increasingly used to quantify recovery in total hip arthroplasty (THA). However, optimum patient-specific activity level before and during THA early-recovery is not well characterised. This study investigated 1) correlations between step-count and PROMs and 2) how patient demographics impact step-count preoperatively and during early postoperative recovery. Smartphone step-count and PROM data from 554 THA patients was retrospectively reviewed. Mean age was 64±10yr, BMI was 29±13kg/m2, 56% were female. Mean daily step count was calculated over three time-windows: 60 days prior to surgery (preop), 5-6 weeks postop (6wk), and 11-12 weeks postop (12wk). Linear correlations between step-count and HOOS12 Function and UCLA activity scores were performed. Patients were separated into three step-count levels: low (<2500steps/day), medium (2500-5500steps/day), and high (>5500steps/day). Age >65years, BMI >30, and sex were used for demographic comparisons. Student's t-tests determined significant differences in mean step-counts between demographic groups and in mean PROMs between step-count groups. UCLA correlated with step-count at all time-windows (p<0.01). HOOS12 Function correlated with step-count preoperatively and at 6wk (p<0.01). High vs low step count individuals had improved UCLA scores preoperatively (∆1.8,p<0.001), at 6wk (∆1.1,p<0.05), and 12wk (∆1.6,p<0.01), and improved HOOS12 Function scores preoperatively (∆8.4,p<0.05) and at 6wk (∆8.8,p<0.001). Younger patients had greater step-count preoperatively (4.1±3.0k vs 3.0±2.5k, p<0.01) and at 12wk (5.1±3.3k vs 3.6±2.9k, p<0.01). Males had greater step-count preoperatively (4.1±3.0k vs. 3.0±2.7k, p<0.001), at 6wk (4.5±3.2k vs 2.6±2.5k, p<0.001), and at 12wk (5.2±3.6k vs. 3.4±2.5k, p<0.001). Low BMI patients had greater step-count at 6wk (4.3±3.3k vs. 2.6±2.7k, p<0.01) and 12wk (5.0±3.6k vs. 3.6±2.6k, p<0.05). Daily step-count is significantly impacted by patient demographics and correlates with PROMs, where patients with high step count exhibit improved PROMs. Generic recovery profiles may therefore not be appropriate for benchmarking across diverse populations.
Inverse Kinematic Alignment (iKA) and Gap Balancing (GB) aim to achieve a balanced TKA via component alignment. However, iKA aims to recreate the native joint line versus resecting the tibia perpendicular to the mechanical axis. This study aims to compare how two alignment methods impact 1) gap balance and laxity throughout flexion and 2) the coronal plane alignment of the knee (CPAK). Two surgeons performed 75 robotic assisted iKA TKA's using a cruciate retaining implant. An anatomic tibial resection restored the native joint line. A digital joint tensioner measured laxity throughout flexion prior to femoral resection. Femoral component position was adjusted using predictive planning to optimize balance. After femoral resection, final joint laxity was collected. Planned GB (pGB) was simulated for all cases posthoc using a neutral tibial resection and adjusting femoral position to optimize balance. Differences in ML balance, laxity, and CPAK were compared between planned iKA (piKA) and pGB. ML balance and laxity were also compared between piKA and final (fiKA). piKA and pGB had similar ML balance and laxity, with mean differences <0.4mm. piKA more closely replicated native MPTA (Native=86.9±2.8°, piKA=87.8±1.8°, pGB=90±0°) and native LDFA (Native=87.5±2.7°, piKA=88.9±3°, pGB=90.8±3.5°). piKA planned for a more native CPAK distribution, with the most common types being II (22.7%), I (20%), III (18.7%), IV (18.7%) and V (18.7%). Most pGB knees were type V (28.4%), VII (37.8%), and III (16.2). fiKA and piKA had similar ML balance and laxity, however fiKA was more variable in midflexion and flexion (p<0.01). Although ML balance and laxity were similar between piKA and pGB, piKA better restored native joint line and CPAK type. The bulk of pGB knees were moved into types V, VII, and III due to the neutral tibial cut. Surgeons should be cognizant of how these differing alignment strategies affect knee phenotype.
Evaluation of patient specific spinopelvic mobility requires the detection of bony landmarks in lateral functional radiographs. Current manual landmarking methods are inefficient, and subjective. This study proposes a deep learning model to automate landmark detection and derivation of spinopelvic measurements (SPM). A deep learning model was developed using an international multicenter imaging database of 26,109 landmarked preoperative, and postoperative, lateral functional radiographs (HREC: Bellberry: 2020-08-764-A-2). Three functional positions were analysed: 1) standing, 2) contralateral step-up and 3) flexed seated. Landmarks were manually captured and independently verified by qualified engineers during pre-operative planning with additional assistance of 3D computed tomography derived landmarks. Pelvic tilt (PT), sacral slope (SS), and lumbar lordotic angle (LLA) were derived from the predicted landmark coordinates. Interobserver variability was explored in a pilot study, consisting of 9 qualified engineers, annotating three functional images, while blinded to additional 3D information. The dataset was subdivided into 70:20:10 for training, validation, and testing. The model produced a mean absolute error (MAE), for PT, SS, and LLA of 1.7°±3.1°, 3.4°±3.8°, 4.9°±4.5°, respectively. PT MAE values were dependent on functional position: standing 1.2°±1.3°, step 1.7°±4.0°, and seated 2.4°±3.3°, p< 0.001. The mean model prediction time was 0.7 seconds per image. The interobserver 95% confidence interval (CI) for engineer measured PT, SS and LLA (1.9°, 1.9°, 3.1°, respectively) was comparable to the MAE values generated by the model. The model MAE reported comparable performance to the gold standard when blinded to additional 3D information. LLA prediction produced the lowest SPM accuracy potentially due to error propagation from the SS and L1 landmarks. Reduced PT accuracy in step and seated functional positions may be attributed to an increased occlusion of the pubic-symphysis landmark. Our model shows excellent performance when compared against the current gold standard manual annotation process.
The Coronal Plane Alignment of the Knee (CPAK) is a recent method for classifying knees using the hip-knee-ankle angle and joint line obliquity to assist surgeons in selection of an optimal alignment philosophy in total knee arthroplasty (TKA)1. It is unclear, however, how CPAK classification impacts pre-operative joint balance. Our objective was to characterise joint balance differences between CPAK categories. A retrospective review of TKA's using the OMNIBotics platform and BalanceBot (Corin, UK) using a tibia first workflow was performed. Lateral distal femoral angle (LDFA) and medial proximal tibial angle (MPTA) were landmarked intra-operatively and corrected for wear. Joint gaps were measured under a load of 70–90N after the tibial resection. Resection thicknesses were validated to recreate the pre-tibial resection joint balance. Knees were subdivided into 9 categories as described by MacDessi et al.1 Differences in balance at 10°, 40° and 90° were determined using a one-way 2-tailed ANOVA test with a critical p-value of 0.05. 1124 knees satisfied inclusion criteria. The highest proportion of knees (60.7%) are CPAK I with a varus aHKA and Distal Apex JLO, 79.8% report a Distal Apex JLO and 69.3% report a varus aHKA. Greater medial gaps are observed in varus (I, IV, VII) compared to neutral (II, V, VIII) and valgus knees (III, VI, IX) (p<0.05 in all cases) as well as in the Distal Apex (I, II, III) compared to Neutral groups (IV, V, VI) (p<0.05 in all cases). Comparisons could not be made with the Proximal Apex groups due to low frequency (≤2.5%). Significant differences in joint balance were observed between and within CPAK groups. Although both hip-knee-ankle angle and joint line orientation are associated with joint balance, boney anatomy alone is not sufficient to fully characterize the knee.
Preoperative ligament laxity can be characterized intraoperatively using digital robotic tensioners. Understanding how preoperative knee joint laxity affects preoperative and early post-operative patient reported outcomes (PROMs) may aid surgeons in tailoring intra-operative balance and laxity to optimize outcomes for specific patients. This study aims to determine if preoperative ligament laxity is associated with PROMs, and if laxity thresholds impact PROMs during early post-operative recovery. 106 patients were retrospectively reviewed. BMI was 31±7kg/m2. Mean age was 67±8 years. 69% were female. Medial and lateral knee joint laxity was measured intraoperatively using a digital robotic ligament tensioning device after a preliminary tibial resection. Linear regressions between laxity and KOOS12-function were performed in extension (10°), midflexion (45°), and flexion (90°) at preoperative, 6-week, and 3-month time points. Patients were separated into two laxity groups: ≥7 mm laxity and <7 mm laxity. Student's Correlations were found between preoperative KOOS12-function and medial laxity in midflexion (p<0.001) and flexion (p<0.01). Patients with <7 mm of medial laxity had greater preoperative KOOS12-function scores compared to patients with ≥7 mm of medial laxity in extension (46.8±18.2 vs. 29.5±15.6, p<0.05), midflexion (48.4±17.8 vs. 32±16.1, p<0.001), and flexion (47.7±18.3 vs. 32.6±14.7, p<0.01). No differences in KOOS12-function scores were observed between medial laxity groups at 6-weeks or 3-months. All knees had <5 mm of medial laxity postoperatively. No correlations were found between lateral laxity and KOOS12-function. Patients with preoperative medial laxity ≥7 mm had lower preoperative PROMs scores compared to patients with <7 mm of medial laxity. No differences in PROMs were observed between laxity groups at 6 weeks or 3 months. Patients with excessive preoperative joint laxity achieve similar PROMs scores to those without excessive laxity after undergoing gap balancing TKA.
Passive smartphone-based apps are becoming more common for measuring patient progress after total knee arthroplasty (TKA). Optimum activity levels during early TKA recovery haven't been well documented. This study investigated correlations between step-count and patient reported outcome measures (PROMs) and how demographics impact step-count preoperatively and during early post-operative recovery. Smartphone capture step-count data from 357 TKA patients was retrospectively reviewed. Mean age was 68±8years. 61% were female. Mean BMI was 31±6kg/m2. Mean daily step count was calculated over three time-windows: 60 days prior to surgery (preop), 5-6 weeks postop (6wk), and 11-12 weeks postop (12wk). Linear correlations between step-count and KOOS12-function and UCLA activity scores were performed. Patients were separated into three step-count levels: low (<1500steps/day), medium (1500-4000steps/day), and high (>4000steps/day). Age >65years, BMI >30kg/m2, and sex were used for demographic comparisons. Student's t-tests determined significant differences in mean step-counts between demographic groups, and in mean PROMs between step-count groups. UCLA correlated with step-count at all time-windows (p<0.01). KOOS12-Function correlated with step-count at 6wk and 12wk (p<0.05). High step-count individuals had improved PROMs compared to low step-count individuals preoperatively (UCLA: ∆1.4 [p<0.001], KOOS12-Function: ∆7.3 [p<0.05]), at 6wk (UCLA: ∆1 [p<0.01], KOOS12-Function: ∆7 [p<0.05]), and at 12wk (UCLA: ∆0.8 [p<0.05], KOOS12-Function: ∆6.5 [p<0.05]). Younger patients had greater step-count preoperatively (3.8±3.0k vs. 2.5±2.3k, p<0.01), at 6wk (3.1±2.9k vs. 2.2±2.3k, p<0.05) and at 12wk (3.9±2.6k vs. 2.8±2.6k, p<0.01). Males had greater step-count preoperatively (3.7±2.6k vs. 2.5±2.6k, p<0.001), at 6wk (3.6±2.6k vs. 1.9±2.4k, p<0.001), and at 12wk (3.9±2.3 vs. 2.8±2.8k, p<0.01). No differences in step-count were observed between low and high BMI patients at any timepoint. High step count led to improved PROMs scores compared to low step-count. Early post-operative step-count was significantly impacted by age and sex. Generic recovery profiles may not be appropriate across a diverse population.
Neither a surgeon’s intraoperative impression nor the parameters of computer navigation have been shown to be predictive of the outcomes following total knee arthroplasty (TKA). The aim of this study was to determine whether a surgeon, with robotic assistance, can predict the outcome as assessed using the Knee Injury and Osteoarthritis Outcome Score (KOOS) for pain (KPS), one year postoperatively, and establish what factors correlate with poor KOOS scores in a well-aligned and balanced TKA. A total of 134 consecutive patients who underwent TKA using a dynamic ligament tensioning robotic system with a tibia first resection technique and a cruciate sacrificing ultracongruent TKA system were enrolled into a prospective study. Each TKA was graded based on the final mediolateral ligament balance at 10° and 90° of flexion: 1) < 1 mm difference in the thickness of the tibial insert and that which was planned (n = 75); 2) < 1 mm difference (n = 26); 3) between 1 mm to 2 mm difference (n = 26); and 4) > 2 mm difference (n = 7). The mean one-year KPS score for each grade of TKA was compared and the likelihood of achieving an KPS score of > 90 was calculated. Finally, the factors associated with lower KPS despite achieving a high-grade TKA (grade A and B) were analyzed.Aims
Methods
Achieving a balanced joint with neutral alignment is not always possible in total knee arthroplasty (TKA). Intra-operative compromises such as accepting some joint imbalance, non-neutral alignment or soft-tissue release may result in worse patient outcomes, however, it is unclear which compromise will most impact outcome. In this study we investigate the impact of post-operative soft tissue balance and component alignment on postoperative pain. 135 patients were prospectively enrolled in robot assisted TKA with a digital joint tensioning tool (OMNIBotics with BalanceBot, Corin USA) (57% female; 67.0 ± 8.1 y/o; BMI: 31.9 ± 4.8 kg/m2). All surgeries were performed with a PCL sacrificing tibia or femur first techniques technique, using CR femoral components and a deep dish tibial insert (APEX, Corin USA). Gap measurements were acquired under load (average 80 N) throughout the range of motion during trialing with the tensioning tool inserted in place of the tibial trial. Component alignment parameters and post-operative joint gaps throughout flexion were recorded. Patients completed 1-year KOOS pain questionnaires. Spearman correlations and Mann-Whitney-U tests were used to investigate continuous and categorical data respectively. All analysis performed in R 3.5.3.Introduction
Methods
Neither a surgeon's intraoperative impression or computer navigation parameters have been shown to be predictive of postoperative outcomes following TKA. The purpose of this study is to determine 1) whether a surgeon and a robot can predict the 1-year KOOS pain score (KPS) and 2) determine what factors correlate with poor KOOS scores in well aligned and balanced TKA. The data of 131 consecutive patients enrolled in a prospective trial was reviewed. All TKAs were performed using a dynamic ligament tensioning robotic system with a tibial first resection technique and a cruciate sacrificing ultracongruent knee implant. Each TKA was graded based on the final recorded mediolateral ligament balance at 10° and 90°: A) <1mm with an implanted insert thickness equal to planned (n=74); B) <1mm (n=25); C) <2mm (n=26); D) >2mm (n=6) (Table-1). The 1-year KPS for each knee grade were compared and the likelihood of achieving an KPS > 90 was calculated. Finally, the factors associated with lower KPS despite achieving a high grade TKA (A/B) was performed. The Mann-Whitney U test and Chi-squared analysis was performed.Introduction
Methods
Soft-tissue balancing methods in TKA have evolved from surgeon feel to digital load-sensing tools. Such techniques allow surgeons to assess the soft-tissue envelope after bone cuts, however, these approaches are ‘after-the-fact’ and require soft-tissue release or bony re-cuts to achieve final balance. Recently, a robotic ligament tensioning device has been deployed which characterizes the soft tissue envelope through a continuous range-of-motion after just the initial tibial cut, allowing for virtual femoral resection planning to achieve a targeted gap profile throughout the range of flexion (figure-1). This study reports the first early clinical results and patient reported outcomes (PROMs) associated with this new technique and compares the outcomes with registry data. Since November 2017, 314 patients were prospectively enrolled and underwent robotic-assisted TKA using this surgical technique (mean age: 66.2 ±8.1; females: 173; BMI: 31.4±5.3). KOOS/WOMAC, UCLA, and HSS-Patient Satisfaction scores were collected pre- and post-operatively. Three, six, and twelve-month assessments were completed by 202, 141, and 63 patients, respectively, and compared to registry data from the Shared Ortech Aggregated Repository (SOAR). SOAR is a TJA PROM repository run by Ortech, an independent clinical data collection entity, and it includes data from thousands of TKAs from a diverse cross-section of participating hospitals, teaching institutions and clinics across the United States and Canada who collect outcomes data. PROMs were compared using a two-tailed t-test for non-equal variance.Introduction
Methods
Soft tissue releases are often required to correct deformity and achieve gap balance in total knee arthroplasty (TKA). However, the process of releasing soft tissues can be subjective and highly variable and is often perceived as an ‘art’ in TKA surgery. Releasing soft tissues also increases the risk of iatrogenic injury and may be detrimental to the mechanically sensitive afferent nerve fibers which participate in the regulation of knee joint stability. Measured resection TKA approaches typically rely on making bone cuts based off of generic alignment strategies and then releasing soft tissue afterwards to balance gaps. Conversely, gap-balancing techniques allow for pre-emptive adjustment of bone resections to achieve knee balance thereby potentially reducing the amount of ligament releases required. No study to our knowledge has compared the rates of soft tissue release in these two techniques, however. The objective of this study was, therefore, to compare the rates of soft tissue releases required to achieve a balanced knee in tibial-first gap-balancing versus femur-first measured-resection techniques in robotic assisted TKA, and to compare with release rates reported in the literature for conventional, measured resection TKA [1]. The number and type of soft tissue releases were documented and reviewed in 615 robotic-assisted gap-balancing and 76 robotic-assisted measured-resection TKAs as part of a multicenter study. In the robotic-assisted gap balancing group, a robotic tensioner was inserted into the knee after the tibial resection and the soft tissue envelope was characterized throughout flexion under computer-controlled tension (fig-1). Femoral bone resections were then planned using predictive ligament balance gap profiles throughout the range of motion (fig-2), and executed with a miniature robotic cutting-guide. Soft tissue releases were stratified as a function of the coronal deformity relative to the mechanical axis (varus knees: >1° varus; valgus knees: >1°). Rates of releases were compared between the two groups and to the literature data using the Fischer's exact test.Introduction
Methods
Achieving good ligament balance in total knee arthroplasty (TKA) is essential to prevent early failure and revision surgery. Poor balance and instability are well-defined, however, an ideal ligament balance target across all patients is not well-understood. In this study we investigate the achieved ligament balance using an imageless, intra-operative dynamic balancing tool and its relation to patient reported outcomes. A prospective, multi-surgeon, multi-center study investigated the use of a dynamic ligament-balancing tool in combination with a robotic-assisted navigation platform using the APEX knee (OMNI-Corin, Raynham MA). After all resections, the femoral trial and a computer-controlled tensioning device in place of the tibial tray was inserted into the knee joint. The difference in medial and lateral (ML) gaps when balancing the knee under constant load at extension (10°), mid-flexion (30°) and flexion (90°) was captured. Patients completed the KOOS questionnaire at 3 months ± 2 weeks post-surgery and considered the past 7 days as a timeframe for responses. Pearson's correlation was used to determine linear correlations between factors and ANOVA tests were used to determine differences in categorical data.Background
Methods
Achieving a well-balanced midflexion and flexion soft tissue envelope is a major goal in Total Knee Arthroplasty (TKA). The definition of soft tissue balance that results in optimal outcomes, however, is not well understood. Studies have investigated the native soft tissue envelope in cadaveric specimen and have shown loosening of the knee in flexion, particularly on the lateral side. These methods however do not reflect the post TKA environment, are invasive, and not appropriate for intra-operative use. This study utilizes a digital gap measuring tool to investigate the impact of soft tissue balance in midflexion and flexion on post-operative pain. A prospective multicenter multi-surgeon study was performed in which patients underwent TKA with a dynamic ligament-balancing tool in combination with a robotic-assisted navigation platform. All surgeries were performed with APEX implants (Corin Ltd., USA) using a variety of tibia and femur first techniques. Gap measurements were acquired under load (average 80 N) throughout the range of motion during trialing with the balancing tool inserted in place of the tibial trial. Patients completed KOOS pain questionnaires at 3months±2weeks post-op. Linear correlations were investigated between KOOS pain and coronal gap measurements in midflexion (30°–60°) and flexion (>70°). T-tests were used to compare outcomes between categorical data.Introduction
Methods
Current CMS reimbursement policy for total joint replacement is aligned with more cost effective, higher quality care. Upon implementation of a standardized evidenced-based care pathway, we evaluated overall procedural costs and clinical outcomes over the 90-day episode of care period for patients undergoing TKA with either conventional (Conv.) or robotic-assisted (RAS) instrumentation. In a retrospective review of the first seven consecutive quarters of Bundled Payment for Care Improvement (BPCI) Model 2 participation beginning January 2014, we compared 90-day readmission rates, Length of Stay (LOS), discharge disposition, gains per episode in relation to target prices and overall episode costs for surgeons who performed either RAS-TKA (3 surgeons, 147 patients) or Conv. TKA (3 surgeons, 85 patients) at a single institution. All Medicare patients from all surgeons performing more than two TKA's within the study period were included. An evidence-based clinical care pathway was implemented prior to the start of the study that standardized pre-operative patient education, anesthesia, pain management, blood management, and physical/occupational therapy throughout the LOS for all patients. Physician specific target prices were established from institutional historical payment data over a prior three year period.Introduction
Methods
Robotic systems have been used in TKA to add precision, although few studies have evaluated clinical outcomes. We report on early clinical results evaluating patient reported outcomes (PROs) on a series of robotic-assisted TKA (RAS-TKA) patients, and compare scores to those reported in the literature. We prospectively consented and enrolled 106 patients undergoing RAS-TKA by a single surgeon performing a measured-resection femur-first technique using a miniature bone-mounted robotic system. Patients completed a KOOS, New Knee Society Score (2011 KSS) and a Veterans RAND-12 (VR-12) pre-operatively and at 3, 6 and 12 months (M) post- operatively. At the time of publication 104, 101, and 78 patients had completed 3M, 6M, and 12M PROs, respectively. Changes in the five KOOS subscales (Pain, Symptoms, Activities of Daily Living (ADL), Sport and recreation function (Sport/Rec) and Knee-related Quality of Life (QOL)) were compared to available literature data from FORCE – TJR, a large, prospective, national cohort of TJR patients enrolled from diverse high-volume centers and community orthopaedic practices in the U.S, as well as to individual studies reporting on conventional (CON-TKA) and computer-assisted (CAS- TKA) at 3M, and on conventional TKA at 6M. The 2011 KSS is a validated method for quantifying patient's expectations and satisfaction with their TKA procedure. Improvements in the 2011 KSS were compared with literature data at 6M post-operatively.Introduction
Methods
Gap balancing technique aims to achieve equal and symmetric gap at full extension and in flexion; however, little is known about the connection between the native and the replaced knee gaps. In this study, a novel robotic assisted ligament tensioning tool was used to measure the pre- and post- operative gaps to better understand their relationship when aiming for balance gaps in flexion and extension. The accuracy of a prediction algorithm for the post-operative gaps based on the native gap and implant alignment was evaluated in this study. The medial and lateral gap were smallest at full extension. The native gaps increase with flexion until 30 degrees where they plateaued for the remaining flexion range. The native lateral gap was larger than the medial gap throughout the flexion range. Planning for equal gaps at extension and flexion resulted with tightest gaps at these angle; however, the gaps in mid-flexion were 3–4 mm larger. Good agreement was observed between the post-operative results and the predicted gas from the software algorithm. The results showed that the native gaps are neither symmetric nor equal. In addition, aiming for equal gaps reduces the variation at these angles but could result in mid- flexion laxity. Advanced robotics-assisted instrumentation can aid in evaluation of soft-tissue and help in surgical planning of TKA. This allows the surgeon to achieve the targeted outcome as well as record the final implant tension to correlate with clinical outcomes.
We introduce a novel active tensioning system that can be used for dynamic gap-based implant planning as well as for assessment of final soft tissue balance during implant trialing. We report on the concept development and preliminary findings observed during early feasibility testing in cadavers with two prototype systems. The active spacer (fig 1) consists of a motorized actuator unit with integrated force sensors, independently actuated medial and lateral upper arms, and a set of modular attachments for replicating the range of tibial baseplate and insert trial sizes. The spacer can be controlled in either force or position (gap) control and is integrated into the OMNIBoticsTM Robotic-assisted TKA platform (OMNI, MA, USA). Two design iterations were evaluated on eleven cadaver specimens by seven orthopaedic surgeons in three separate cadaver labs. The active spacer was used in a tibial-first technique to apply loads and measure gaps prior to and after femoral resections. To determine the range of forces applied on the spacer during a varus/valgus assessment procedure, each surgeon performed a varus/valgus stress test and peak medial and lateral forces were measured. Surgeons also rated the feel of the stability of the knee at 50N and 80N of preload using the following scale: 1 – too loose; 2 – slightly loose; 3 – ideal; 4 slightly tight; 5 – too tight. Final balanced was assessed with the spacer and with manual trial components.System description
Cadaver Study
Knee instability, stiffness, and soft-tissue imbalance are causes of aseptic revision and patient dissatisfaction following total knee arthroplasty (TKA). Surgical techniques that ensure optimal ligament balance throughout the range of motion may help reduce TKA revision for instability and improve outcomes. We evaluated a novel tibial-cut first gap balancing technique where a computer-controlled tensioner is used to dynamically apply a varying degree of distraction force in real-time as the knee is taken through a range of motion. Femoral bone cuts can then be planned while visualizing the predicted knee implant laxity throughout the arc of flexion. After registering the mechanical axes and morphology of the tibia and femur using computer navigation, the tibial resection was performed and a robotic tensioning tool was inserted into the knee prior to cutting the femur. The tool was programmed to apply equal loads in the medial and lateral compartments of the knee, but to dynamically vary the distraction force in each compartment as the knee is flexed with a higher force being applied in extension and a progressively lower force applied though mid-flexion up to 90° of flexion. The tension and predictive femoral gaps between the tibial cut and the femoral component in real-time was determined based on the planned 3D position and size of the femoral implant and the acquired pre-resection gaps (figure 1). Femoral resections were then performed using a robotic cutting guide and the trial components were inserted.Introduction
Surgical Technique Description
