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Introduction
The degree of cartilage degeneration assessed intraoperatively may not be sufficient as a criterion for patellar resurfacing in total knee arthroplasty (TKA). However, single-photon emission tomography/computed tomography (SPECT/CT) is useful for detecting osteoarthritic involvement deeper in the subchondral bone. The purpose of the study was to determine whether SPECT/CT reflected the cartilage lesion underneath the patella in patients with end-stage osteoarthritis (OA) and whether clinical outcomes after TKA without patellar resurfacing differed according to the severity of patellofemoral (PF) OA determined by visual assessment and SPECT/CT findings.
Methods
This study included 206 knees which underwent TKA. The degree of cartilage degeneration was graded intraoperatively according to the International Cartilage Repair Society grading system. Subjects were classified into four groups according to the degree of bone tracer uptake (BTU) on SPECT/CT in the PF joint. The Feller's patella score and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were assessed preoperatively and postoperative 1 and 2 years.
Introduction
The functional ante-inclination (AI) of the cup after total hip arthroplasty (THA) is a key component in the combined sagittal index (CSI) to predict joint stability after THA. To accurately predict AI, we deducted a mathematic algorithm between the radiographic anteversion (RA), radiographic inclincation (RI), pelvic tilting (PT), and AI. The current study aims (1) to validate the mathematic algorithm; (2) to convert the AI limits in the CSI index (standing AI ≤ 45°, sitting AI ≥ 41°) into coronal functional safe zone (CFSZ) and explore the influences of the stand-to-sit pelvic motion (PM) and pelvic incidence (PI) on CFSZ; (3) to locate a universal cup orientation that always fulfill the AI criteria of CSI safe zone for all patients or subgroups of PM(PM ≤ 10°, 10° < PM ≤ 30°, and PM > 30°) and PI (PI≤ 41°, 41°< PI ≤ 62°, and PI >62°), respectively.
Methods
A 3D printed phantom pelvic model was designed to simulate changing PT values. An acetabular cup was implanted with different RA, RI, and PT settings using robot assisted technique. We enrolled 100 consecutive patients who underwent robot assisted THA from April, 2019 to June, 2019 in our hospital. EOS images before THA and at 6-month follow-up were collected. AI angles were measured on the lateral view radiographs as the reference method. Mean absolute error (MAE), Bland-Altman analysis and linear regression were conducted to assess the accuracy of the AI algorithm for both the phantom and patient radiographic studies. The 100 patients were classified into three subgroups by PM and PI, respectively. Linear regression and ANOVA analysis were conducted to explore the relationship between the size of CFSZ, and PM and PI, respectively. Intersection of the CFSZ was conducted to identify if any universal cup orientation (RA, RI) existed for the CSI index.
Introduction
Although total knee arthroplasty (TKA) is generally considered successful, 16–30% of patients are dissatisfied. There are multiple reasons for this, but some of the most frequent reasons for revision are instability and joint stiffness. A possible explanation for this is that the implant alignment is not optimized to ensure joint stability in the individual patient. In this work, we used an artificial neural network (ANN) to learn the relation between a given standard cruciate-retaining (CR) implant position and model-predicted post-operative knee kinematics. The final aim was to find a patient-specific implant alignment that will result in the estimated post-operative knee kinematics closest to the native knee.
Methods
We developed subject-specific musculoskeletal models (MSM) based on magnetic resonance images (MRI) of four
INTRODUCTION
The restoration of physiological kinematics is one of the goals of a total knee arthroplasty (TKA). Navigation systems have been developed to allow an accurate and precise placement of the implants. But its application to the intraoperative measurement of knee kinematics has not been validated. The hypothesis of this study was that the measurement of the knee axis, femoral rotation, femoral translation with respect to the tibia, and medial and lateral femorotibial gaps during continuous passive knee flexion by the navigation system would be different from that by fluoroscopy taken as reference.
MATERIAL – METHODS
Five pairs of knees of preserved specimens were used. The e.Motion FP ® TKA (B-Braun Aesculap, Tuttlingen, Germany) was implanted using the OrthoPilot TKA 4.3 version and Kobe version navigation system (B-Braun Aesculap, Tuttlingen, Germany). Kinematic recording by the navigation system was performed simultaneously with fluoroscopic recording during a continuous passive flexion-extension movement of the prosthetic knee. Kinematic parameters were extracted from the fluoroscopic recordings by image processing using JointTrack Auto ® software (University of Florida, Gainesville, USA). The main criteria were the axis of the knee measured by the angle between the center of the femoral head, the center of the knee and the center of the ankle (HKA), femoral rotation, femoral translation with respect to the tibia, and medial and lateral femorotibial gaps. The data analysis was performed by a Kappa correlation test. The agreement of the measurements was assessed using the intraclass correlation coefficient (ICC) and its 95% confidence interval.
Introduction
Re-revision due to instability and dislocation can occur in up to 1 in 4 cases following revision total hip arthroplasty (THA). Optimal placement of components during revision surgery is thus critical in avoiding re-revision. Computer-assisted navigation has been shown to improve the accuracy and precision of component placement in primary THA; however, its role in revision surgery is less well documented. The purpose of our study was to evaluate the effect of computer-assisted navigation on component placement in revision total hip arthroplasty, as compared with conventional surgery.
Methods
To examine the effect of navigation on acetabular component placement in revision THA, we retrospectively reviewed data from a multi-centre cohort of 128 patients having undergone revision THA between March 2017 and January 2019. An imageless computer navigation device (Intellijoint HIP®, Intellijoint Surgical, Kitchener, ON, Canada) was utilized in 69 surgeries and conventional methods were used in 59 surgeries. Acetabular component placement (anteversion, inclination) and the proportion of acetabular components placed in a functional safe zone (40° inclination/20° anteversion) were compared between navigation assisted and conventional THA groups.
Robotic and navigated TKA procedures have been introduced to improve component placement precision for the purpose of improving implant survivorship and other clinical outcomes. Although numerous studies have shown enhanced precision in placing components, adoption of technology-assistance (TA) for TKA has been relatively slow. One reason for this has been the difficulty in demonstrating the cost-effectiveness of implementing TA-TKA systems and assessing their impact on revision rates.
In this study, we aimed to use a simulation approach to answer the following questions: (1) Can we determine the distribution of likely reductions in TKA revision rates attributable to TA-TKA in an average US patient population? And, (2) What reduction in TKA revision rates are required to achieve economic neutrality?
In a previous study, we developed a method for creating large sets of simulated TKA patient populations with distributions of patient-specific factors (age at index surgery, sex, BMI) and one surgeon-controlled factor (coronal alignment) drawn from registry data and published literature. Effect sizes of each factor on implant survival was modeled using large clinical studies. For 10,000 simulated TKA patients, we simulated 20,000 TKA surgeries, evenly split between groups representing coronal alignment precisions reported for manual (±3°) and TA-TKA (±1.0°), calculating the patient-specific survival curve for each group. Extending our previous study, we incorporated the probability of each patient's expected survival into our model using publicly available actuarial data. This allowed us to calculate a patient-specific estimate of the Reduction in Lifetime Risk of Revision (RLRR) for each simulated patient. Our analysis showed that 90% of patients will achieve an RLRRof 1.5% or less in an average US TKA population.
We then conducted a simplified economic analysis with the goal of determining the net cost of using TA-TKA per case when factoring in future savings by TKA revision rates. We assumed an average cost of revision surgery to be $75,000 as reported by Delanois (2017) and an average added cost incurred by TA-TKA to be $6,000 per case as reported by Antonis (2019). We estimate the net cost per TA-TKA case (
Based on these results, it appears that it would not be cost-effective to use TA-TKA on more than a small fraction of the typical US TKA patient population if the goal is to reduce overall costs through reducing revision risks. However, we note that this simulation does not consider other possible reported benefits of TA-TKA surgery, such as improved functional and pain outcome scores which may justify its use on other grounds. Alternative costs incurred by TA-TKA will be evaluated in a future study. To reach economic neutrality, TA-TKA systems either must reduce the added cost per intervention or increase RLRR by better addressing the root causes of revision.
Introduction
Robot systems have been successfully introduced to improve the accuracy and reduce severe iatrogenic soft tissue damage in knee arthroplasty. Unfortunately to perform complete a complete bone cut, the cutting tool has to slightly pass the edge of the bone. In the posterior zones were retractor protection is impossible this will lead to contact between the cutting tool and the soft tissue envelope. Therefore, complete soft tissue preservation cannot be guaranteed with the current commercial systems.
Methods
This study presents an alternative robotic controlled cutting technique to perform the bone resections during TKA by milling a slot with a long slender high-speed milling tool. The system is composed by a long milling tool driven by a high-speed motor and a protector covering the end of the cutter. The protector is rigidly connected to the motor by the support structure next to the mill, which moves behind the mill in the slot created by the cutter. The protector at the end of the cutter has four functions: providing mechanical support for the mill, preventing soft tissue to come into contact with the cutter, sensing the edge of the bone to accurately follow the shape of the bone and releasing the attached soft tissue. The edge of the bone is sensed by force feedback and with the help of a probing motion the adaptive algorithm enables the protector to follow the edge of the bone closely by compensating for small segmentation and registration errors. A pilot test to evaluate the concept was performed on three fresh frozen knees. The flatness of the resection, the iatrogenic soft tissue damage, the cutting time and the efficiency of the bone contour following algorithm was measured.
Introduction
Reverse shoulder Arthroplasty is a successful treatment for gleno-humeral osteoarthritis. However, components loosening and painful prostheses, related to components wrong positioning, are still a problem for those patients who underwent this kind of surgery. Several new technology has been developed the improve the implant positioning. CT-based intraoperative navigation system is a suitable technology that allow the surgeon to prepare the implant site exactly as planned with preoperative software.
Method
Thirty reverse shoulder prostheses were performed at Modena Polyclinic using GPS CT-based intraoperative navigation system (Exactech, Gainsville, Florida). Walch classification was used to assess glenoid type. Planned version and inclination of the glenoid component, planned seating, final version and inclination of the reamer were recorded. Intraoperative and perioperative complication were recorded. Planned positioning was conducted aiming to the maximum seating, avoiding retroversion >10° and superior inclination.
INTRODUCTION
Determining proper joint tension in reverse total shoulder arthroplasty (rTSA) can be a challenging task for shoulder surgeons. Often, this is a subjective metric learned by feel during fellowship training with no real quantitative measures of what proper tension encompasses. Tension too high can potentially lead to scapular stress fractures and limitation of range of motion (ROM), whereas tension too low may lead to instability. New technologies that detect joint load intraoperatively create the opportunity to observe rTSA joint reaction forces in a clinical setting for the first time. The purpose of this study was to observe the differences in rTSA loads in cases that utilized two different humeral liner sizes.
METHODS
Ten different surgeons performed a total of 37 rTSA cases with the same implant system. During the procedure, each surgeon reconstructed the rTSA implants to his or her own preferred tension. A wireless load sensing humeral liner trial (VERASENSE for Equinoxe, OrthoSensor, Dania Beach, FL) was used in lieu of a traditional plastic humeral liner trial to provide real-time load data to the operating surgeon during the procedure. Two humeral liner trial sizes were offered in 38mm and 42mm curvatures and were selected each case based on surgeon preference. To ensure consistent measurements between surgeons, a standardized ROM assessment consisting of four dynamic maneuvers (maximum internal to external rotation at 0°, 45°, and 90° of abduction, and a maximum flexion/extension maneuver) and three static maneuvers (arm overhead, across the body, and behind the back) was completed in each case. Deidentified load data in lbf was collected and sorted based on which size liner was selected. Differences in means for minimum and maximum load values for the four dynamic maneuvers and differences in means for the three static maneuvers were calculated using 2-tailed unpaired t-tests.
Introduction
Post-meniscectomy syndrome is broadly characterised by intractable pain following the partial or total removal of a meniscus. There is a large treatment gap between the first knee pain after meniscectomy and the eligibility for a TKA. Hence, there is a strong unmet need for a solution that will relieve this post-meniscectomy pain. Goal of this first-in-man study was to evaluate the safety and performance of an anatomically shaped artificial medial meniscus prosthesis and the accompanying surgical technique.
Methods
A first-in-man, prospective, multi-centre, single arm clinical investigation was intended to be performed on 18 post-medial meniscectomy syndrome patients with limited underlying cartilage damage (Kellgren Lawrence scale 0–3) in the medial compartment and having a normal lateral compartment. Eventually 5 patients received a polycarbonate urethane mediale meniscus prosthesis (Trammpolin® medial meniscus prosthesis; ATRO Medical B.V., the Netherlands) which was clicked onto two titanium screws fixated at the native horn attachments on the tibia. PROMs were collected at baseline and at 6 weeks, 3, 6, 12 and 24 months following the intervention including X-rays at 6, 12 and 24 Months. MRI scans were repeated after 12 and 24 months.
OSSTEC is a pre-spin-out venture at Imperial College London seeking industry feedback on our orthopaedic implants which maintain bone quality in the long term. Existing orthopaedic implants provide successful treatment for knee osteoarthritis, however, they cause loss of bone quality over time, leading to more dangerous and expensive revision surgeries and high implant failure rates in young patients.
OSSTEC tibial implants stimulate healthy bone growth allowing simple primary revision surgery which will provide value for all stakeholders. This could allow existing orthopaedics manufacturers to capture high growth in existing and emerging markets while offering hospitals and surgeons a safer revision treatment for patients and a 35% annual saving on lifetime costs. For patients, our implant technology could mean additional years of quality life by revising patients to a primary TKA before full revision surgery.
Our implants use patent-filed additive manufacturing technology to restore a healthy mechanical environment in the proximal tibia; stimulating long term bone growth. Proven benefits of this technology include increased bone formation and osseointegration, shown in an animal model, and restoration of native load transfer, shown in a human cadaveric model.
This technology could help capture the large annual growth (24%) currently seen in the cementless knee reconstruction market, worth $1.2B. Furthermore, analysis suggests an additional market of currently untreated younger patients exists, worth £0.8B and growing by 18% annually. Making revision surgery and therefore treatment of younger patients easier would enable access to this market. We aim to offer improved patient treatment via B2B sales of implants to existing orthopaedic manufacturer partners, who would then provide them with instrumentation to hospitals and surgeons.
Existing implant materials provide good options for patient treatments, however OSSTEC's porous titanium structures offer unique competitive advantages; combining options for modular design, cementless fixation, initial bone fixation and crucially long term bone maintenance.
Speaking to surgeons across global markets shows that many surgeons are keen to pursue bone preserving surgeries and the use of porous implants. Furthermore, there is a growing demand to treat young patients (with 25% growth in patients younger than 65 over the past 10 years) and to use cementless knee treatments, where patient volume has doubled in the past 4 years and is following trends in hip treatments.
Our team includes engineers and consultant surgeons who have experience developing multiple orthopaedic implants which have treated over 200,000 patients. To date we have raised £175,000 for the research and development of these implants and we hope to gain insight from industry professionals before further development towards our aim to begin trials for regulatory approval in 2026.
OSSTEC implants provide a way to stimulate bone growth after surgery to reduce revision risk. We hope this could allow orthopaedic manufactures to explore high growth markets while meaning surgeons can treat younger patients in a cost effective way and add quality years to patients' lives.
INTRODUCTION
The benefits of combining enhanced recovery after surgery (ERAS) interventions with an outpatient THA/TKA program are uncertain. The primary objective was to compare adverse event rate and secondly to compare pain management, functional recovery, PROMs and patients' satisfaction.
METHODS
We conducted an ambidirectional single subject cohort study on 48 consecutive patients who experienced both a standard-inpatient and an ERAS-outpatient THA/TKA (contralaterally). We compared complications according to Clavien-Dindo scale and Comprehensive Complications Index (CCI), and unplanned episodes of care. Postoperative pain assessed with a numeric rating scale, opioid consumption in morphine milligram equivalents, functional recovery, patient-reported outcome measures (WOMAC, KOOS, HOOS, Forgotten Joint Score and Patient Joint Perception) and patients' satisfaction were also evaluated.
Introduction
Recent advances in algorithms developed with passively collected sensor data from smart phones and watches demonstrate new, objective, metrics with the capacity to show qualitative gait characteristics. The purpose of this feasibility study was to assess the recovery of gait quality following primary total hip and knee arthroplasty collected using a smartphone-based care platform.
Methods
A secondary data analysis of an IRB approved multicenter prospective trial evaluating the use of a smartphone-based care platform for primary total knee arthroplasty (TKA, n=88), unicondylar knee arthroplasty (UKA, n=28), and total hip arthroplasty (THA, n=82). Subjects were followed from 6 weeks preoperative to 24 weeks postoperative. The group was comprised of 117 females and 81 males with a mean age of 61.4 and BMI of 30.7. Signals were collected from the participants' smartphones. These signals were used to estimate gait quality according to walking speed, step length, and timing asymmetry. Post-operative measures were compared to preoperative baseline levels using a Signed-Rank test (p<0.05).
Introduction
The purpose of this study was to demonstrate the feasibility of passively collecting objective data from a commercially available smartphone-based care management platform (sbCMP) and robotic assisted total knee arthroplasty (raTKA).
Methods
Secondary data analysis was performed using de-identified data from a commercial database that collected metrics from a sbCMP combined with intraoperative data collection from raTKA. Patients were included in this analysis if they underwent unilateral raTKA between July 2020 and February 2021, and were prescribed the sbCMP (n=131). The population consisted of 76 females and 55 males, with a mean age of 64 years (range, 43 – 81). Pre-operative through six-week post-operative data included step counts from the sbCMP, as well as administration of the KOOS JR. Intraoperative data included surgical times, the hip-knee-ankle angle (HKA), and medial and lateral laxity assessments from the robotic assessment. Data are presented using descriptive statistics. Comparisons were performed using a paired samples t-test, or Wilcoxon Signed-rank test, with significance assessed at p<0.05. A minimal detectable change (MDC) in the KOOS JR score was considered ½ standard deviation of the preoperative values.
Introduction
The purpose of this study was to characterize the recovery of physical activity following knee arthroplasty by means of step counts and flight counts (flights of stairs) measured using a smartphone-based care platform.
Methods
This is a secondary data analysis on the treatment cohort of a multicenter prospective trial evaluating the use of a smartphone-based care platform for primary total and unicondylar joint arthroplasty. Participants in the treatment arm that underwent primary total or unicondylar knee arthroplasty and had at least 3 months of follow-up were included (n=367). Participants were provided the app with an associated smart watch for measuring several different health measures including daily step and flight counts. These measures were monitored preoperatively, and the following postoperative intervals were selected for review: 2–4 days, 1 month, 1.5 month, 3 months and 6 months. The data are presented as mean, standard deviation, median, and interquartile range (IQR). Signed rank tests were used to assess the difference in average of daily step counts over time. As not all patients reported having multiple stairs at home, a separate analysis was also performed on average flights of stairs (n=214). A sub-study was performed to evaluate patients who returned to preoperative levels at 1.5 months (step count) and 3 months (flight count) using an independent samples T test or Fisher's Exact test was to compare demographics between patients that returned to preoperative levels and those that did not.
Introduction
Clinical decision support tools are software that match the input characteristics of an individual patient to an established knowledge base to create patient-specific assessments that support and better inform individualized healthcare decisions. Clinical decision support tools can facilitate better evidence-based care and offer the potential for improved treatment quality and selection, shared decision making, while also standardizing patient expectations.
Methods
Predict+ is a novel, clinical decision support tool that leverages clinical data from the Exactech Equinoxe shoulder clinical outcomes database, which is composed of >11,000 shoulder arthroplasty patients using one specific implant type from more than 30 different clinical sites using standardized forms. Predict+ utilizes multiple coordinated and locked supervised machine learning algorithms to make patient-specific predictions of 7 outcome measures at multiple postoperative timepoints (from 3 months to 7 years after surgery) using as few as 19 preoperative inputs. Predict+ algorithms predictive accuracy for the 7 clinical outcome measures for each of aTSA and rTSA were quantified using the mean absolute error and the area under the receiver operating curve (AUROC).
Problem
Total hip replacement (THA) is among the most common and highest total spend elective operations in the United States. However, up to 7% of patients have 90-day complications after surgery, most frequently joint dislocation that is related to poor acetabular component positioning. These complications lead to patient morbidity and mortality, as well as significant cost to the health system. As such, surgeons and hospitals value navigation technology, but existing solutions including robotics and optical navigation are costly, time-consuming, and complex to learn, resulting in limited uptake globally.
Solution
Augmented reality represents a navigation solution that is rapid, accurate, intuitive, easy to learn, and does not require large and costly equipment in the operating room. In addition to providing cutting edge technology to specialty orthopedic centers, augmented reality is a very attractive solution for lower volume and smaller operative settings such as ambulatory surgery centers that cannot justify purchases of large capital equipment navigation systems.
PROBLEM
Since the COVID-19 pandemic of 2020, there has been a marked rise in the use of telemedicine to evaluate patients following total knee arthroplasty (TKA). Telemedicine is helpful to maintain patient contact, but it cannot provide objective functional TKA data. External monitoring devices can be used, but in the past have had mixed results due to patient compliance and data continuity, particularly for monitoring over numerous years. This novel stem is a translational product with an embedded sensor that can remotely monitor patient activity following TKA
SOLUTION
The Canturio™ TE∗ System (Canary Medical) functions structurally as a tibial extension for the Persona® cemented tibial plate (Zimmer Biomet). The stem is instrumented with internal motion sensors (3-D accelerometer and gyroscope) and telemetry that collects and transmits kinematic data. Raw data is converted by analytics into clinically relevant gait metrics using a proprietary algorithm. The Canturio™ TE∗ will monitor the patient's gait daily for the first year and then with lower frequency thereafter to conserve battery power enabling the potential for 20 years of longitudinal data collection and analysis. A base station in the OR activates the device and links the stem and data to the patient. A base station in the patient's home collects and uploads data to the Cloud Based Canary Data Management Platform (Canary Medical). The Canary Cloud is structured as an FDA regulated and HIPPA-compliant database with cybersecurity protocols integrated into the architecture. A third base station is an accessory used in the health care professional's office to perform an on-demand gait analysis of a patient. A dashboard allows the health care professional and patient to monitor objective data of the patient's activity and progress post treatment.
Problem
The identification of unknown orthopaedic implants is a crucial step in the pre-operative planning for revision joint arthroplasty. Compatibility of implant components and instrumentation for implant removal is specific based on the manufacturer and model of the implant. The inability to identify an implant correctly can lead to increased case complexity, procedure time, procedure cost and bone loss for the patient. The number of revision joint arthroplasty cases worldwide and the number implants available on the market are growing rapidly, leading to greater difficulty in identifying unknown implants.
Solution
The solution is a machine-learning based mobile platform which allows for instant identification of the manufacturer and model of any implant based only on the x-ray image. As more surgeons and implant representatives use the platform, the model should continue to improve in accuracy and number of implants recognized until the algorithm reaches its theoretical maximum of 99% accuracy.
