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 t-tests determined significant differences between laxity groups for KOOS12-function scores at all time points.

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.

Introduction

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.

Introduction

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.

Introduction

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].

Background

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.

Introduction

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.

Introduction

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.

Introduction

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.

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.

Introduction

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.

Arthrofibrosis remains a dominant post-operative complication and reason for returning to the OR following total knee arthroplasty. Trauma induced by ligament releases during TKA soft tissue balancing and soft tissue imbalance are thought to be contributing factors to arthrofibrosis, which is commonly treated by manipulation under anesthesia (MUA). We hypothesized that a robotic-assisted ligament balancing technique where the femoral component position is planned in 3D based on ligament gap data would result in lower MUA rates than a measured resection technique where the implants are planned based solely on boney alignment data and ligaments are released afterwards to achieve balance. We also aimed to determine the degree of mechanical axis deviation from neutral that resulted from the ligament balancing technique.

Introduction

There is increasing pressure on healthcare providers to demonstrate competitiveness in quality, patient outcomes and cost. Robotic and computer-assisted total knee arthroplasty (TKA) have been shown to be more accurate than conventional TKA, thereby potentially improving quality and outcomes, however these technologies are usually associated with longer procedural times and higher costs for hospitals. The aim of this study was to determine the surgical efficiency, learning curve and early patient satisfaction of robotic-assisted TKA with a contemporary imageless system.

Introduction:

Computer-assisted surgery (CAS) aims to improve component positioning and mechanical alignment in Total Knee Arthroplasty (TKA). Robotic cutting-guides have been integrated into CAS systems with the intent to improve bone-cutting precision and reduce navigation time by precisely automating the placement of the cutting-guide. The objectives of this study were to compare the intra-operative efficiency and accuracy of a robotic-assisted TKA procedure to a conventional computer-assisted TKA procedure where fixed sequential cutting-blocks are navigated free-hand.

Introduction

Intimate bone-implant contact is a requirement for achieving stable component fixation and osseo-integration of porous-coated implants in TKA. However, consistently attaining a press-fit and a tight-fitting femoral component can be problematic when using conventional instrumentation. We present a new robotic cutting-guide system that permits intra-operative adjustment of the femoral resections such that a specified amount of press-fit can be consistently attained.

System Description: A.R.T. (Apex Robotic Technology) employs a miniature bone-mounted robotic cutting-guide and flexible software that permits the surgeon to adjust the anterior and posterior femoral resections in increments of 0.25 mm per resection, allowing a maximum of 1.5mm of total added press in the AP dimension.