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

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.

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

Variation in resection thickness of the femur in Total Knee Arthroplasty (TKA) impacts the flexion and extension tightness of the knee. Less well investigated is how variation in patient anatomy drives flexion or extension tightness pre- and post- operatively. Extension and flexion stability of the post TKA knee is a function of the tension in the ligaments which is proportional to the strain. This study sought to investigate how femoral ligament offset relates to post-operative navigation kinematics and how outcomes are affected by component position in relation to ligament attachment sites.

Introduction & aims

Patient specific instrumentation (PSI) is a useful tool to execute pre-operatively planned surgical cuts and reduce the number of trays in surgery. Debate currently exists around improved accuracy, efficacy and patient outcomes when using PSI cutting guides compared to conventional instruments. Unicompartmental Knee Arthroplasty (UKA) revision to Total Knee Arthroplasty (TKA) represents a complex scenario in which traditional bone landmarks, and patient specific axes that are routinely utilised for component placement may no longer be easily identifiable with either conventional instruments or navigation. PSI guides are uniquely placed to solve this issue by allowing detailed analysis of the patient morphology outside the operating theatre. Here we present a tibia and femur PSI guide for TKA on patients with UKA.

Introduction

Knee ligament laxity and soft tissue balance are important pre- and intra- operative balancing factors in total knee arthroplasty (TKA). Laxity can be measured pre-operatively from short-leg radiographs using a stress device to apply a reproducible force to the knee, whereas intra-operative laxity is routinely measured using a navigation system in which a variable surgeon-applied force is applied. The relationship between these two methods and TKA outcome however, has not been investigated. This study aims to determine how intra-operative assessments of laxity relate to functional radiographic assessments performed on pre-operatively. We also investigate how laxity relates to short-term patient-reported outcomes.

Introduction

Kinematics post-TKA are complex; component alignment, component geometry and the patient specific musculoskeletal environment contribute towards the kinematic and kinetic outcomes of TKA. Tibial rotation in particular is largely uncontrolled during TKA and affects both tibiofemoral and patellofemoral kinematics. Given the complex nature of post- TKA kinematics, this study sought to characterize the contribution of tibial tray rotation to kinematic outcome variability across three separate knee geometries in a simulated framework.

Introduction & aims

Resurfacing of the patella is an important part of most TKA operations, usually using an onlay technique. One common practice is to medialise the patellar button and aim to recreate the patellar offset, but most systems do not well control alignment of the patella button. This study aimed to investigate for relationships between placement and outcomes and report on the accuracy of patella placement achieved with the aid of a patella Patient Specific Guide (PSG).

Introduction

Provision of prehabilitation prior to total knee arthroplasty (TKA) through a digital mobile application is a novel concept. The primary aim of our research is to determine whether provision of prehabilitation through a mobile digital application impacts length of stay (LOS), requirement for inpatient rehabilitation and hospital-associated costs after TKA. Our study hypothesis is that a mobile digital application provides a low resource, cost effective method of delivering prehabilitation prior to TKA.

Introduction

Dissatisfaction rates after TKA are reported to be between 15 – 25%, with unmet outcome expectations being a key contributor. Shared decision making tools (SDMT) are designed to align a patient's and surgeon's expectations. This study demonstrates clinical validation of a patient specific shared decision making tool.