Partial Knee Arthroplasty Research Articles

A radiographic artificial intelligence tool to identify candidates suitable for partial knee arthroplasty.

Knee osteoarthritis is a prevalent condition frequently necessitating knee replacement surgery, with demand projected to rise substantially. Partial knee arthroplasty (PKA) offers advantages over total knee arthroplasty (TKA), yet its utilisation remains low despite guidance recommending consideration alongside TKA in shared decision making. Radiographic decision aids exist but are underutilised due to clinician time constraints. This research develops a novel radiographic artificial intelligence (AI) tool using a dataset of knee radiographs and a panel of expert orthopaedic surgeons' assessments. Six AI models were trained to identify PKA candidacy. 1241 labelled four-view radiograph series were included. Models achieved statistically significant accuracies above random assignment, with EfficientNet-ES demonstrating the highest performance (AUC 95%, F1 score 83% and accuracy 80%). The AI decision tool shows promise in identifying PKA candidates, potentially addressing underutilisation of this procedure. Its integration into clinical practice could enhance shared decision making and improve patient outcomes. Further validation and implementation studies are warranted to assess real-world utility and impact.

Changes in health-related quality of life in young-old and old-old patients undergoing elective orthopedic surgery: A systematic review.

There is a significant gap in research exploring changes in postoperative health-related quality of life (HRQoL) among patients aged 65 years and older undergoing hip or knee arthroplasty. To investigate the variations in HRQoL improvement, as evaluated by patient-reported outcome measures following total hip arthroplasty, total knee arthroplasty, and partial knee arthroplasty between the young-old and old-old adults. We searched six online databases (including MEDLINE, Embase) from their inception dates to May 15, 2023. We included studies using a validated HRQoL assessment tool to evaluate changes in HRQoL in patients aged ≥65 years undergoing hip or knee arthroplasty. These include the EuroQol five-dimension (EQ-5D), Short Form 36 (SF-36) and Short Form 12 (SF-12). The primary outcomes were postoperative HRQoL changes between young-old (65-74 years) and old-old groups (≥75 years). The secondary outcomes included complications, length of stay, and mortality. The search yielded 12,229 articles; twelve studies (n = 103,613) were included. Studies using EQ-5D found no significant differences between young-old and old-old patients after hip and knee arthroplasty. Analyses of SF-36 and SF-12 scales showed no significant age-related differences in postoperative improvements in physical and mental health. Our review of four studies that included multivariable analyses revealed inconsistent associations between age and EQ-5D. Comparisons between the young-old and old-old age groups in postoperative complications, hospital length of stay, and mortality revealed no associated age-related changes in HRQoL. The young-old and old-old patients exhibited comparable improvement in HRQoL following hip or knee arthroplasty. The older patients did not have higher postoperative complications rates, longer hospital length of stay, and increased mortality. While chronological age should be considered when planning hip and knee arthroplasty, greater emphasis should be placed on assessing the comorbidities and functional status of patients.

Is outpatient joint arthroplasty safe ina high volume academic centre? A retrospective monocentric study using an institutional pathway.

Hip and knee arthroplasties are daily procedures in orthopaedic departments. Recently, same-day discharge (SDD) became increasingly popular, but doubts remain about its safety and generalization. Our hypothesis is that outpatient arthroplasty, in a high volume centre and with an institutional protocol, is an effective and reliable practice. We realized a monocentric retrospective study of patients undergoing outpatient partial (UKA) or total (TKA) knee or hip arthroplasty (THA) in a high volume academic centre using a well-defined institutional pathway. Epidemiological data and complications occurring in the month following surgery were studied. 498 patients undergoing 501 arthroplasties (219 hips and 282 knees) were examined. The percentage of men and women was 60.28% and 39.72% respectively, mean age was 64.56 ± 9.59years, mean BMI was 26.87 ± 4.2 and the most represented ASA score was 2. The success rate for same-day discharge was 97.21%. The most frequent causes of failure were urinary retention (28.6%), orthostatic hypotension (28.6%) and insufficiently controlled pain (14.3%). The readmission rate in the month following the operation was 0.8% and the rate of emergency department visits was 1.6%. Finally, the rate of early consultation visits was 7.98%. The comparison between success and failure subgroups in the outpatient setting of our cohort did not highlight statistically significant differences for studied parameters. Outpatient arthroplasty, performed in a center used to managing such operations and with a well-established institutional pre- and post-operative protocol, is a safe practice.

How Often Does Technology Fail in Robotic-Assisted Arthroplasty? A Comprehensive Analysis of a United States Food and Drug Administration Database

BackgroundRobotic-assisted arthroplasty is a growing alternative to conventionally instrumented arthroplasty; however, the incidence of adverse events (AEs) associated with this technology reported to the United States Food and Drug Administration (FDA) remains poorly quantified. The objective of this study was to categorize AEs associated with robotic-assisted arthroplasty and calculate their annual incidence as reported to the FDA. MethodsThe FDA’s Manufacturer and User Facility Device Experience database was queried for AEs from January 1, 2017 to December 31, 2021 associated with the most prevalent robotic-arthroplasty system. The AEs were calculated using national surgical numbers provided by the manufacturer and grouped by total hip arthroplasty (THA), total knee arthroplasty (TKA), or partial knee arthroplasty (PKA). ResultsThere were 1,710 unique AEs across the study period, with 436 THA, 1,005 TKA, and 269 PKA, representing incidence rates of 0.37, 0.30, and 0.40%, respectively. All procedures demonstrated lower rates of AEs in the final year of the study, compared to the first year. Most complications were related to mechanical problems, not software issues. Surgical delays due to AEs occurred in THA (0.13% cumulative incidence, 14.0 minutes average delay), TKA (0.13%, 20.6 minutes), and PKA (0.22%, 19.4 minutes). No cases were canceled due to AEs in THA, though a few TKA (0.003%) and PKA (0.02%) cases were not performed. Patient injuries occurred in 0.05, 0.05, and 0.08% of THA, TKA, and PKA, respectively. Surgical reintervention was necessary in 0.004, 0.003, and 0.01% of THA, TKA, and PKA, respectively. ConclusionsRobotic-assisted arthroplasty has a small number and relatively low rate of AEs reported to the FDA as measured through the Manufacturer and User Facility Device Experience database, with rates steadily decreasing over the study period. Patient injury, case delay, and reoperation represent only a small fraction of the already rare AEs in the database.

Prevention and treatment of peri-implant fibrosis by functionally inhibiting skeletal cells expressing the leptin receptor.

The cellular and molecular mediators of peri-implant fibrosis-a most common reason for implant failure and for surgical revision after the replacement of a prosthetic joint-remain unclear. Here we show that peri-implant fibrotic tissue in mice and humans is largely composed of a specific population of skeletal cells expressing the leptin receptor (LEPR) and that these cells are necessary and sufficient to generate and maintain peri-implant fibrotic tissue. In a mouse model of tibial implantation and osseointegration that mimics partial knee arthroplasty, genetic ablation of LEPR+ cells prevented peri-implant fibrosis and the implantation of LEPR+ cells from peri-implant fibrotic tissue was sufficient to induce fibrosis in secondary hosts. Conditional deletion of the adhesion G-protein-coupled receptor F5 (ADGRF5) in LEPR+ cells attenuated peri-implant fibrosis while augmenting peri-implant bone formation, and ADGRF5 inhibition by the intra-articular or systemic administration of neutralizing anti-ADGRF5 in the mice prevented and reversed peri-implant fibrosis. Pharmaceutical agents that inhibit the ADGRF5 pathway in LEPR+ cells may be used to prevent and treat peri-implant fibrosis.

A single center descriptive study of local anesthetic dose in knee arthroplasty: Was there evidence of local anesthetic systemic toxicity?

Study objectiveDescribe dosing of local anesthetic when both a periarticular injection (PAI) and peripheral nerve block (PNB) are utilized for knee arthroplasty analgesia, and compare the dosing of local to suggested maximum dosing, and look for evidence of local anesthetic systemic toxicity (LAST). DesignA single center retrospective cohort study between May 2018 and November 2022. SettingA major academic hospital. PatientsPatients who had both a PAI and PNB while undergoing primary, revision, total, partial, unilateral, or bilateral knee arthroplasty. InterventionsNone. MeasurementsCalculate the dose of local anesthetic given via PAI, PNB, and both routes combined as based on lean body weight and compare that to the suggested maximum dosing. Look for medications, clinical interventions, and critical event notes suggestive of a LAST event. Main resultsThere were 4527 knee arthroplasties where both a PAI and PNB were performed during the study period. When combining PAI and PNB doses, >75% of patients received more than the suggested maximum dose of 3 mg/kg lean body weight. The median local anesthetic dosing over the study period, 4.4 mg/kg (IQR 3.5,5.9), was 147% of the suggested maximum dose (IQR 117,197). There was no conclusive evidence of LAST among any of the patients in the study. ConclusionsOver the course of our study, we had 4527 knee arthroplasties with a median PAI and PNB local anesthetic dose that was 147% of the suggested maximum without any clear clinical evidence of a LAST event.

Stepping Up Recovery: Integrating Patient-reported Outcome Measures and Wearable Technology for Rehabilitation Following Knee Arthroplasty.

Improvement after knee arthroplasty (KA) is often measured using patient-reported outcome measures (PROMs). However, PROMs are limited due to their subjectivity. Therefore, wearable technology is becoming commonly utilized to objectively assess physical activity and function. We assessed the correlation between PROMs and step/stair flight counts in total (TKA) and partial knee arthroplasty (PKA) patients.Analysis of a multicenter, prospective, longitudinal cohort study investigating the collection of average daily step and stair flight counts, was performed. Subjects (N = 1,844 TKA patients and N = 489 PKA patients) completed the Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) and provided numerical rating scale pain scores pre- and postoperatively. Only patients who reported living in a multilevel home environment (N = 896 TKA patients and N = 258 PKA patients) were included in analysis of stair flight counts. Pearson correlation coefficients were calculated to determine correlations between variables.Among TKA patients, pain scores demonstrated a negative correlation to mean step counts at preoperative (r = -0.14, p < 0.0001) and 1-month follow-up (r = -0.14, p < 0.0001). Similar negative correlations were true for pain and stair flight counts at preoperative (r = -0.16, p < 0.0001) and 1-month follow-up (r = -0.11, p = 0.006). KOOS JR scores demonstrated weak positive correlations with mean step counts at preoperative (r = 0.19, p < 0.0001) and 1-month postoperative (r = 0.17, p < 0.0001). Similar positive correlations were true for KOOS JR scores and stair flight counts preoperatively (r = 0.13, p = 0.0002) and at 1-month postoperatively (r = 0.10, p = 0.0048). For PKA patients, correlations between pain and KOOS JR with step/stair counts demonstrated similar directionality.Given the correlation between wearable-generated data and PROMs, wearable technology may be beneficial in evaluating patient outcomes following KA. By combining subjective feedback with the objective data, health care providers can gain a holistic view of patients' progress and tailor treatment plans accordingly.

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

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

Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty.

Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling approaches such as traditional survivorship estimators (such as Kaplan-Meier) or competing risk estimators. Recent advances in machine learning survival analysis might improve decision support tools in this setting. Therefore, this study aimed to assess the performance of machine learning compared with that of conventional modeling to predict revision after arthroplasty. Does machine learning perform better than traditional regression models for estimating the risk of revision for patients undergoing hip or knee arthroplasty? Eleven datasets from published studies from the Dutch Arthroplasty Register reporting on factors associated with revision or survival after partial or total knee and hip arthroplasty between 2018 and 2022 were included in our study. The 11 datasets were observational registry studies, with a sample size ranging from 3038 to 218,214 procedures. We developed a set of time-to-event models for each dataset, leading to 11 comparisons. A set of predictors (factors associated with revision surgery) was identified based on the variables that were selected in the included studies. We assessed the predictive performance of two state-of-the-art statistical time-to-event models for 1-, 2-, and 3-year follow-up: a Fine and Gray model (which models the cumulative incidence of revision) and a cause-specific Cox model (which models the hazard of revision). These were compared with a machine-learning approach (a random survival forest model, which is a decision tree-based machine-learning algorithm for time-to-event analysis). Performance was assessed according to discriminative ability (time-dependent area under the receiver operating curve), calibration (slope and intercept), and overall prediction error (scaled Brier score). Discrimination, known as the area under the receiver operating characteristic curve, measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities; a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest. A scaled version of the Brier score, 1 - (model Brier score/null model Brier score), can be interpreted as the amount of overall prediction error. Using machine learning survivorship analysis, we found no differences between the competing risks estimator and traditional regression models for patients undergoing arthroplasty in terms of discriminative ability (patients who received a revision compared with those who did not). We found no consistent differences between the validated performance (time-dependent area under the receiver operating characteristic curve) of different modeling approaches because these values ranged between -0.04 and 0.03 across the 11 datasets (the time-dependent area under the receiver operating characteristic curve of the models across 11 datasets ranged between 0.52 to 0.68). In addition, the calibration metrics and scaled Brier scores produced comparable estimates, showing no advantage of machine learning over traditional regression models. Machine learning did not outperform traditional regression models. Neither machine learning modeling nor traditional regression methods were sufficiently accurate in order to offer prognostic information when predicting revision arthroplasty. The benefit of these modeling approaches may be limited in this context.

Load transfer in bone after partial, multi-compartmental, and total knee arthroplasty.

Introduction: Arthroplasty-associated bone loss remains a clinical problem: stiff metallic implants disrupt load transfer to bone and, hence, its remodeling stimulus. The aim of this research was to analyze how load transfer to bone is affected by different forms of knee arthroplasty: isolated partial knee arthroplasty (PKA), compartmental arthroplasty [combined partial knee arthroplasty (CPKA), two or more PKAs in the same knee], and total knee arthroplasty (TKA). Methods: An experimentally validated subject-specific finite element model was analyzed native and with medial unicondylar, lateral unicondylar, patellofemoral, bi-unicondylar, medial bicompartmental, lateral bicompartmental, tricompartmental, and total knee arthroplasty. Three load cases were simulated for each: gait, stair ascent, and sit-to-stand. Strain shielding and overstraining were calculated from the differences between the native and implanted states. Results: For gait, the TKA femoral component led to mean strain shielding (30%) more than three times higher than that of PKA (4%-7%) and CPKA (5%-8%). Overstraining was predicted in the proximal tibia (TKA 21%; PKA/CPKA 0%-6%). The variance in the distribution for TKA was an order of magnitude greater than for PKA/CPKA, indicating less physiological load transfer. Only the TKA-implanted femur was sensitive to the load case: for stair ascent and gait, almost the entire distal femur was strain-shielded, whereas during sit-to-stand, the posterior femoral condyles were overstrained. Discussion: TKA requires more bone resection than PKA and CPKA. These finite element analyses suggest that a longer-term benefit for bone is probable as partial and multi-compartmental knee procedures lead to more natural load transfer compared to TKA. High-flexion activity following TKA may be protective of posterior condyle bone resorption, which may help explain why bone loss affects some patients more than others. The male and female bone models used for this research are provided open access to facilitate future research elsewhere.

Robot-assisted partial knee replacement versus standard total knee replacement (RoboKnees): a protocol for a pilot randomized controlled trial

BackgroundTotal knee arthroplasty is a common surgery for end-stage knee osteoarthritis. Partial knee arthroplasty is also a treatment option for patients with arthritis present in only one or two knee compartments. Partial knee arthroplasty can preserve the natural knee biomechanics, but these replacements may not last as long as total knee replacements. Robotic-assisted orthopedic techniques can help facilitate partial knee replacements, increasing accuracy and precision. This trial will investigate the feasibility and assess clinical outcomes for a larger definitive trial.MethodsThis is a protocol for an ongoing parallel randomized pilot trial of 64 patients with uni- or bicompartmental knee arthritis. Patients are randomized to either receive robot-assisted partial knee arthroplasty or manual total knee arthroplasty. The primary outcome of this pilot is investigating the feasibility of a larger trial. Secondary (clinical) outcomes include joint awareness, return to activities, knee function, patient global impression of change, persistent post-surgical pain, re-operations, resource utilization and cost-effectiveness, health-related quality of life, radiographic alignment, knee kinematics during walking gait, and complications up to 24 months post-surgery.DiscussionThe RoboKnees pilot study is the first step in determining the outcome of robot-assisted partial knee replacements. Conclusions from this study will be used to design future large-scale trials. This study will inform surgeons about the potential benefits of robot-assisted partial knee replacements.Trial registrationThis study was prospectively registered on clinicaltrials.gov (identifier: NCT04378049) on 4 May 2020, before the first patient was randomized.

Learning curve of Persona Partial Knee (PPK) arthroplasty: a clinical trial

BackgroundUnicompartmental knee arthroplasty (UKA) procedures are considered to be more technically demanding than conventional total knee arthroplasty (TKA), requiring a longer learning curve and more expert surgical skills. Despite some clear advantages of UKA over TKA (such as lesser blood loss, greater bone stock, greater knee performances, etc.), UKA evidenced a greater rate of revision.ObjectThis study investigated the learning curve of Persona Partial Knee (PPK) arthroplasty for primary medial UKA performed by a single, non-designer surgeon. PPK is a fixed-bearing, compartment-specific implant. The primary outcome of interest for this study was to evaluate the learning curve of the surgical duration. The secondary outcome of interest was to evaluate the learning curve of radiological implant positioning.MethodsPatients who underwent primary medial UKA using PPK (Zimmer-Biomet, Warsaw IN, USA) were prospectively enrolled for the study. All surgeries were performed by a single, non-designer surgeon experienced in knee and hip arthroplasty. The primary outcome of interest was to evaluate the surgical duration. The secondary outcome of interest was to evaluate the implant positioning. The learning curve was estimated using an appropriate nonlinear polynomial regression model with a lower Akaike Information Criterion (AIC).ResultsOne hundred twenty five patients were enrolled in the study. 59% of them (74 of 125 patients) were women. The patients’ mean age at the time of surgery was 70.1 ± 9.5 years and their mean body mass index (BMI) was 27.8 ± 4.2 kg/m2. Curve stabilisation of the surgical time was at the 94th patient, of the tibial angle at the 47th patient, of the tibial slope at the 54th patient, of the anterior protrusion at the 29th patient, and of the posterior protrusion at the 51st patient.ConclusionsThe learning curve for component positioning was achieved in approximately 50 cases. The curve of the surgical time achieved a plateau at 94 Persona Partial Knee. Additionally, the factors directly correlated with earlier stabilization of the learning curve in terms of component positioning were: male gender, younger age, right side, and larger components.

Patient health-related quality of life deteriorates significantly while waiting six to 12 months for total hip or knee arthroplasty.

The primary aim was to assess change in health-related quality of life (HRQoL) of patients as they waited from six to 12 months for a total hip (THA) or total or partial knee arthroplasty (KA). Secondary aims were to assess change in joint-specific function, mental health, quality of sleep, number living in a state worse than death (WTD), wellbeing, and patient satisfaction with their healthcare. This prospective study included 142 patients awaiting a THA (mean age 66.7 years (SD 11.4); 71 female) and 214 patients awaiting KA (mean age 69.7 years (SD 8.7); 117 female). Patients completed questionnaires (EuroQol five-dimension health questionnaire (EQ-5D), Oxford Hip and Knee Scores (OHS/OKS), Pittsburgh Sleep Quality Index (PSQI), Hospital Anxiety and Depression Score (HADS), University of California, Los Angeles Activity Scale, wellbeing assessment, and satisfaction with their healthcare) at six and 12 months while awaiting surgery. There was a clinical and statistically significant deterioration in the EQ-5D while awaiting THA (mean change 0.071 (95% confidence interval (CI) 0.018 to 0.124); p = 0.009) and KA (mean change 0.069 (95% CI 0.032 to 0.106); p < 0.001). For patients awaiting a THA, there were deteriorations in OHS (p = 0.003), PSQI (p = 0.008), both HADS depression (p = 0.001) and anxiety (p = 0.002), and an increased prevalence in those in a state WTD (p = 0.010). For those awaiting KA, there were significant deteriorations in OKS (p < 0.001), UCLA (p = 0.001), and HADS depression (p < 0.001) and anxiety (p < 0.001). There were significant decreases in wellbeing (satisfaction with life, feeling life to be worthwhile, and happiness) and increases in anxiety for those awaiting THA or KA (p < 0.001). Those awaiting THA (odds ratio (OR) 0.52 (95% CI 0.31 to 0.89); p = 0.016) and KA (OR 0.46 (95% CI 0.31 to 0.71); p < 0.001) had a significant decrease in satisfaction with their healthcare. As patients waited from six to 12 months for THA or KA, they experienced a clinically significant deterioration in HRQoL. There were also deteriorations in joint-specific function, mental health, wellbeing, and patient satisfaction with healthcare.

Temporal trends in revision rate due to knee periprosthetic joint infection: a study of 115,120 cases from the Danish Knee Arthroplasty Register.

We aimed to examine the temporal trends in periprosthetic joint infection (PJI) revision incidence after knee arthroplasty (KA) from 1997 through 2019. 115,120 primary KA cases from the Danish Knee Arthroplasty Register were followed until the first PJI revision. We computed cumulative incidences and adjusted hazard ratios (aHRs) of PJI revision by calendar periods and several patient- and surgical-related risk factors. Results were analyzed from 0-3 months and from 3-12 months after KA. The overall 1-year PJI revision incidence was 0.7%, increasing from 0.5% to 0.7% (1997 through 2019). The incidence of PJI revision within 3 months increased from 0.1% to 0.5% (1997 through 2019). The adjusted hazard ratio (aHR) within 1 year of primary KA was 5.1 comparing 2017-2019 with 2001-2004. The PJI revision incidence from 3-12 months of KA decreased from 0.4% to 0.2%, with an aHR of 0.5 for 2017-2019 vs. 2001-2004. Male sex, age 75-84 (vs. 65-74), and extreme obesity (vs. normal weight) were positively associated with the risk of PJI revision within 3 months, whereas only male sex was associated from 3-12 months. Partial knee arthroplasty (PKA) vs. total KA was associated with a lower risk of PJI revision both within 3 months and 3-12 months of KA. We observed an increase in PJI revision within 3 months of KA, and a decrease in PJI revision incidence from 3-12 months from 1997 through 2019. The reasons for this observed time-trend are thought to be multifactorial. PKA was associated with a lower risk of PJI revision.

Identifying Preoperative Predictors for 24-Hour Discharge After Elective Hip and Knee Arthroplasties.

Introduction The resumption of elective medical services post-pandemic has brought to the forefront the importance of outpatient arthroplasty services in promoting efficiency and mitigating hospital-acquired infections. This study aimed to identify preoperative factors that predict the success of outpatient lower limb arthroplasty surgeries. Methods Our investigation involved a retrospective review of 606 patients who underwent elective hip and knee arthroplasty. We documented variables such as the hospital length of stay, patient demographics (age and gender), Oxford Joint Scores, body mass index, socioeconomic status, American Society of Anaesthesiologists' (ASA) physical status classification, comorbid conditions, the Functional Comorbidity Index (FCI), preoperative blood test results, implant types, scheduling details of the surgery, and rates of readmission within 30 days post-surgery. A two-step analysis using univariate and multivariate regression models was performed to pinpoint preoperative indicators that could predict same-day discharge following arthroplasty. Results Forty-five patients (7.4%) were discharged within 24 hours of surgery. Early discharge did not correlate with higher rates of readmission within 30 days (p>0.05). Neither weekend nor afternoon surgeries significantly extended the length of stay beyond 24 hours (p>0.05). No significant differences in the prevalence of comorbidities, FCI scores, socioeconomic status, or preoperative blood test results were found when comparing patients discharged within 24 hours to those who stayed longer. Multivariate analysis revealed that patients younger than 65 years (relative risk (RR) 2.41; 95% confidence interval (CI) 1.02-5.74) and those receiving partial knee arthroplasty (RR 8.91; 95% CI 3.05-26.04) were more likely to be discharged within 24 hours. Conclusions Outpatient arthroplasty is a viable option, especially for individuals younger than 65 years undergoing partial knee arthroplasty, independent of other patient-related factors, comorbidities, and specifics of the hospital episode.

Knee Cartilage Lesion Management-Current Trends in Clinical Practice.

Many patients, particularly those aged above 40, experience knee joint pain, which hampers both sports activities and daily living. Treating isolated chondral and osteochondral defects in the knee poses a significant clinical challenge, particularly in younger patients who are not typically recommended partial or total knee arthroplasty as alternatives. Several surgical approaches have been developed to address focal cartilage defects. The treatment strategies are characterized as palliation (e.g., chondroplasty and debridement), repair (e.g., drilling and microfracture), or restoration (e.g., autologous chondrocyte implantation, osteochondral autograft, and osteochondral allograft). This review offers an overview of the commonly employed clinical methods for treating articular cartilage defects, with a specific focus on the clinical trials conducted in the last decade. Our study reveals that, currently, no single technology fully meets the essential requirements for effective cartilage healing while remaining easily applicable during surgical procedures. Nevertheless, numerous methods are available, and the choice of treatment should consider factors such as the location and size of the cartilage lesion, patient preferences, and whether it is chondral or osteochondral in nature. Promising directions for the future include tissue engineering, stem cell therapies, and the development of pre-formed scaffolds from hyaline cartilage, offering hope for improved outcomes.

Overcoming floor and ceiling effects in knee arthroplasty outcome measurement.

To map the Oxford Knee Score (OKS) and High Activity Arthroplasty Score (HAAS) items to a common scale, and to investigate the psychometric properties of this new scale for the measurement of knee health. Patient-reported outcome measure (PROM) data measuring knee health were obtained from the NHS PROMs dataset and Total or Partial Knee Arthroplasty Trial (TOPKAT). Assumptions for common scale modelling were tested. A graded response model (fitted to OKS item responses in the NHS PROMs dataset) was used as an anchor to calibrate paired HAAS items from the TOPKAT dataset. Information curves for the combined OKS-HAAS model were plotted. Bland-Altman analysis was used to compare common scale scores derived from OKS and HAAS items. A conversion table was developed to map between HAAS, OKS, and the common scale. We included 3,329 response sets from 528 patients undergoing knee arthroplasty. These generally met the assumptions of unidimensionality, monotonicity, local independence, and measurement invariance. The HAAS items provided more information than OKS items at high levels of knee health. Combining both instruments resulted in higher test-level information than either instrument alone. The mean error between common scale scores derived from the OKS and HAAS was 0.29 logits. The common scale allowed more precise measurement of knee health than use of either the OKS or HAAS individually. These techniques for mapping PROM instruments may be useful for the standardization of outcome reporting, and pooling results across studies that use either PROM in individual-patient meta-analysis.

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

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

Regional Trends in Unicondylar and Patellofemoral Knee Arthroplasty: An Analysis of the American Joint Replacement Registry

BackgroundAs the popularity of unicondylar knee arthroplasty (UKA) and patellofemoral arthroplasty (PFA) have expanded, more patients who have limited arthritis are undergoing partial knee arthroplasty. No studies have evaluated if any regional differences in the United States (U.S.) exist between partial versus total knee arthroplasty (TKA). The purpose of this study was to utilize the American Joint Replacement Registry to evaluate regional differences in UKA, PFA, and TKA. MethodsThe American Joint Replacement Registry was queried for all TKA, PFA, and UKA procedures between 2012 and 2021. Surgical volume was compared between the Midwest (MW), Northeast (NE), South, and West (W) regions. Trends were compared using multivariate logistic regression analyses and least squared mean logistic regression models. ResultsSince 2012, there has been a steady increase in the amount of UKAs performed across the U.S., except during the COVID-19 pandemic when numbers decreased. Logistic regression analyses demonstrated a higher likelihood of receiving a UKA compared to TKA in the NE compared to other regions, and higher likelihood of receiving a PFA in the NE and W compared to other regions over the study period. Other factors that increased the likelihood of having a UKA nationally were men, the procedure being performed at a teaching hospital, and having surgery in urban areas. Patients were more likely to have a PFA in the NE compared to the S and MW, and higher odds in the South and W compared to the MW, with rates of PFA consistently lower in the MW compared to other regions. ConclusionsPatients were more likely to have a UKA in the NE compared to other regions of the country. Patients had higher odds of having a PFA in the NE and W regions relative to the MW. Men had higher odds of having either UKA or PFA than women across the nation.

Current State of Knee Arthroplasty in Russia: Analysis of 36,350 Сases from the Register of the Vreden National Medical Research Center of Traumatology and Orthopedics

Background.Nowadays the knee arthroplasty register of the Vreden National Medical Research Center of Traumatology and Orthopedics (hereinafter referred to as the Vreden Center) contains clinical and statistical data on more than 39,000 primary and revision knee replacements, that mimics current state of this kind of surgery in Russia.&#x0D; Aimofthestudy to analyze the last decade trends in primary knee arthroplasty in largest Russian arthroplasty center.&#x0D; Methods.Data were obtained from the register of the Vreden Center for the period from 2011 to 2022. Information on knee arthroplasty included epidemiologic and numerous peri-operative data including type of surgery and implant, degree of constrain, primary patella resurfacing etc.&#x0D; Resultsanddiscussion.From 2011 to 2022, 36,350 (92.3%) primary arthroplasties performed at the Vreden Center.&#x0D; The number of interventions increased more than twice: from 1,678 in 2011 to 3,924 in 2022. Similar trends observed in Australia and Sweden, where the number of knee arthroplasties increased by 8.2% and 8% in 2021 compared to 2020, respectively. The frequency of primary patellar resurfacing at the Vreden Center was 2.2% over the entire period of observation. On the contrary, the rate of patella replacement increased from 41% in 2005 to 76.1% in 2021 in Australia and from 24.4% in 2015 to 31.9% in 2020 in Switzerland. The partial knee arthroplasty showed enormous growth more than 14 times: from 0.3% in 2011 to 4.3% in 2022 at the Vreden Center. Worldwide unicompartmental knee replacement is still less popular than total and its number widely varies: 4.2% in the USA, 6.9% in Australia, 9.2% in Canada, 11.9% in Norway, 12.8% in Sweden, and 18.4% in Switzerland. Posterior cruciate ligament (PCL) retaining total knee arthroplasties (TKA) prevailed at the Vreden Center: 68.3%, while in other countries it utilize even more widely: 70.5% in Norway, 75% in New Zealand and 93.5% in Sweden.&#x0D; The total length of hospital stay (LOS) decreased dramatically from 19.6 in 2011 to 8.6 in 2022 at the Vreden Center. Nevertheless, there are still opportunities to improve it: by the way in Canada the average LOS for TKA is 2.3 and the USA 0.8 and 1.7 for partial and total arthroplasty, respectively.&#x0D; Conclusion.The main current trends of knee arthroplasty in Russia are the following: increase the number of surgeries, reduced LOS, TKA without patella resurfacing and with PCL retention, finally the growth of partial knee arthroplasties.