Predicting 30-day reoperation following primary total knee arthroplasty: machine learning model outperforms the ACS risk calculator.

The ACS risk calculator (ARC) has proven less effective in predicting patient-specific risk of early reoperation after primary total knee arthroplasty (TKA), compromising care quality and cost efficiency. This study compared the performance of a machine learning (ML) model and ARC in predicting 30-day reoperation after primary TKA using a national-scale dataset. Data of 366,151 TKAs were acquired from the ACS-NSQIP database.

Machine learning model outperforms the ACS Risk Calculator in predicting non-home discharge following primary total knee arthroplasty.

Purpose: Despite the increase in outpatient total knee arthroplasty (TKA) procedures, many patients are still discharged to non-home locations following index surgery. The ability to accurately predict non-home discharge (NHD) following TKAs has the potential to promote a reduction in associated adverse events and excess healthcare costs. This study aimed to evaluate whether a machine learning (ML) model could outperform the American College of Surgeons (ACS) Risk Calculator in predicting NHD following TKA, using the same set of clinical variables.

Predicting prolonged length of stay following revision total knee arthroplasty: A national database analysis using machine learning models.

Background: As the number of revision total knee arthroplasty (TKA) continues to rise, close attention has been paid to factors influencing postoperative length of stay (LOS). The aim of this study is to develop generalizable machine learning (ML) algorithms to predict extended LOS following revision TKA using data from a national database.

Methods: 23,656 patients undergoing revision TKA between 2013 and 2020 were identified using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database.

Predicting extended hospital stay following revision total hip arthroplasty: a machine learning model analysis based on the ACS-NSQIP database.

Introduction: Prolonged length of stay (LOS) following revision total hip arthroplasty (THA) can lead to increased healthcare costs, higher rates of readmission, and lower patient satisfaction. In this study, we investigated the predictive power of machine learning (ML) models for prolonged LOS after revision THA using patient data from a national-scale patient repository.

Materials And Methods: We identified 11,737 revision THA cases from the American College of Surgeons National Surgical Quality Improvement Program database from 2013 to 2020.

Are social determinants of health associated with an increased length of hospitalization after revision total hip and knee arthroplasty? A comparison study of social deprivation indices.

Introduction: Length of stay (LOS) has been extensively assessed as a marker for healthcare utilization, functional outcomes, and cost of care for patients undergoing arthroplasty. The notable patient-to-patient variation in LOS following revision hip and knee total joint arthroplasty (TJA) suggests a potential opportunity to reduce preventable discharge delays. Previous studies investigated the impact of social determinants of health (SDoH) on orthopaedic conditions and outcomes using deprivation indices with inconsistent findings.

Generalizability of machine learning models predicting 30-day unplanned readmission after primary total knee arthroplasty using a nationally representative database.

Unplanned readmission after primary total knee arthroplasty (TKA) costs an average of US $39,000 per episode and negatively impacts patient outcomes. Although predictive machine learning (ML) models show promise for risk stratification in specific populations, existing studies do not address model generalizability. This study aimed to establish the generalizability of previous institutionally developed ML models to predict 30-day readmission following primary TKA using a national database.

Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort.

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset.

Do preoperative PROMIS scores independently predict 90-day readmission following primary total knee arthroplasty?

Introduction: The rising demand for total knee arthroplasty (TKA) is expected to increase the total number of TKA-related readmissions, presenting significant public health and economic burden. With the increasing use of Patient-Reported Outcomes Measurement Information System (PROMIS) scores to inform clinical decision-making, this study aimed to investigate whether preoperative PROMIS scores are predictive of 90-day readmissions following primary TKA.

Materials And Methods: We retrospectively reviewed a consecutive series of 10,196 patients with preoperative PROMIS scores who underwent primary TKA.

Can machine learning models predict prolonged length of hospital stay following primary total knee arthroplasty based on a national patient cohort data?

Introduction: The total length of stay (LOS) is one of the biggest determinators of overall care costs associated with total knee arthroplasty (TKA). An accurate prediction of LOS could aid in optimizing discharge strategy for patients in need and diminishing healthcare expenditure. The aim of this study was to predict LOS following TKA using machine learning models developed on a national-scale patient cohort.

Machine Learning Models Based on a National-Scale Cohort Identify Patients at High Risk for Prolonged Lengths of Stay Following Primary Total Hip Arthroplasty.

Background: Existing machine learning models that predicted prolonged lengths of stay (LOS) following primary total hip arthroplasty (THA) were limited by the small training volume and exclusion of important patient factors. This study aimed to develop machine learning models using a national-scale data set and examine their performance in predicting prolonged LOS following THA.

Methods: A total of 246,265 THAs were analyzed from a large database.

Validation of Machine Learning Model Performance in Predicting Blood Transfusion After Primary and Revision Total Hip Arthroplasty.

Background: The rates of blood transfusion following primary and revision total hip arthroplasty (THA) remain as high as 9% and 18%, respectively, contributing to patient morbidity and healthcare costs. Existing predictive tools are limited to specific populations, thereby diminishing their clinical applicability. This study aimed to externally validate our previous institutionally developed machine learning (ML) algorithms to predict the risk of postoperative blood transfusion following primary and revision THA using national inpatient data.

Effects of prolonged brisk walking induced lower limb muscle fatigue on the changes of gait parameters in older adults.

Background: Lower extremity muscle fatigue affects gait stability and increases the probability of injuries in the elderly.

Research Question: How does prolonged walking-induced fatigue affect lower limb muscle activity, plantar pressure distribution, and tripping risk?

Methods: Eighteen elderly adults walked fast on a treadmill for 60 minutes at a fixed speed. The plantar pressure was measured with an in-shoe monitoring system, eight lower limb muscles were monitored using surface electromyography, and foot movements were tracked by a motion capture analysis system.

Internal and External Validation of the Generalizability of Machine Learning Algorithms in Predicting Non-home Discharge Disposition Following Primary Total Knee Joint Arthroplasty.

Background: Nonhome discharge disposition following primary total knee arthroplasty (TKA) is associated with a higher rate of complications and constitutes a socioeconomic burden on the health care system. While existing algorithms predicting nonhome discharge disposition varied in degrees of mathematical complexity and prediction power, their capacity to generalize predictions beyond the development dataset remains limited. Therefore, this study aimed to establish the machine learning model generalizability by performing internal and external validations using nation-scale and institutional cohorts, respectively.

Dynamic finite element analyses to compare the influences of customised total talar replacement and total ankle arthroplasty on foot biomechanics during gait.

Unlabelled: Objective, Total talar replacement (TTR) using a customised talus prosthesis is an emerging surgical alternative to conventional total ankle arthroplasty (TAA) for treating ankle problems. Upon satisfying clinical reports in the literature, this study explored the advantages of TTR in restoring foot biomechanics during walking compared with TAA through computational simulations.Methods, A dynamic finite element foot model was built from the MRIs of a healthy participant and modified into two implanted counterparts (TTR and TAA) by incorporating the corresponding prosthetic components into the ankle joint.

The utilization of artificial neural networks for the prediction of 90-day unplanned readmissions following total knee arthroplasty.

Background: A reliable predictive tool to predict unplanned readmissions has the potential to lower readmission rates through targeted pre-operative counseling and intervention with respect to modifiable risk factors. This study aimed to develop and internally validate machine learning models for the prediction of 90-day unplanned readmissions following total knee arthroplasty.

Methods: A total of 10,021 consecutive patients underwent total knee arthroplasty.

Reversing the Mismatch With Forefoot Striking to Reduce Running Injuries.

Recent studies have suggested that 95% of modern runners land with a rearfoot strike (RFS) pattern. However, we hypothesize that running with an RFS pattern is indicative of an evolutionary mismatch that can lead to musculoskeletal injury. This perspective is predicated on the notion that our ancestors evolved to run barefoot and primarily with a forefoot strike (FFS) pattern.

Different Design Feature Combinations of Flatfoot Orthosis on Plantar Fascia Strain and Plantar Pressure: A Muscle-Driven Finite Element Analysis With Taguchi Method.

Customized foot orthosis is commonly used to modify foot posture and relieve foot pain for adult acquired flexible flatfoot. However, systematic investigation of the influence of foot orthotic design parameter combination on the internal foot mechanics remains scarce. This study aimed to investigate the biomechanical effects of different combinations of foot orthoses design features through a muscle-driven flatfoot finite element model.

A Three-Dimensional Printed Foot Orthosis for Flexible Flatfoot: An Exploratory Biomechanical Study on Arch Support Reinforcement and Undercut.

The advancement of 3D printing and scanning technology enables the digitalization and customization of foot orthosis with better accuracy. However, customized insoles require rectification to direct control and/or correct foot deformity, particularly flatfoot. In this exploratory study, we aimed at two design rectification features (arch stiffness and arch height) using three sets of customized 3D-printed arch support insoles (R+U+, R+U-, and R-U+).

Biomechanical comparison among five mid/hindfoot arthrodeses procedures in treating flatfoot using a musculoskeletal multibody driven finite element model.

Background And Objective: Mid/hindfoot arthrodesis could modify the misalignment of adult-acquired flatfoot and attenuate pain. However, the long-term biomechanical effects of these surgical procedures remain unclear, and the quantitative evidence is scarce. Therefore, we aimed to investigate and quantify the influences of five mid/hindfoot arthrodeses on the internal foot biomechanics during walking stance.

A half marathon shifts the mediolateral force distribution at the tibiofemoral joint.

Runners' gait patterns vary during a half marathon and influence the knee joint mechanics. Joint contact force is a better estimate of the net joint loadings than external joint moments and closely correlates to injury risks. This study explored the changes of lower limb joint kinematics, muscle activities, and knee joint loading in runners across the running mileages of a half marathon.

Plantar Pressure Variability and Asymmetry in Elderly Performing 60-Minute Treadmill Brisk-Walking: Paving the Way towards Fatigue-Induced Instability Assessment Using Wearable In-Shoe Pressure Sensors.

Evaluation of potential fatigue for the elderly could minimize their risk of injury and thus encourage them to do more physical exercises. Fatigue-related gait instability was often assessed by the changes of joint kinematics, whilst planar pressure variability and asymmetry parameters may complement and provide better estimation. We hypothesized that fatigue condition (induced by the treadmill brisk-walking task) would lead to instability and could be reflected by the variability and asymmetry of plantar pressure.