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.
View Article and Find Full Text PDFBackground: Recent changes in Medicare reimbursement policies have facilitated the shift of primary total joint arthroplasty (TJA) volume to ambulatory surgical centers (ASC). The ASCs potentially provide a more cost-effective alternative to a hospital-setting TJA. This study investigated Medicare primary TJA utilization and reimbursement trends at ASCs compared to inpatient and outpatient settings between 2019 and 2022.
View Article and Find Full Text PDFBackground: The increasing incidence of primary total hip (THA) and knee (TKA) arthroplasty has been accompanied by a subsequent rise in revision surgeries. Revision total joint arthroplasty (TJA) is associated with major litigation risk, primarily due to procedural and postsurgical errors. However, the understanding of the causes and outcomes of revision TJA malpractice cases remains unstudied.
View Article and Find Full Text PDFKnee Surg Sports Traumatol Arthrosc
September 2024
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.
View Article and Find Full Text PDFIntroduction: 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.
Background: Total joint arthroplasty (TJA) is the most common procedure associated with malpractice claims within orthopaedic surgery. Although prior research has assessed prevalent causes and outcomes of TJA-related lawsuits before 2018, the dynamic healthcare environment demands regular re-evaluations. This study aimed to provide an updated analysis of the predominant causes and outcomes of TJA-related malpractice lawsuits and analyze the outcomes of subsequent appeals following initial jury verdicts.
View Article and Find Full Text PDFMed Biol Eng Comput
August 2024
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.
View Article and Find Full Text PDFRevision 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.
View Article and Find Full Text PDFBackground Context: Pedicle screw breach (PSB) is not uncommon following lumbar instrumentation, and in some instances, it may lead to vascular and/or neurologic complications. Previous literature suggested that screws crossing the vertebral midline on an anterior-posterior (AP) radiograph (or midsagittal on CT) are concerning for medial pedicle breach.
Objective: Our primary aim was to map out the safe zones (SZ) of bilateral pedicle instrumentation and their relationship at each lumbar vertebral level.
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.
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.
View Article and Find Full Text PDFBackground: 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.
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.
View Article and Find Full Text PDFBackground: Postoperative discharge to facilities account for over 33% of the $ 2.7 billion revision total knee arthroplasty (TKA)-associated annual expenditures and are associated with increased complications when compared to home discharges. Prior studies predicting discharge disposition using advanced machine learning (ML) have been limited due to a lack of generalizability and validation.
View Article and Find Full Text PDFBackground: 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.
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