Similar Medium-Term Revision Rates Following Acute Total Hip Arthroplasty Versus Open Reduction and Internal Fixation for Acetabular Fractures in the Elderly.

Background: The management of elderly acetabular fractures is complex, with high rates of conversion total hip arthroplasty (THA) after open reduction and internal fixation (ORIF), but potentially higher rates of complications after acute THA.

Methods: The California Office of Statewide Health Planning and Development database was queried between 2010 and 2017 for all patients aged 60 years or older who sustained a closed, isolated acetabular fracture and underwent ORIF, THA, or a combination. Chi-square tests and Student t tests were used to identify demographic differences between groups.

Compliant Intramedullary Stems for Joint Reconstruction.

The longevity of current joint replacements is limited by aseptic loosening, which is the primary cause of non-infectious failure for hip, knee, and ankle arthroplasty. Aseptic loosening is typically caused either by osteolysis from particulate wear, or by high shear stresses at the bone-implant interface from over-constraint. Our objective was to demonstrate feasibility of a compliant intramedullary stem that eliminates over-constraint without generating particulate wear.

A predictive algorithm for perioperative complications and readmission after ankle arthrodesis.

Purpose: Ankle arthrodesis is a mainstay of surgical management for ankle arthritis. Accurately risk-stratifying patients who undergo ankle arthrodesis would be of great utility. There is a paucity of accurate prediction models that can be used to pre-operatively risk-stratify patients for ankle arthrodesis.

Short-Term Outcomes and Long-Term Implant Survival After Inpatient Surgical Management of Geriatric Proximal Humerus Fractures.

Introduction: The most common surgical options for geriatric proximal humerus fractures are open reduction and internal fixation (ORIF), hemiarthroplasty (HA), and reverse total shoulder arthroplasty. We used a longitudinal inpatient discharge database to determine the cumulative incidence of conversion to arthroplasty after ORIF of geriatric proximal humerus fractures. The rates of short-term complications and all-cause reoperation were also compared.

Development and External Validation of a Risk Calculator for Prediction of Major Complications and Readmission After Anterior Cervical Discectomy and Fusion.

Study Design: A retrospective, case-control study.

Objective: We aim to build a risk calculator predicting major perioperative complications after anterior cervical fusion. In addition, we aim to externally validate this calculator with an institutional cohort of patients who underwent anterior cervical discectomy and fusion (ACDF).

Geriatric Distal Femur Fractures: Equivalent Long-Term Reoperation Rates Between Fixation and Primary Arthroplasty.

Objective: Our primary objectives were to (1) determine the rate of requiring conversion to arthroplasty after open reduction internal fixation (ORIF) of geriatric distal femur fractures and (2) compare 10-year reoperation rates after ORIF versus primary arthroplasty for geriatric distal femur fractures.

Design: Propensity-matched retrospective cohort study.

Setting: All centers participating in the California Office of Statewide Health Planning and Development (OSHPD) database.

A Risk Calculator for the Prediction of C5 Nerve Root Palsy After Instrumented Cervical Fusion.

Background: C5 palsy is a common postoperative complication after cervical fusion and is associated with increased health care costs and diminished quality of life. Accurate prediction of C5 palsy may allow for appropriate preoperative counseling and risk stratification. We primarily aim to develop an algorithm for the prediction of C5 palsy after instrumented cervical fusion and identify novel features for risk prediction.

Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Primary Anatomic Total Shoulder Replacements.

Background: The demand and incidence of anatomic total shoulder arthroplasty (aTSA) procedures is projected to increase substantially over the next decade. There is a paucity of accurate risk prediction models which would be of great utility in minimizing morbidity and costs associated with major post-operative complications. Machine learning is a powerful predictive modeling tool and has become increasingly popular, especially in orthopedics.

Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Reverse Total Shoulder Arthroplasty.

Background: Reverse total shoulder arthroplasty (rTSA) offers tremendous promise for the treatment of complex pathologies beyond the scope of anatomic total shoulder arthroplasty but is associated with a higher rate of major postoperative complications. We aimed to design and validate a machine learning (ML) model to predict major postoperative complications or readmission following rTSA.

Methods: We retrospectively reviewed California's Office of Statewide Health Planning and Development database for patients who underwent rTSA between 2015 and 2017.

Total joint replacement surgeon choice: A qualitative analysis in a medicare population.

Introduction: Previous research has shown that patients from historically marginalized groups in the United States tend to have poorer outcomes after joint replacement surgery and that they are less likely to receive joint replacement surgery at high-volume hospitals. However, little is known regarding how this group of patients chooses their joint replacement surgeon. The purpose of this study was to understand the factors influencing the choice of joint replacement surgeon amongst a diverse group of patients.

A Novel, Potentially Universal Machine Learning Algorithm to Predict Complications in Total Knee Arthroplasty.

Background: There remains a lack of accurate and validated outcome-prediction models in total knee arthroplasty (TKA). While machine learning (ML) is a powerful predictive tool, determining the proper algorithm to apply across diverse data sets is challenging. AutoPrognosis (AP) is a novel method that uses automated ML framework to incorporate the best performing stages of prognostic modeling into a single well-calibrated algorithm.

Machine learning-driven identification of novel patient factors for prediction of major complications after posterior cervical spinal fusion.

Purpose: Posterior cervical fusion is associated with increased rates of complications and readmission when compared to anterior fusion. Machine learning (ML) models for risk stratification of patients undergoing posterior cervical fusion remain limited. We aim to develop a novel ensemble ML algorithm for prediction of major perioperative complications and readmission after posterior cervical fusion and identify factors important to model performance.

Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.

Background: Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great utility. We aim to develop a machine learning model for prediction of major complications and readmission after lumbar fusion. We also aim to identify the factors most important to performance of each tested model.

Development of a Novel, Potentially Universal Machine Learning Algorithm for Prediction of Complications After Total Hip Arthroplasty.

Background: As the prevalence of hip osteoarthritis increases, the number of total hip arthroplasty (THA) procedures performed is also projected to increase. Accurately risk-stratifying patients who undergo THA would be of great utility, given the significant cost and morbidity associated with developing perioperative complications. We aim to develop a novel machine learning (ML)-based ensemble algorithm for the prediction of major complications after THA, as well as compare its performance against standard benchmark ML methods.

Risk Adjustment in the California Joint Replacement Registry: Is Patient Complexity Accurately Assessed in Academic Versus Nonacademic Hospitals?

Background: We sought to report on the differences in observed versus expected arthroplasty outcomes between academic and nonacademic hospitals in a large joint registry. We utilized the California Joint Replacement Registry's data and risk adjustment model.

Methods: Observed versus expected hip and knee arthroplasty complications were utilized to assess hospital and surgeon risk-adjusted complication rates (RACRs).

Randomized Trial of a Virtual Reality Tool to Teach Surgical Technique for Tibial Shaft Fracture Intramedullary Nailing.

Introduction: Active learning methods have accumulated popularity due to improved results in knowledge acquisition as opposed to passive learning methods. For surgical resident physicians with limited training opportunities outside of the operating room due to time constraints, virtual reality (VR) is a relatively inexpensive and time-efficient active training method for procurement of surgical skills. We conducted a simulated intramedullary nailing (IMN) of a tibia to demonstrate VR training programs as a more effective modality of learning orthopedic surgical techniques compared to passive learning tools such as a standard guide (SG) through trained novice medical students performing a SawBones simulation of intramedullary nail fixation.

The Role of Gender, Academic Affiliation, and Subspecialty in Relation to Industry Payments to Orthopaedic Surgeons.

Background: The Physician-Payments-Sunshine-Act (PPSA) was introduced in 2010 to provide transparency regarding physician-industry payments by making these payments publicly available. Given potential ethical implications, it is important to understand how these payments are being distributed, particularly as the women orthopaedic workforce increases. The purpose of this study was thus to determine the role of gender and academic affiliation in relation to industry payments within the orthopaedic subspecialties.

Incidence and predictors of early complications following primary and revision total ankle arthroplasty.

Background: Total ankle arthroplasty (TAA) offers an effective option for end-stage osteoarthritis. The incidence and preoperative risk factors for early adverse events (AEs) following primary and revision TAA may be useful information for providers.

Methods: A large database was queried from 2010 to 2016 to identify 905 patients of whom 818 underwent primary TAA (90.

Who Cares for Total Hip Arthroplasty Complications? Rates of Readmission to a Hospital Different From the Location of the Index Procedure.

Introduction: While the number of total hip arthroplasties (THAs) performed increases, so is the number of postoperative readmissions, resulting in costly episodes of care that may disproportionately affect certain hospitals. This study examines the rates of readmission of patients to the same hospital at which they underwent index THA, compared with readmission to a different hospital.

Methods: Data for all hospital discharges from 1995 to 2010 were obtained from the California Office of Statewide Health Planning and Development database.