Differences in total shoulder arthroplasty utilization and 30-day outcomes among White, Black, and Hispanic patients: do disparities exist in the outpatient setting?

BACKGROUND: In the United States, efforts to improve efficiency and reduce healthcare costs are shifting more total shoulder arthroplasty (TSA) surgeries to the outpatient setting. However, whether racial and ethnic disparities in access to high-quality outpatient TSA care exist remains to be elucidated. The purpose of this study was to assess racial/ethnic differences in relative outpatient TSA utilization and perioperative outcomes using a large national surgical database. METHODS: White, Black, and Hispanic patients who underwent TSA between 2017 and 2021 were identified from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database. Baseline demographic and clinical characteristics were collected, and rates of outpatient utilization, adverse events, readmission, reoperation, nonhome discharge, and mortality within 30 days of surgery were compared between racial/ethnic groups. Race/ethnicity-specific trends in utilization of outpatient TSA were assessed, and multivariable logistic regression was used to adjust for baseline demographic factors and comorbidities. RESULTS: A total of 21,186 patients were included, consisting of 19,135 (90.3%) White, 1093 (5.2%) Black, and 958 (4.5%) Hispanic patients and representing 17,649 (83.3%) inpatient and 3537 (16.7%) outpatient procedures. Black and Hispanic patients were generally younger and less healthy than White patients, yet incidences of complications, nonhome discharge, readmission, reoperation, and death within 30 days were similar across groups following outpatient TSA (P > .050 for all). Relative utilization of outpatient TSA increased by 28.7% among White patients, 29.5% among Black patients, and 38.6% among Hispanic patients (ptrend<0.001 for all). Hispanic patients were 64% more likely than White patients to undergo TSA as an outpatient procedure across the study period (OR: 1.64, 95% CI 1.40-1.92, P < .001), whereas odds did not differ between Black and White patients (OR: 1.04, 95% CI 0.87-1.23, P = .673). CONCLUSION: Relative utilization of outpatient TSA remains highest among Hispanic patients but has been significantly increasing across all racial and ethnic groups, now accounting for more than one-third of all TSA procedures. Considering outpatient TSA is associated with fewer complications and lower costs, increasing utilization may represent a promising avenue for reducing disparities in orthopedic shoulder surgery.

National Patterns in Utilization of Knee and Hip Arthroscopy: An Analysis of Racial, Ethnic, and Geographic Disparities in the United States.

Background: Racial and ethnic disparities in the field of orthopaedic surgery have been reported extensively across many subspecialties. However, these data remain relatively sparse in orthopaedic sports medicine, especially with respect to commonly performed procedures including knee and hip arthroscopy. Purpose: To assess (1) differences in utilization of knee and hip arthroscopy between White, Black, Hispanic, and Asian or Pacific Islander patients in the United States (US) and (2) how these differences vary by geographical region. Study Design: Descriptive epidemiology study. Methods: The study sample was acquired from the 2019 National Ambulatory Surgery Sample database. Racial and ethnic differences in age-standardized utilization rates of hip and knee arthroscopy were calculated using survey weights and population estimates from US census data. Poisson regression was used to model age-standardized utilization rates for hip and knee arthroscopy while controlling for several demographic and clinical variables. Results: During the study period, rates of knee arthroscopy utilization among White patients were significantly higher than those of Black, Hispanic, and Asian or Pacific Islander patients (ie, per 100,000, White: 180.5, Black: 113.2, Hispanic: 122.2, and Asian: 58.6). Disparities were even more pronounced among patients undergoing hip arthroscopy, with White patients receiving the procedure at almost 4 to 5 times higher rates (ie, per 100,000, White: 12.6, Black: 3.2, Hispanic: 2.3, Asian or Pacific Islander: 1.8). Disparities in knee and hip arthroscopy utilization between White and non-White patients varied significantly by region, with gaps in knee arthroscopy being most pronounced in the Midwest (adjusted rate ratio, 2.0 [95% CI, 1.9-2.1]) and those in hip arthroscopy being greatest in the West (adjusted rate ratio, 5.3 [95% CI, 4.9-5.6]). Conclusion: Racial and ethnic disparities in the use of knee and hip arthroscopy were found across the US, with decreased rates among Black, Hispanic, and Asian or Pacific Islander patients compared with White patients. Disparities were most pronounced in the Midwest and South and greater for hip than knee arthroscopy, possibly demonstrating emerging inequality in a rapidly growing and evolving procedure across the country.

Risk Factors for Graft Failure After Meniscal Allograft Transplantation: A Systematic Review and Meta-analysis.

Background: Graft failure after meniscal allograft transplantation (MAT) may necessitate revision surgery or conversion to arthroplasty. A comprehensive understanding of the risk factors for failure after MAT of the knee may facilitate more informed shared decision-making discussions before surgery and help determine whether MAT should be performed based on patient risk. Purpose: To perform a systematic review and meta-analysis of risk factors associated with graft failure after MAT of the knee. Study Design: Systematic review; Level of evidence, 4. Methods: The PubMed, OVID/Medline, and Cochrane databases were queried in October 2021. Data pertaining to study characteristics and risk factors associated with failure after MAT were recorded. DerSimonian-Laird binary random-effects models were constructed to quantitatively evaluate the association between risk factors and MAT graft failure by generating effect estimates in the form of odds ratios (ORs) with 95% CIs. Qualitative analysis was performed to describe risk factors that were variably reported. Results: In total, 17 studies including 2184 patients were included. The overall pooled prevalence of failure at the latest follow-up was 17.8% (range, 3.3%-81.0%). In 10 studies reporting 5-year failure rates, the pooled prevalence of failure was 10.9% (range, 4.7%-23%). In 4 studies reporting 10-year failure rates, the pooled prevalence was 22.7% (range, 8.1%-55.0%). A total of 39 risk factors were identified, although raw data presented in a manner amenable to meta-analysis only allowed for 3 to be explored quantitatively. There was strong evidence to support that an International Cartilage Regeneration & Joint Preservation Society grade >3a (OR, 5.32; 95% CI, 2.75-10.31; P < .001) was a significant risk factor for failure after MAT. There was no statistically significant evidence to incontrovertibly support that patient sex (OR, 2.16; 95% CI, 0.83-5.64; P = .12) or MAT laterality (OR, 1.11; 95% CI, 0.38-3.28; P = .85) was associated with increased risk of failure after MAT. Conclusion: Based on the studies reviewed, there was strong evidence to suggest that degree of cartilage damage at the time of MAT is associated with graft failure; however, the evidence was inconclusive on whether laterality or patient sex is associated with graft failure.

Challenges and Opportunities for the Use of Patient-Reported Outcome Measures in Orthopaedic Pediatric and Sports Medicine Surgery.

Patient-reported outcome measures (PROMs) are essential tools in assessing treatment response, informing clinical decision making, driving healthcare policy, and providing important prognostic data regarding patient health status change. These tools become essential in orthopaedic disciplines, such as pediatrics and sports medicine, given the diversity of patient populations and procedures. However, the creation and routine administration of standard PROMs alone do not suffice to appropriately facilitate the aforementioned functions. Indeed, both the interpretation and optimal application of PROMs are essential to provide to achieve greatest clinical benefit. Contemporary developments and technologies surrounding PROMs may help augment this benefit, including the application of artificial intelligence, novel PROM structure with improved interpretability and validity, and PROM delivery methods that provide increased access to patients resulting in greater compliance and data acquisition yields. Despite these exciting innovations, several challenges remain in this realm that must be addressed to continue to advance the clinical usefulness and subsequent benefit of PROMs. This review will highlight the opportunities and challenges surrounding contemporary PROM use in the orthopaedic subspecialties of pediatrics and sports medicine.

A guide to appropriately planning and conducting meta-analyses: part 2-effect size estimation, heterogeneity and analytic approaches.

Meta-analyses by definition are a subtype of systematic review intended to quantitatively assess the strength of evidence present on an intervention or treatment. Such analyses may use individual-level data or aggregate data to produce a point estimate of an effect, also known as the combined effect, and measure precision of the calculated estimate. The current article will review several important considerations during the analytic phase of a meta-analysis, including selection of effect estimators, heterogeneity and various sub-types of meta-analytic approaches.

Evaluating the Need for Preoperative MRI Before Primary Hip Arthroscopy in Patients 40 Years and Younger With Femoroacetabular Impingement Syndrome: A Multicenter Comparative Analysis.

Background: Routine hip magnetic resonance imaging (MRI) before arthroscopy for patients with femoroacetabular impingement syndrome (FAIS) offers questionable clinical benefit, delays surgery, and wastes resources. Purpose: To assess the clinical utility of preoperative hip MRI for patients aged ≤40 years who were undergoing primary hip arthroscopy and who had a history, physical examination findings, and radiographs concordant with FAIS. Study Design: Cohort study; Level of evidence, 3. Methods: Included were 1391 patients (mean age, 25.8 years; 63% female; mean body mass index, 25.6) who underwent hip arthroscopy between August 2015 and December 2021 by 1 of 4 fellowship-trained hip surgeons from 4 referral centers. Inclusion criteria were FAIS, primary surgery, and age ≤40 years. Exclusion criteria were MRI contraindication, reattempt of nonoperative management, and concomitant periacetabular osteotomy. Patients were stratified into those who were evaluated with preoperative MRI versus those without MRI. Those without MRI received an MRI before surgery without deviation from the established surgical plan. All preoperative MRI scans were compared with the office evaluation and intraoperative findings to assess agreement. Time from office to arthroscopy and/or MRI was recorded. MRI costs were calculated. Results: Of the study patients, 322 were not evaluated with MRI and 1069 were. MRI did not alter surgical or interoperative plans. Both groups had MRI findings demonstrating anterosuperior labral tears treated intraoperatively (99.8% repair, 0.2% debridement, and 0% reconstruction). Compared with patients who were evaluated with MRI and waited 63.0 ± 34.6 days, patients who were not evaluated with MRI underwent surgery 6.5 ± 18.7 days after preoperative MRI. MRI delayed surgery by 24.0 ± 5.3 days and cost a mean $2262 per patient. Conclusion: Preoperative MRI did not alter indications for primary hip arthroscopy in patients aged ≤40 years with a history, physical examination findings, and radiographs concordant with FAIS. Rather, MRI delayed surgery and wasted resources. Routine hip MRI acquisition for the younger population with primary FAIS with a typical presentation should be challenged.

A practical guide to the development and deployment of deep learning models for the Orthopedic surgeon: part I.

Deep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of what is necessary to design and implement deep learning-based project pipelines. This review provides a practical guide for the Orthopedic surgeon to understand the steps needed to design, develop, and deploy a deep learning pipeline for clinical applications. A detailed description of the processes involved in defining the problem, building the team, acquiring and curating the data, labeling the data, establishing the ground truth, pre-processing and augmenting the data, and selecting the required hardware is provided. In addition, an overview of unique considerations involved in the training and evaluation of deep learning models is provided. This review strives to provide surgeons with the groundwork needed to identify gaps in the clinical landscape that deep learning models may be able to fill and equips them with the knowledge needed to lead an interdisciplinary team through the process of creating novel deep-learning-based solutions to fill those gaps.

Hip Arthroscopy With Bone Marrow Aspirate Injection for Patients With Symptomatic Labral Tears and Early Degenerative Changes Shows Similar Improvement Compared With Patients Undergoing Hip Arthroscopy With Symptomatic Labral Tears Without Arthritis.

PURPOSE: To define the clinical effect of intra-articular injection of iliac crest-derived bone marrow aspirate concentrate (BMAC) at the time of hip arthroscopy in patients with symptomatic labral tears and early radiographic degenerative changes. METHODS: A retrospective review of a prospectively collected hip registry database was performed. Patients with symptomatic labral tears and Tönnis grade 1 or 2 degenerative changes who underwent labrum-preserving hip arthroscopy with BMAC injection were included and were matched with patients who underwent hip arthroscopy without BMAC injection. Patient-reported outcomes (PROs) collected preoperatively and up to 2 years postoperatively included the modified Harris Hip Score, Hip Outcome Score-Activities of Daily Living, Hip Outcome Score-Sport, and International Hip Outcome Tool 33 score. Clinical relevance was measured with the minimal clinically important difference, patient acceptable symptom state, and substantial clinical benefit for each outcome score. RESULTS: A total of 35 patients underwent labrum-preserving hip arthroscopy with BMAC injection and were matched with 35 control patients. There were no differences in demographic characteristics between the groups (P > .05). The BMAC group consisted of 22 patients (62.9%) with Tönnis grade 1 changes and 13 (37.1%) with Tönnis grade 2 changes, whereas all 35 control patients had Tönnis grade 0 hips. All PROs were significantly improved in both groups at 2 years, with no difference in improvement. The rate of failure requiring conversion to total hip arthroplasty was 14.3% (mean, 1.6 years postoperatively) in the BMAC group and 5.7% (mean, 7 years postoperatively) in the control group (P = .09). The difference in the frequency of patients achieving the minimal clinically important difference, patient acceptable symptom state, and substantial clinical benefit was not statistically significant between cohorts. CONCLUSIONS: In a challenging group of patients with symptomatic labral tears and early radiographic degenerative changes, hip arthroscopy with BMAC injection results in statistically and clinically significant improvement in PROs comparable to a group of patients with nonarthritic hips undergoing hip arthroscopy at short-term follow-up. LEVEL OF EVIDENCE: Level III, retrospective comparative therapeutic trial.

Supervised machine learning and associated algorithms: applications in orthopedic surgery.

Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on "big data" develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.

Choosing the appropriate measure of central tendency: mean, median, or mode?

Mean, median, and mode are among the most basic and consistently used measures of central tendency in statistical analysis and are crucial for simplifying data sets to a single value. However, there is a lack of understanding of when to use each metric and how various factors can impact these values. The aim of this article is to clarify some of the confusion related to each measure and explain how to select the appropriate metric for a given data set. The authors present this work as an educational resource, ensuring that these common statistical concepts are better understood throughout the Orthopedic research community.

Multivariable regression: understanding one of medicine's most fundamental statistical tools.

Multivariable regression is a fundamental tool that drives observational research in orthopaedic surgery. However, regression analyses are not always implemented correctly. This study presents a basic overview of regression analyses and reviews frequent points of confusion. Topics include linear, logistic, and time-to-event regressions, causal inference, confounders, overfitting, missing data, multicollinearity, interactions, and key differences between multivariable versus multivariate regression. The goal is to provide clarity regarding the use and interpretation of multivariable analyses for those attempting to increase their statistical literacy in orthopaedic research.

Natural language processing: using artificial intelligence to understand human language in orthopedics.

Natural language processing (NLP) describes the broad field of artificial intelligence by which computers are trained to understand and generate human language. Within healthcare research, NLP is commonly used for variable extraction and classification/cohort identification tasks. While these tools are becoming increasingly popular and available as both open-source and commercial products, there is a paucity of the literature within the orthopedic space describing the key tasks within these powerful pipelines. Curation and navigation of the electronic medical record are becoming increasingly onerous, and it is important for physicians and other healthcare professionals to understand potential methods of harnessing this large data resource. The purpose of this study is to provide an overview of the tasks required to develop an NLP pipeline for orthopedic research and present recent examples of successful implementations.

Unsupervised machine learning methods and emerging applications in healthcare.

Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensional data and can help simplify complex datasets. This article provides an overview of key unsupervised machine learning techniques including K-means clustering, hierarchical clustering, principal component analysis, and factor analysis. With a deeper understanding of these analytical tools, unsupervised machine learning methods can be incorporated into health sciences research to identify novel risk factors, improve prevention strategies, and facilitate delivery of personalized therapies and targeted patient care.Level of evidence: I.

Risk Factors for Failure After Osteochondral Allograft Transplantation of the Knee: A Systematic Review and Exploratory Meta-analysis.

BACKGROUND: Graft failure after osteochondral allograft transplantation (OCA) of the knee is a devastating outcome, often necessitating subsequent interventions. A comprehensive understanding of the risk factors for failure after OCA of the knee may provide enhanced prognostic data for the knee surgeon and facilitate more informed shared decision-making discussions before surgery. PURPOSE: To perform a systematic review and meta-analysis of risk factors associated with graft failure after OCA of the knee. STUDY DESIGN: Systematic review and meta-analysis; Level of evidence, 4. METHODS: The PubMed, Ovid/MEDLINE, and Cochrane databases were queried in April 2021. Data pertaining to study characteristics and risk factors associated with failure after OCA were recorded. DerSimonian-Laird binary random-effects models were constructed to quantitatively evaluate the association between risk factors and graft failure by generating effect estimates in the form of odds ratios (ORs) with 95% CIs, while mean differences (MDs) were calculated for continuous data. Qualitative analysis was performed to describe risk factors that were variably reported. RESULTS: A total of 16 studies consisting of 1401 patients were included. The overall pooled prevalence of failure was 18.9% (range, 10%-46%). There were 44 risk factors identified, of which 9 were explored quantitatively. There was strong evidence to support that the presence of bipolar chondral defects (OR, 4.20 [95% CI, 1.17-15.08]; P = .028) and male sex (OR, 2.04 [95% CI, 1.17-3.55]; P = .012) were significant risk factors for failure after OCA. Older age (MD, 5.06 years [95% CI, 1.44-8.70]; P = .006) and greater body mass index (MD, 1.75 kg/m2 [95% CI, 0.48-3.03]; P = .007) at the time of surgery were also significant risk factors for failure after OCA. There was no statistically significant evidence to incontrovertibly support that concomitant procedures, chondral defect size, and defect location were associated with an increased risk of failure after OCA. CONCLUSION: Bipolar chondral defects, male sex, older age, and greater body mass index were significantly associated with an increased failure rate after OCA of the knee. No statistically significant evidence presently exists to support that chondral defect size and location or concomitant procedures are associated with an increased graft failure rate after OCA of the knee. Additional studies are needed to evaluate these associations.

Fragility Part I: a guide to understanding statistical power.

The aim of this paper is to close the knowledge-to-practice gap around statistical power. We demonstrate how four factors affect power: p value, effect size, sample size, and variance. This article further delves into the advantages and disadvantages of a priori versus post hoc power analyses, though we believe only understanding of the former is essential to addressing the present-day issue of reproducibility in research. Upon reading this paper, physician-scientists should have expanded their arsenal of statistical tools and have the necessary context to understand statistical fragility.

Editorial Commentary: Clinically Depressed Patients Show Worse Outcomes After Anterior Cruciate Ligament Reconstruction Than Situationally Depressed Patients and Patients Without Mood Issues.

The impact of psychological factors on outcome after orthopaedic surgery is increasingly well reported. Specific to anterior cruciate ligament reconstruction, prior authors have reported that psychological factors influence return-to-sport outcome. Recent research shows that there is a difference between clinical depression and situational depression. Situationally depressed patients score low on psychological screens but do not have a pre-injury clinical diagnosis for depression. Clinically depressed patients score worse on outcome measures after anterior cruciate ligament reconstruction than situationally depressed patients and patients without any mood issues. Furthermore 90.5% of situationally depressed patients had postoperative resolution of their depressive symptoms. Although there appears to be a strong link between clinical depression and limited postoperative outcome, it is still unclear whether depression is a modifier of outcome or whether poor outcome is predictive of persistent depression.

The development and deployment of machine learning models.

Applications of artificial intelligence, specifically machine learning, are becoming increasingly popular in Orthopaedic Surgery, and medicine as a whole. This growing interest is shared by data scientists and physicians alike. However, there is an asymmetry of understanding of the developmental process and potential applications of machine learning. As new technology will undoubtedly affect clinical practice in the coming years, it is important for physicians to understand how these processes work. The purpose of this paper is to provide clarity and a general framework for building and assessing machine learning models.

External Validation of a Machine Learning Algorithm for Predicting Clinically Meaningful Functional Improvement After Arthroscopic Hip Preservation Surgery.

BACKGROUND: Individualized risk prediction has become possible with machine learning (ML), which may have important implications in enhancing clinical decision making. We previously developed an ML algorithm to predict propensity for clinically meaningful outcome improvement after hip arthroscopy for femoroacetabular impingement syndrome. External validity of prognostic models is critical to determine generalizability, although it is rarely performed. PURPOSE: To assess the external validity of an ML algorithm for predicting clinically meaningful improvement after hip arthroscopy. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: An independent hip preservation registry at a tertiary academic medical center was queried for consecutive patients/athletes who underwent hip arthroscopy for femoroacetabular impingement syndrome between 2015 and 2017. By assuming a minimal clinically important difference (MCID) outcome/event proportion of 75% based on the original study, a minimum sample of 132 patients was required. In total, 154 patients were included. Age, body mass index, alpha angle on anteroposterior pelvic radiographs, Tönnis grade and angle, and preoperative Hip Outcome Score-Sports Subscale were used as model inputs to predict the MCID for the Hip Outcome Score-Sports Subscale 2 years postoperatively. Performance was assessed using identical metrics to the internal validation study and included discrimination, calibration, Brier score, and decision curve analysis. RESULTS: The concordance statistic in the validation cohort was 0.80 (95% CI, 0.71 to 0.87), suggesting good to excellent discrimination. The calibration slope was 1.16 (95% CI, 0.74 to 1.61) and the calibration intercept 0.13 (95% CI, -0.26 to 0.53). The Brier score was 0.15 (95% CI, 0.12 to 0.18). The null model Brier score was 0.20. Decision curve analysis revealed favorable net treatment benefit for patients with use of the algorithm as compared with interventional changes made for all and no patients. CONCLUSION: The performance of this algorithm in an independent patient population in the northeast region of the United States demonstrated superior discrimination and comparable calibration to that of the derivation cohort. The external validation of this algorithm suggests that it is a reliable method to predict propensity for clinically meaningful improvement after hip arthroscopy and is an essential step forward toward introducing initial use in clinical practice. Potential uses include integration into electronic medical records for automated prediction, enhanced shared decision making, and more informed allocation of resources to optimize patient outcomes.