Race and ethnic disparities arthroplasty trends and hotspots: Bibliometric analysis.

Introduction: Despite continual advancements in total joint arthroplasty and perioperative optimization, there remains national variability in outcomes. These outcome variabilities have been in part attributed to racial and ethnic disparities in healthcare quality and access to care. This study aims to identify arthroplasty racial and ethnic disparities research and to predict future hotspots. Methods: Ethnic and racial disparities articles between 1992 and 2022 were queried from the Web of Science Core Collection of Clarivate Analytics. Bibliometric indicators in excel format were extracted and subsequently imported for further analysis. Bibliometrix and VOSviewer analyzed current and previous research. Results: Database search yielded 234 total articles assessing racial and ethnic disparities between 1992 and 2022. Twenty-six countries published manuscripts with the United States producing the majority of publications. The Veterans Health Administration and University of Pittsburgh were the most relevant institutions. Ibrahim SA was the most relevant and influential author within this field. Visuals of thematic map and co-occurrences identified the basic, motor, and niche themes within the literature. Conclusions: Racial and ethnic disparity within arthroplasty literature demonstrate growing traction with global contributions. United States authors and institutions are the largest contributors within this field. This bibliometric analysis identified previous, current, and future trends for prediction of future hotspots.

The Impact of Financial Education on Stress Among Orthopaedic Surgeons.

BACKGROUND: Financial stress has been an increasing area of concern for residents and attendings. The primary goal of this study was to determine the financial education level and differentiate financial outcome measures of orthopaedic surgery residents and attendings. METHODS: A survey of all residents and attendings of the 201 Accreditation Council for Graduate Medical Education (ACGME)-accredited orthopaedic surgery programs in the United States. RESULTS: Total participation in the study was 118 residents (postgraduate year (PGY) 1-5), three fellows (PGY 6), and 57 attending orthopaedic surgeons. A significant difference existed between average current financial stress scores between residents versus attending (2.32 vs 1.17), but not Doctor of Medicine (MD) versus Doctor of Osteopathic Medicine (DO) attendings (0.96 vs 1.67) and MD versus DO residents (2.25 vs 2.50). There was a significant difference in average future financial stress scores between residents and attendings (1.85 vs 1.44) and MD vs DO residents (1.61 vs 2.25) but no difference between MD vs DO attending (1.31 vs 1.63). Residents' confidence in financial knowledge compared to college graduates had a significantly negative Pearson coefficient with current financial stress score, while the attending group was not significant. CONCLUSIONS: Orthopaedic residents and attending physicians' financial stress levels are positively correlated with the amount of student debt they hold. Most residents who currently have no personal finance education offered in their residency would likely attend a personal finance course if offered. Decreasing the amount of debt held by residents, increasing their financial knowledge, and helping them develop good financial habits would likely lead to a decrease in financial stress.

Perioperative Nutritional, Functional, and Bone Health Optimization in Spine Surgery: A National Investigation of Spine Surgeons' Perceptions and Practices.

INTRODUCTION: While perioperative nutritional, functional, and bone health status optimization in spine surgery is supported with ample evidence, the implementation and surgeon perception regarding such efforts in clinical practice remain largely unexplored. This study sought to assess the current perception of spine surgeons and implementation regarding the nutritional, functional status, and bone health perioperative optimization. METHODS: An anonymous 30-question survey was distributed to orthopaedic spine fellowship and neurosurgery program directors identified through the North American Spine Society and American Association of Neurological Surgeons contact databases. RESULTS: The questionnaire was completed by 51 surgeon survey respondents. Among those, 62% reported no current formal nutritional optimization protocols with 14% not recommending an optimization plan, despite only 10% doubting benefits of nutritional optimization. While 5% of respondents perceived functional status optimization as nonbeneficial, 68% of respondents reported no protocol in place and 46% noted a functional status assessment relying on patient dependency. Among the respondents, 85% routinely ordered DEXA scan if there was suspicion of osteoporosis and 85% usually rescheduled surgery if bone health optimization goals were not achieved while 6% reported being suspicious of benefit from such interventions. CONCLUSION: While most responding spine surgeons believe in the benefit of perioperative nutritional and functional optimization, logistical and patient compliance challenges were noted as critical barriers toward optimization. Understanding surgeon perception and current practices may guide future efforts toward advancement of optimization protocols.

Bibliometric analysis of machine learning trends and hotspots in arthroplasty literature over 31 years.

Background: Artificial intelligence has demonstrated utility in orthopedic research. Algorithmic models derived from machine learning have demonstrated adaptive learning with predictive application towards outcomes, leading to increased traction in the literature. This study aims to identify machine learning arthroplasty research trends and anticipate emerging key terms. Methods: Published literature focused on machine learning in arthroplasty from 1992 to 2023 was selected through the Web of Science Core Collection of Clarivate Analytics. Following that, bibliometric indicators were attained and brought in to perform an additional examination using Bibliometrix and VOSviewer to identify historical and present patterns within the literature. Results: A total of 235 documents were obtained through bibliometric sourcing based on machine learning applications within the arthroplasty literature. Thirty-four countries published articles on the topic, and the United States was demonstrated to be the largest global contributor. Four hundred-five institutions internationally contributed articles, with Harvard Medical School and the University of California system as the most relevant institutes, with 75 and 44 articles produced, respectively. Kwon YM was the most productive author, while Haeberle HS and Ramkumar PN were the most impactful based on h-index. The Thematic map and Co-occurrence visualization helped identify both major and niche themes present in the scientific databases. Conclusions: Machine learning in arthroplasty research continues to gain traction with a growing annual production rate and contributions from international authors and institutions. Institutions and authors based in the United States are the leading contributors to machine learning applications within arthroplasty research. This research discerns trends that have occurred, are presently ongoing, and are emerging within this field, aiming to inform future hotspot development.

The utilization of US-based large data in arthroplasty research: Bibiliometric analysis of trends and hotspots.

Background: The accessibility of digital information has expanded orthopaedic surgery with expanded role of Big Databases. The increasing interest have led to creation of large databases with increasing utilization in retrospective studies. The aim of this study is to identify Big Database research and predict future hotspots. Methods: Big Database publications between 1982 and 2022 were identified from the Web of Science Core Collection of Clarivate Analytics. Bibliometric indicators were obtained and imported for further analysis with VOSviewer and Bibliometrix to identify previous and ongoing trends within this field. Results: Bibliometric sourcing identified 811 total articles that was associated with major databases. Twenty-eight countries published manuscript in the field with the United States as the largest contributor. The most relevant institutions were Cleveland Clinic and Harvard University. Mont MA was the most productive and influential author. Co-occurrence visualization and thematic map identified niche and major themes within the literature. Conclusions: Large Database research continue to show an increasing trend since 2011 with contributions globally. United States institutions and authors are the leading contributors in big database research. This study identifies previous, current, and developing trends within this field for future hotspot development.

Assessing the predictive capacity of machine learning models using patient-specific variables in determining in-hospital outcomes after THA.

Background: Machine learning is a subset of artificial intelligence using algorithmic modeling to progressively learn and create predictive models. Clinical application of machine learning can aid physicians through identification of risk factors and implications of predicted patient outcomes. Aims: The aim of this study was to compare patient-specific and situation perioperative variables through optimized machine learning models to predict postoperative outcomes. Methods: Data from 2016 to 2017 from the National Inpatient Sample was used to identify 177,442 discharges undergoing primary total hip arthroplasty, which were included in the training, testing, and validation of 10 machine learning models. 15 predictive variables consisting of 8 patient-specific and 7 situational specific variables were utilized to predict 3 outcome variables: length of stay, discharge, and mortality. The machine learning models were assessed in responsiveness via area under the curve and reliability. Results: For all outcomes, Linear Support Vector Machine had the highest responsiveness among all models when using all variables. When utilizing patient-specific variables only, responsiveness of the top 3 models ranged between 0.639 and 0.717 for length of stay, 0.703-0.786 for discharge disposition, and 0.887-0.952 for mortality. The top 3 models utilizing situational variables only produced responsiveness of 0.552-0.589 for length of stay, 0.543-0.574 for discharge disposition, and 0.469-0.536 for mortality. Conclusions: Linear Support Vector Machine was the most responsive machine learning model of the 10 algorithms trained, while decision list was most reliable. Responsiveness was observed to be consistently higher with patient-specific variables than situational variables, emphasizing the predictive capacity and value of patient-specific variables. The current practice in machine learning literature generally deploys a single model, it is suboptimal to develop optimized models for application into clinical practice. The limitation of other algorithms may prohibit potential more reliable and responsive models.Level of Evidence III.

Epidemiology of primary and revision total knee arthroplasty: analysis of demographics, comorbidities and outcomes from the national inpatient sample.

INTRODUCTION: Primary total knee arthroplasty (TKA) is a preferred treatment for end-stage knee osteoarthritis. In the setting of a failed TKA, revision total knee arthroplasty (rTKA) acts as a salvage procedure and carries a higher risk compared to primary TKA. Given increased interest in postoperative outcomes from these procedures, a thorough understanding of the demographics, comorbidities, and inpatient outcomes is warranted. This study aimed to report the epidemiological data of demographics, comorbidity profiles and outcomes of patients undergoing TKA and rTKA. METHODS: A retrospective review of NIS registry discharge data from 2006 to 2015 third quarter was performed. This study included adults aged 40 and older who underwent TKA or rTKA. A total of 5,901,057 TKA patients and 465,968 rTKA patients were included in this study. Simple descriptive statistics were used to present variables on demographics, medical comorbidities, and postoperative complications. RESULTS: A total of 5,901,057 TKA and 465,968 rTKA discharges were included in this study, with an average age of 66.30 and 66.56 years, and the major payor being Medicare, accounting for 55.34% and 59.88% of TKA and rTKA cases, respectively. Infection (24.62%) was the most frequent reason for rTKA, and was followed by mechanical complications (18.62%) and dislocation (7.67%). The most common medical comorbidities for both groups were hypertension, obesity, and diabetes. All types of inpatient complications were reported in 22.21% TKA and 28.78% of rTKA cases. Postoperative anemia was the most common complication in both groups (20.34% vs. 25.05%). CONCLUSIONS: Our data demonstrated a 41.9% increase in patients receiving TKA and 28.8% increase in rTKA from the years 2006 to 2014. The data showed a 22.21% and a 28.78% "complication" rate with TKA and rTKA, with postoperative anemia being the most common complication. The top 3 medical comorbidities were hypertension, obesity, and diabetes for both groups and with increased focus on perioperative optimization, future analyses into preoperative medical optimization, and improved primary arthroplasty protocol may result in improved postoperative outcomes.