Rising incidence of stair-related upper extremity fractures among older adults in the United States: a 10-year nationwide analysis.

Upper extremity (UE) fractures are prevalent age-related fractures, and stair-associated falls are a common mechanism for these injuries. Our study has identified an increasing incidence of stair-related UE fractures and associated hospitalization rates among the older United States population between 2012-2021. Targeted prevention efforts should be implemented by health systems. INTRODUCTION: To analyze United States (US) emergency department trends in upper extremity stair-related fractures among older adults and investigate risk factors associated with hospitalization. METHODS: We queried the National Electronic Injury Surveillance System (NEISS) for all stair-related fracture injuries between 2012 and 2021 among adults 65 years or older. The US Census Bureau International Database (IDB) was analyzed to calculate incidence rates. Descriptive analysis, linear regression analysis, and multivariate regression analysis were used to interpret the collected data. RESULTS: Our analysis estimated 251,041 (95% CI: 211,678-290,404) upper extremity stair-related fractures among older adults occurred between 2012 and 2021. The primary anatomical locations were the humeral shaft (27%), wrist (26%), and proximal humerus (18%). We found a 56% increase in injuries (R2 = 0.77, p < 0.001), 7% increase in incidence per 100,000 persons (R2 = 0.42, p < 0.05), and an 38% increase in hospitalization rate (R2 = 0.61, p < 0.01) during the 10-year study period. Women sustained the majority of fractures (76%) and most injuries occurred in homes (89%). Advanced age (p < 0.0001), males (p < 0.0001), proximal humerus fractures (p < 0.0001), humeral shaft fractures (p < 0.0001), and elbow fractures (p < 0.0001) were associated with increased odds of hospitalization after injury. CONCLUSION: Stair-related UE fracture injuries, incidence, and hospitalization rates among older adults are increasing significantly, particularly among older females. Improving bone health, optimizing functional muscle mass, and "fall-proofing" homes of older age groups may help mitigate the rising incidence of these injuries.

Sofa and bed-related pediatric trauma injuries treated in United States emergency departments.

INTRODUCTION: Children under the age of 5 years suffer from the highest rates of fall-related injuries. Caretakers often leave young children on sofas and beds, however, falling and rolling off these fixtures can lead to serious injury. We investigated the epidemiologic characteristics and trends of bed and sofa-related injuries among children aged <5 years treated in US emergency departments (EDs). METHODS: We conducted a retrospective analysis of data from the National Electronic Injury Surveillance System from 2007 through 2021 using sample weights to estimate national numbers and rates of bed and sofa-related injuries. Descriptive statistics and regression analyses were employed. RESULTS: An estimated 3,414,007 children aged <5 years were treated for bed and sofa-related injuries in emergency departments (EDs) in the United States from 2007 through 2021, averaging 115.2 injuries per 10,000 persons annually. Closed head injuries (30%) and lacerations (24%) comprised the majority of injuries. The primary location of injury was the head (71%) and upper extremity (17%). Children <1 year of age accounted for most injuries, with a 67% increase in incidence within the age group between 2007 and 2021 (p < 0.001). Falling, jumping, and rolling off beds and sofas were the primary mechanisms of injury. The proportion of jumping injuries increased with age. Approximately 4% of all injuries required hospitalization. Children <1 year of age were 1.58 times more likely to be hospitalized after injury than all other age groups (p < 0.001). CONCLUSION: Beds and sofas can be associated with injury among young children, especially infants. The annual rate of bed and sofa-related injuries among infants <1 year old is increasing, which underscores the need for increased prevention efforts, including parental education and improved safety design, to decrease these injuries.

Risk of hospital admission related to scooter trauma injuries: a national emergency room database study.

BACKGROUND: We investigated key risk factors for hospital admission related to powered scooters, which are modes of transportation with increasing accessibility across the United States (US). METHODS: We queried the National Electronic Injury Surveillance System (NEISS) for injuries related to powered scooters, obtaining US population projections of injuries and hospital admissions. We determined mechanism of injury, characterized injury types, and performed multivariate regression analyses to determine factors associated with hospital admission. RESULTS: One thousand one hundred ninety-one patients sustained electric-motorized scooter (e-scooter) injuries and 10.9% (131) required hospitalization from 2013 to 2018. This extrapolated to a US annual total of 862 (95% CI:745-979) scooter injuries requiring hospitalization, with estimated annual mortality of 6.7 patients per year (95% CI:4.8-8.5). The incidence of hospital admissions increased by an average of 13.1% each year of the study period. Fall (79 [60%]) and motor vehicle collision (33 [25%]) were the most common mechanism. Injury locations included head (44 [34%]), lower extremity (22 [17%]), and lower trunk (16 [12%]). On multivariable analysis, significant factors associated with admission included increased age (OR 1.02, 95% CI:1.01-1.02), torso injuries (OR 6.19, 2.93-13.10), concussion (25.45, 5.88-110.18), fractures (21.98, 7.13-67.66), musculoskeletal injury (6.65, 1.20-36.99), and collision with vehicle (3.343, 2.009-5.562). Scooter speed, seasonality, and gender were not associated with risk of hospitalization. CONCLUSION: Our findings show increased hospital admissions and mortality from powered scooter trauma, with fall and motor vehicle collisions as the most common mechanisms resulting in hospitalization. This calls for improved rider safety measures and regulation surrounding vehicular collision scenarios.

Identifying New Frontiers for Social Media Engagement in Global Surgery: An Observational Study.

BACKGROUND: The purpose of this observational study is to characterize the use of social media content pertaining to global surgery. METHODS: A search for public posts on social media related to global surgery was performed over a 3-month window, from January 1st, 2019, to March 31st, 2019. Two public domains were included in the search: Instagram and Twitter. Posts were selected by filtering for one hashtag: #GlobalSurgery. A binary scoring system was used for media format, perspective of the poster, timing of the post, tone, and post content. Data were analyzed using Chi-squared tests with significance set to p < 0.05. RESULTS: Overall, 2633 posts with the hashtag #GlobalSurgery were publicly shared on these two social media platforms over the 3-month period. Of these, 2272 (86.3%) referenced content related to global surgery and were original posts. Physicians and other health professionals authored a majority (60.5%, 1083/1788) of posts on Twitter, whereas organizations comprised a majority of the posts on Instagram (59.9%, 290/484). Posts either had a positive (50.2%, 1140/2272) or neutral (49.6%, 1126/2272) tone, with only 0.3% or 6/2272 of posts being explicitly negative. The content of the posts varied, but most frequently (43.4%, 986/2272) focused on promoting communication and engagement within the community, followed by educational content (21.3%, 486/2272), advertisements (18.8%, 427/2272), and published research (13.2%, 299/2272). The majority of global surgery posts originated from the USA, UK, or Canada (67.6%, 1537/2272), followed by international organizations (11.5%, 261/2272). Chi-squared analysis comparing Instagram with Twitter performed examining media content, tone, perspective, and content, finding statistically significant differences (p < 0.001) the two platforms for each of the categories. CONCLUSION: The online social media community with respect to global surgery engagement is predominantly composed of surgeons and health care professionals, focused primarily on promoting dialogue within the online community. Social media platforms may provide a scalable tool that can augment engagement between global surgeons, with remaining opportunity to foster global collaboration, community engagement, education and awareness.

Prognostic utility of an magnetic resonance imaging-based classification for operative versus nonoperative management of ulnar collateral ligament tears: one-year follow-up.

BACKGROUND: A recently introduced classification of medial ulnar collateral ligament (UCL) tears has demonstrated high interobserver and intraobserver reliability, but little is known about its prognostic utility. The purpose of this study was to assess the relationship of the magnetic resonance imaging (MRI)-based classification system and nonoperative vs. operative management. Secondary objectives included subanalysis of baseball players. METHODS: Eighty-five consecutive patients with UCL tears after a standardized treatment paradigm were categorized as operative vs. nonoperative. UCL tears of patients with a minimum of 1-year follow-up were retrospectively classified using the MRI-based classification system. Subanalyses for baseball players included return-to-play and return-to-prior performance. RESULTS: A total of 80 patients (62 baseball players, 54 pitchers) met inclusion criteria. A total of 51 patients underwent surgery, and 29 patients completed nonoperative management. In baseball players, 59% of the proximal tears were treated nonoperatively and 97% of the distal tears were treated operatively; 100% of the proximal partial-thickness tears and 100% of the distal complete tears were treated nonoperatively and operatively, respectively. Patients with distal (odds ratio: 48.4, P < .0001) and complete (odds ratio: 5.0, P = .004) tears were more likely to undergo surgery. Baseball players, regardless of position, were determinants of operative management, and there was no difference in return-to-play clearance and return-to-prior performance between the operative and nonoperative groups. CONCLUSION: A reliable 6-stage MRI-based classification addressing UCL tear grade and location may confer decision making between operative and nonoperative management. Complete and distal tears carry a markedly increased risk of failing nonoperative care compared with proximal, partial tears.

Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model.

BACKGROUND: The objective is to develop and validate an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition before primary total knee arthroplasty (TKA). The secondary objective applied the ANN to propose a risk-based, patient-specific payment model (PSPM) commensurate with case complexity. METHODS: Using data from 175,042 primary TKAs from the National Inpatient Sample and an institutional database, an ANN was developed to predict LOS, charges, and disposition using 15 preoperative variables. Outcome metrics included accuracy and area under the curve for a receiver operating characteristic curve. Model uncertainty was stratified by All Patient Refined comorbidity indices in establishing a risk-based PSPM. RESULTS: The dynamic model demonstrated "learning" in the first 30 training rounds with areas under the curve of 74.8%, 82.8%, and 76.1% for LOS, charges, and discharge disposition, respectively. The PSPM demonstrated that as patient comorbidity increased, risk increased by 2.0%, 21.8%, and 82.6% for moderate, major, and severe comorbidities, respectively. CONCLUSION: Our deep learning model demonstrated "learning" with acceptable validity, reliability, and responsiveness in predicting value metrics, offering the ability to preoperatively plan for TKA episodes of care. This model may be applied to a PSPM proposing tiered reimbursements reflecting case complexity.

Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model.

BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create a patient-specific payment model (PSPM) accounting for patient complexity. METHODS: Using 15 preoperative variables from 78,335 primary total hip arthroplasty cases for osteoarthritis from the National Inpatient Sample and our institutional database, an ANN was developed to predict LOS, charges, and disposition. Validity metrics included accuracy and area under the curve of the receiver operating characteristic curve. Predictive uncertainty was stratified by All Patient Refined comorbidity cohort to establish the PSPM. RESULTS: The dynamic model demonstrated "learning" in the first 30 training rounds with areas under the curve of 82.0%, 83.4%, and 79.4% for LOS, charges, and disposition, respectively. The proposed PSPM established a risk increase of 2.5%, 8.9%, and 17.3% for moderate, major, and severe comorbidities, respectively. CONCLUSION: The deep learning ANN demonstrated "learning" with good reliability, responsiveness, and validity in its prediction of value-centered outcomes. This model can be applied to implement a PSPM for tiered payments based on the complexity of the case.

Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models.

BACKGROUND: Value-based payment programs in orthopedics, specifically primary total hip arthroplasty (THA), present opportunities to apply forecasting machine learning techniques to adjust payment models to a specific patient or population. The objective of this study is to (1) develop and validate a machine learning algorithm using preoperative big data to predict length of stay (LOS) and patient-specific inpatient payments after primary THA and (2) propose a risk-adjusted patient-specific payment model (PSPM) that considers patient comorbidity. METHODS: Using an administrative database, we applied 122,334 patients undergoing primary THA for osteoarthritis between 2012 and 16 to a naïve Bayesian model trained to forecast LOS and payments. Performance was determined using area under the receiver operating characteristic curve and percent accuracy. Inpatient payments were grouped as <$12,000, $12,000-$24,000, and >$24,000. LOS was grouped as 1-2, 3-5, and 6+ days. Payment model uncertainty was applied to a proposed risk-based PSPM. RESULTS: The machine learning algorithm required age, race, gender, and comorbidity scores ("risk of illness" and "risk of morbidity") to demonstrate excellent validity, reliability, and responsiveness with an area under the receiver operating characteristic curve of 0.87 and 0.71 for LOS and payment. As patient complexity increased, error for predicting payment increased in tiers of 3%, 12%, and 32% for moderate, major, and extreme comorbidities, respectively. CONCLUSION: Our preliminary machine learning algorithm demonstrated excellent construct validity, reliability, and responsiveness predicting LOS and payment prior to primary THA. This has the potential to allow for a risk-based PSPM prior to elective THA that offers tiered reimbursement commensurate with case complexity. LEVEL OF EVIDENCE: III.

Predicting Inpatient Payments Prior to Lower Extremity Arthroplasty Using Deep Learning: Which Model Architecture Is Best?

BACKGROUND: Recent advances in machine learning have given rise to deep learning, which uses hierarchical layers to build models, offering the ability to advance value-based healthcare by better predicting patient outcomes and costs of a given treatment. The purpose of this study is to compare the performance of 2 common deep learning models, traditional multilayer perceptron (MLP), and the newer dense neural network (DenseNet), in predicting outcomes for primary total hip arthroplasty (THA) and total knee arthroplasty (TKA) as a foundation for future musculoskeletal studies seeking to utilize machine learning. METHODS: Using 295,605 patients undergoing primary THA and TKA from a New York State inpatient administrative database from 2009 to 2016, 2 neural network designs (MLP vs DenseNet) with different model regularization techniques (dropout, batch normalization, and DeCovLoss) were applied to compare model performance on predicting inpatient procedural cost using the area under the receiver operating characteristic curve (AUC). Models were implemented to identify high-cost surgical cases. RESULTS: DenseNet performed similarly to or better than MLP across the different regularization techniques in predicting procedural costs of THA and TKA. Applying regularization to DenseNet resulted in a significantly higher AUC as compared to DenseNet alone (0.813 vs 0.792, P = .011). When regularization methods were applied to MLP, the AUC was significantly lower than without regularization (0.621 vs 0.791, P = 1.1 × 10-15). When the optimal MLP and DenseNet models were compared in a head-to-head fashion, they performed similarly at cost prediction (P > .999). CONCLUSION: This study establishes that in predicting costs of lower extremity arthroplasty, DenseNet models improve in performance with regularization, whereas simple neural network models perform significantly worse without regularization. In light of the resource-intensive nature of creating and testing deep learning models for orthopedic surgery, particularly for value-centric procedures such as arthroplasty, this study establishes a set of key technical features that resulted in better prediction of inpatient surgical costs. We demonstrated that regularization is critically important for neural networks in arthroplasty cost prediction and that future studies should utilize these deep learning techniques to predict arthroplasty costs. LEVEL OF EVIDENCE: III.

Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review.

BACKGROUND: Driven by the rapid development of big data and processing power, artificial intelligence and machine learning (ML) applications are poised to expand orthopedic surgery frontiers. Lower extremity arthroplasty is uniquely positioned to most dramatically benefit from ML applications given its central role in alternative payment models and the value equation. METHODS: In this report, we discuss the origins and model specifics behind machine learning, consider its progression into healthcare, and present some of its most recent advances and applications in arthroplasty. RESULTS: A narrative review of artificial intelligence and ML developments is summarized with specific applications to lower extremity arthroplasty, with specific lessons learned from osteoarthritis gait models, joint-specific imaging analysis, and value-based payment models. CONCLUSION: The advancement and employment of ML provides an opportunity to provide data-driven, high performance medicine that can rapidly improve the science, economics, and delivery of lower extremity arthroplasty.

Remote Patient Monitoring Using Mobile Health for Total Knee Arthroplasty: Validation of a Wearable and Machine Learning-Based Surveillance Platform.

BACKGROUND: Recent technologic advances capable of measuring outcomes after total knee arthroplasty (TKA) are critical in quantifying value-based care. Traditionally accomplished through office assessments and surveys with variable follow-up, this strategy lacks continuous and complete data. The primary objective of this study was to validate the feasibility of a remote patient monitoring (RPM) system in terms of the frequency of data interruptions and patient acceptance. Second, we report pilot data for (1) mobility; (2) knee range of motion, (3) patient-reported outcome measures (PROMs); (4) opioid use; and (5) home exercise program (HEP) compliance. METHODS: A pilot cohort of 25 patients undergoing primary TKA for osteoarthritis was enrolled. Patients downloaded the RPM mobile application preoperatively to collect baseline activity and PROMs data, and the wearable knee sleeve was paired to the smartphone during admission. The following was collected up to 3 months postoperatively: mobility (step count), range of motion, PROMs, opioid consumption, and HEP compliance. Validation was determined by acquisition of continuous data and patient tolerance at semistructured interviews 3 months after operation. RESULTS: Of the 25 enrolled patients, 100% had uninterrupted passive data collection. Of the 22 available for follow-up interviews, all found the system motivating and engaging. Mean mobility returned to baseline within 6 weeks and exceeded preoperative baseline by 30% at 3 months. Mean knee flexion achieved was 119°, which did not differ from clinic measurements (P = .31). Mean KOOS improvement was 39.3 after 3 months (range: 3-60). Opioid use typically stopped by postoperative day 5. HEP compliance was 62% (range: 0%-99%). CONCLUSIONS: In this pilot study, we established the ability to remotely acquire continuous data for patients undergoing TKA, who found the application to be engaging. RPM offers the newfound ability to more completely evaluate the patients undergoing TKA in terms of mobility and rehabilitation compliance. Study with more patients is required to establish clinical significance.

Stratum-Specific Likelihood Ratio Analysis: An Evidence-Based and Pragmatic Approach to Meaningful Thresholds in Lower Extremity Arthroplasty.

BACKGROUND: With the transition toward a value-based care delivery model, an evidence-based approach to quantify the effect of procedural volume on outcomes and cost presents an opportunity to understand and optimize the delivery of lower extremity arthroplasty. Stratum-specific likelihood ratio (SSLR) analysis has been recently applied to define benchmarks which confer a significant advantage in value at the hospital or surgeon level. MATERIALS AND METHODS: In this report, the role, statistical technique, and future applications of SSLR analysis are described with an example outlined for total hip arthroplasty (THA). RESULTS: SSLR analysis provides multiple significant value-based thresholds, providing an advantage over previous methods used to describe the effects of surgeon and hospital volume. These benchmarks have been developed for THA, total knee arthroplasty (TKA), hip fracture, and several other orthopaedic procedures. Current SSLR analyses are limited by the databases employed, and the study of a national database may provide more generalizable benchmarks, which may be applied by hospitals and orthopaedic residencies to define minimum competency thresholds. CONCLUSION: The use of SSLR analysis provides a pragmatic, data-driven approach to understanding and communicating the volume-value relationship in orthopaedic surgery, particularly lower-extremity arthroplasty.

The Effects of Space Microgravity on Hip and Knee Cartilage: A New Frontier in Orthopaedics.

INTRODUCTION: Given the expansion of commercial and recreational space exploration, orthopaedic surgeons will need to understand the implications of microgravity on cartilaginous damage and to anticipate the resulting pathology from accelerated chondrolysis. The purpose of this systematic review is to evaluate the effects of space and microgravity on hip and knee articular cartilage, including its impact on joint mobility and functional status. MATERIALS AND METHODS: A review of the current literature was performed utilizing the terms "joints," "joint mobility," "articular cartilage," "knee," "hip," "space," "microgravity," and "osteoarthritis" in PubMed and Google Scholar from 1990 to 2018, yielding a total of 1,400 citations following the removal of 500 duplicates. Following screening by eligibility criteria, five reports were included. RESULTS: Dysregulation of osteogenesis and weakened structural integrity of hip and knee cartilage were demonstrated secondary to microgravity. Adequate cartilage repair requires Earth-like conditions as signified by a statistically significant increase in serum cartilage oligomeric matrix protein concentrations in astronauts. Reduced loading led to the degradation of knee ligaments and menisci which may pose a risk for subluxation or dislocation. Murine studies demonstrated decreased articular cartilage thickness in the medial femoral condyle and patella as assessed by ultrasound. Additionally, glycosaminoglycan levels in unloaded rats were lower than weight-bearing rats, with a concomitant increase in matrix metalloproteinase-13 protein, degrading collagen. Return to weight-bearing demonstrated partial recovery of cartilaginous degeneration. CONCLUSIONS: Space and associated microgravity conditions adversely impact articular cartilage as demonstrated in murine and human studies. The pathogenetic process occurs due to the mechanically responsive nature of cartilage, with an increase in cartilage metabolism in microgravity. There remains a marked paucity of literature regarding the gravitational force necessary for adequate cartilage survival and the impact of space-related radiation on cartilage repair. Additionally, further studies should assess pharmacologic interventions, such as recombinant human fibroblast growth factor to stimulate cartilaginous growth.

Social Media in Shoulder & Elbow Surgery: An Analysis of Twitter and Instagram.

Social media provide a unique method of analyzing outcomes and quality in medicine. The purpose of this observational study was to investigate the nature of social media content related to shoulder and elbow (S&E) surgery posted by patients, surgeons, and hospitals. A public search of Instagram for a two-year period yielded 1,177 patient-related posts. A categorical system assessed the perspective, timing, tone, and content of each post. Twitter accounts of 77 S&E specialists from the top five ranked U.S. News & World Report institutions were analyzed for activity and content. 5,246 Twitter and Instagram posts for the institutions were analyzed for frequency and content. Most patient-related posts were by patients (68%), postoperative (82%), positive (87%), and centered on return-to-play for Tommy John (34%), surgical site for shoulder arthroplasty (52%), and activities of daily living for rotator cuff repair (22%). 37% of surgeons had active accounts averaging 46 posts, 87% of which were practice advertisements. Hospitals averaged 273 posts over the 2-year period, focusing on education (38%) and community (18%). S&E patients share outcomes on social media in a positive tone with procedure-dependent emphases. Surgeons on social media use sites for practice augmentation. Hospitals often focused posts towards educating the community.

Mobile technology and telemedicine for shoulder range of motion: validation of a motion-based machine-learning software development kit.

BACKGROUND: Mobile technology offers the prospect of delivering high-value care with increased patient access and reduced costs. Advances in mobile health (mHealth) and telemedicine have been inhibited by the lack of interconnectivity between devices and software and inability to process consumer sensor data. The objective of this study was to preliminarily validate a motion-based machine learning software development kit (SDK) for the shoulder compared with a goniometer for 4 arcs of motion: (1) abduction, (2) forward flexion, (3) internal rotation, and (4) external rotation. METHODS: A mobile application for the SDK was developed and "taught" 4 arcs of shoulder motion. Ten subjects without shoulder pain or prior shoulder surgery performed the arcs of motion for 5 repetitions. Each motion was measured by the SDK and compared with a physician-measured manual goniometer measurement. Angular differences between SDK and goniometer measurements were compared with univariate and power analyses. RESULTS: The comparison between the SDK and goniometer measurement detected a mean difference of less than 5° for all arcs of motion (P > .05), with a 94% chance of detecting a large effect size from a priori power analysis. Mean differences for the arcs of motion were: abduction, -3.7° ± 3.2°; forward flexion, -4.9° ± 2.5°; internal rotation, -2.4° ± 3.7°; and external rotation -2.6° ± 3.4°. DISCUSSION: The SDK has the potential to remotely substitute for a shoulder range of motion examination within 5° of goniometer measurements. An open-source motion-based SDK that can learn complex movements, including clinical shoulder range of motion, from consumer sensors offers promise for the future of mHealth, particularly in telemonitoring before and after orthopedic surgery.

No Difference in Outcomes 12 and 24 Months After Lower Extremity Total Joint Arthroplasty: A Systematic Review and Meta-Analysis.

BACKGROUND: A better understanding of how patient-reported outcome measures (PROMs) change after total knee and hip arthroplasties (TKA and THA) is needed to support the minimum arbitrary follow-up of 24-months required by orthopedic journals. Therefore, our purpose was to perform a systematic review and meta-analysis of the THA and TKA literature to determine if equivalence exists between 12- and 24-month outcomes data. METHODS: A search was performed using the PubMed and EMBASE databases for primary and revision THA and TKA studies reporting PROMs data at both 12 and 24 months. Reports on PROMs for TKA and THAs were included for meta-analysis to detect statistical differences at 12 and 24 months. RESULTS: A total of 15 reports from 9 TKA (n = 1564) and 6 THA (n = 740) reports were analyzed. The mean change between 12 and 24 months for Knee Society Score was 0.15 absolute points (95% confidence interval [CI]: 0.97-1.06, P = .13) and for Western Ontario and McMaster Universities Osteoarthritis index was 0.50 absolute points (95% CI: 0.94-1.07, P = .49). The mean change between 12 and 24 months for Harris Hip Score was 2.01 absolute points (95% CI: 0.94-1.1, P = .22) and for short form was 0.02 absolute points (95% CI: 0.92-1.08, P = .94). CONCLUSION: No different outcomes were found within THA and TKA for 4 PROMs at 12- and 24-month follow-up. Although the findings from this study do not alleviate the need for collecting data from longer follow-up periods, there may not be additional value in collecting short-term outcomes data in routine practice at both 1 and 2 years.

Evidence-Based Thresholds for the Volume and Cost Relationship in Total Hip Arthroplasty: Outcomes and Economies of Scale.

BACKGROUND: High-volume surgeons and hospital systems have been shown to deliver higher-value care in several studies. However, no evidence-based volume thresholds for cost currently exist in total hip arthroplasty (THA). The objective of this study was to establish meaningful thresholds in cost for surgeons and hospitals performing THA. A secondary objective was to analyze the market share of THAs for each surgeon and hospital stratifications. METHODS: Using a database of 136,501 patients undergoing THA, we used stratum-specific likelihood ratio analysis of a receiver operating characteristic curve to generate volume thresholds based on costs for surgeons and hospitals. In addition, we examined the relative proportion of annual THA cases performed by each surgeon and hospital stratifications. RESULTS: Stratum-specific likelihood ratio analysis of cost by annual surgeon THA volume produced stratifications at: 0-73 (low), 74-123 (medium), and 124 or more (high). Analysis by annual hospital THA volume produced stratifications at: 0-121 (low), 122-309 (medium), and 310 or more (high). Hospital costs decreased significantly (P < .05) in progressively higher volume stratifications. High-volume centers perform the largest proportion of THA cases (48.6%); however, low volume surgeons perform the greatest share of these cases (44.6%). CONCLUSION: Our study establishes economies of scale in THA by demonstrating a direct relationship between volume and cost reduction. High-volume hospitals are performing the greatest proportion of THAs; however, low-volume surgeons perform the largest share of these cases, which highlights a potential area for enhanced value in the care of patients undergoing THA.

Evidence-Based Thresholds for the Volume and Length of Stay Relationship in Total Hip Arthroplasty: Outcomes and Economies of Scale.

BACKGROUND: Several studies have indicated that high-volume surgeons and hospitals deliver higher value care. However, no evidence-based volume thresholds currently exist in total hip arthroplasty (THA). The primary objective of this study was to establish meaningful thresholds taking patient outcomes into consideration for surgeons and hospitals performing THA. A secondary objective was to examine the market share of THAs for each surgeon and hospital strata. METHODS: Using 136,501 patients undergoing hip arthroplasty, we used stratum-specific likelihood ratio (SSLR) analysis of a receiver-operating characteristic curve to generate volume thresholds predictive of increased length of stay (LOS) for surgeons and hospitals. Additionally, we examined the relative proportion of annual THA cases performed by each surgeon and hospital strata established. RESULTS: SSLR analysis of LOS by annual surgeon THA volume produced 3 strata: 0-69 (low), 70-121 (medium), and 121 or more (high). Analysis by annual hospital THA volume produced strata at: 0-120 (low), 121-357 (medium), and 358 or more (high). LOS decreased significantly (P < .05) in progressively higher volume categories. High-volume hospitals performed the majority of cases, whereas low-volume surgeons performed the majority of THAs. CONCLUSION: Our study validates economies of scale in THA by demonstrating a direct relationship between volume and value for THA through risk-based volume stratification of surgeons and hospitals using SSLR analysis of receiver-operating characteristic curves to identify low-, medium-, and high-volume surgeons and hospitals. While the majority of primary THAs are performed at high-volume centers, low-volume surgeons are performing the majority of these cases, which may offer room for improvement in delivering value-based care.

Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model.

BACKGROUND: Value-based and patient-specific care represent 2 critical areas of focus that have yet to be fully reconciled by today's bundled care model. Using a predictive naïve Bayesian model, the objectives of this study were (1) to develop a machine-learning algorithm using preoperative big data to predict length of stay (LOS) and inpatient costs after primary total knee arthroplasty (TKA) and (2) to propose a tiered patient-specific payment model that reflects patient complexity for reimbursement. METHODS: Using 141,446 patients undergoing primary TKA from an administrative database from 2009 to 2016, a Bayesian model was created and trained to forecast LOS and cost. Algorithm performance was determined using the area under the receiver operating characteristic curve and the percent accuracy. A proposed risk-based patient-specific payment model was derived based on outputs. RESULTS: The machine-learning algorithm required age, race, gender, and comorbidity scores ("risk of illness" and "risk of morbidity") to demonstrate a high degree of validity with an area under the receiver operating characteristic curve of 0.7822 and 0.7382 for LOS and cost. As patient complexity increased, cost add-ons increased in tiers of 3%, 10%, and 15% for moderate, major, and extreme mortality risks, respectively. CONCLUSION: Our machine-learning algorithm derived from an administrative database demonstrated excellent validity in predicting LOS and costs before primary TKA and has broad value-based applications, including a risk-based patient-specific payment model.

Clinical Results of Direct Anterior Approach THA with Minimal Fluoroscopic Exposure Optimization Techniques.

PURPOSE: To determine if low-level intraoperative fluoroscopy usage is associated with increased complications during an initial series for an experienced surgeon transitioning to direct anterior approach (DAA) for total hip arthroplasty (THA). MATERIALS AND METHODS: Subjects who underwent DAA were eligible for analysis. Inclusion criteria included the first 50 subjects who underwent DA hip arthroplasty by a single surgeon (January 2013 to December 2014). Total operating room (OR) time, fluoroscopy absorbed dose, flouoroscopy time, procedure time, and complications were collected and analyzed. Subject demographics were also collected with subjects divided by date of surgery to one of two possible groups. Simple linear regression analysis was performed to determine the relation between case number and both radiation dose and fluoroscopy time. RESULTS: Subjects underwent DAA total hip arthroplasty (n=45). Total OR time ranged from 1.1hrs up to 2.5 hours. Surgeries required an average fluoroscopic time of 7.8 seconds, with improvement over the series of 3.7 seconds. The average radiation dose or fluoroscopy was 2.6 mrem per case. This resulted in a total estimated exposure of 127 mrem over a 23-month period. No patients suffered intraoperative or postoperative fractures or revisions. No significant difference was found for the groups by weight, age, height, and body mass index. Regression analysis yielded a statistically significant (p<0.05) decrease in fluoroscopy time of 0.36 seconds per case over the 45 cases studied. CONCLUSION: An experienced single surgeon's learning curve in DAA THA can be accelerated, with proper training and technique, within a lifetime case experience less than 50 procedures. Surgeons should be aware that with proper techniques and sufficiently-experienced teams, a flattened learning curve is attainable while minimizing fluoroscopy exposure and maintaining clinical outcomes.