One-Year Outcomes of the Anatomical Front and Back Reconstruction for Scapholunate Dissociation.

PURPOSE: Anatomical front and back (ANAFAB) reconstruction addresses the critical volar and dorsal ligaments associated with scapholunate dissociation. We hypothesized that patients with symptomatic, chronic, late-stage scapholunate dissociation would demonstrate improvements in all radiographic parameters and patient-reported outcomes (PROMs) after ANAFAB reconstruction. METHODS: From 2018 to 2021, 21 ANAFAB reconstructions performed by a single surgeon were followed prospectively, with 20 patients having a minimum follow-up of 12 months. In total, 17 men and four women were included, with an average age of 49 years. Three patients had modified Garcia-Elias stage 3 disease, eight stage 4, seven stage 5, and three stage 7. ANAFAB reconstruction of intrinsic and extrinsic ligament stabilizers was performed using a hybrid synthetic tape/tendon graft in a transosseous reconstruction. Pre- and postoperative radiographic parameters, grip, pinch strength, the Patient-Rated Wrist Evaluation, PROMIS Upper Extremity Function, and PROMIS Pain Interference outcome measures were compared. RESULTS: Mean follow-up was 17.9 months (range: 12-38). Radiographic parameters were improved at follow-up, including the following: scapholunate angle (mean 75.3° preoperatively to 69.2°), scapholunate gap (5.9-4.2 mm), dorsal scaphoid translation (1.2-0.2 mm), and radiolunate angle (13.5° to 1.8°). Mean Patient-Rated Wrist Evaluation scores for pain and function decreased from 40.6 before surgery to 10.4. We were unable to detect a significant difference in grip or pinch strength or radioscaphoid angle with the numbers tested. There were two minor complications, and two complications required re-operations, one patient who was converted to a proximal row carpectomy for failure of fixation, and one who required tenolysis/arthrolysis for arthrofibrosis. CONCLUSIONS: At 17.9-month average follow-up, radiographic and patient-reported outcome parameters improved after reconstruction of the critical dorsal and volar ligament stabilizers of the proximal carpal row with the ANAFAB technique. TYPE OF STUDY/LEVEL OF EVIDENCE: Therapeutic IV.

Artificial Intelligence for Automated Implant Identification in Total Hip Arthroplasty: A Multicenter External Validation Study Exceeding Two Million Plain Radiographs.

BACKGROUND: The surgical management of complications after total hip arthroplasty (THA) necessitates accurate identification of the femoral implant manufacturer and model. Automated image processing using deep learning has been previously developed and internally validated; however, external validation is necessary prior to responsible application of artificial intelligence (AI)-based technologies. METHODS: We trained, validated, and externally tested a deep learning system to classify femoral-sided THA implants as one of the 8 models from 2 manufacturers derived from 2,954 original, deidentified, retrospectively collected anteroposterior plain radiographs across 3 academic referral centers and 13 surgeons. From these radiographs, 2,117 were used for training, 249 for validation, and 588 for external testing. Augmentation was applied to the training set (n = 2,117,000) to increase model robustness. Performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated. RESULTS: The training and testing sets were drawn from statistically different populations of implants (P < .001). After 1,000 training epochs by the deep learning system, the system discriminated 8 implant models with a mean area under the receiver operating characteristic curve of 0.991, accuracy of 97.9%, sensitivity of 88.6%, and specificity of 98.9% in the external testing dataset of 588 anteroposterior radiographs. The software classified implants at a mean speed of 0.02 seconds per image. CONCLUSION: An AI-based software demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents responsible and meaningful clinical application of AI with immediate potential to globally scale and assist in preoperative planning prior to revision THA.

Pectoralis muscle injuries in Major and Minor League Baseball.

BACKGROUND AND HYPOTHESIS: Although shoulder and elbow injuries in professional baseball players have been thoroughly studied, little is known about the frequency and impact of pectoralis muscle injuries in this population. The purpose of this study was to use the official league injury surveillance system to describe pectoralis muscle injuries in professional baseball players in Major League Baseball (MLB) and Minor League Baseball (MiLB). Specifically, (1) player demographic characteristics, (2) return to play (RTP), (3) injury mechanism, (4) throwing- and batting-side dominance, and (5) injury rate per athlete exposure (AE) were characterized to guide future injury prevention strategies. METHODS: The MLB Health and Injury Tracking System database was used to compile all pectoralis muscle injuries in MLB and MiLB athletes in the 2011-2017 seasons. Injury-related data including diagnosis (tear or rupture vs. strain), player demographic characteristics, injury timing, need for surgical intervention, RTP, and mechanism of injury were recorded. Subanalyses of throwing- and batting-side dominance, as well as MLB vs. MiLB injury frequency, were performed. RESULTS: A total of 138 pectoralis muscle injuries (32 MLB and 106 MiLB injuries) were reported in the study period (5 tears or ruptures and 133 strains), with 5 of these being recurrent injuries. Operative intervention was performed in 4 athletes (2.9%). Of the 138 injuries, 116 (84.1%) resulted in missed days of play, with a mean time to RTP of 19.5 days. Starting pitchers sustained the greatest proportion of pectoralis injuries (48.1%), with pitching being the most common activity at the time of injury (45.9%). A majority of injuries (86.5%) were sustained during non-contact play. Overall, 87.5% of injuries occurred on the player's dominant throwing side and 81.3% occurred on the player's dominant batting side. There was no significant difference in the rate of pectoralis injuries in the MLB regular season (0.584 per 10,000 AEs) vs. the MiLB regular season (0.425 per 10,000 AEs) (P = .1018). CONCLUSION: Pectoralis muscle injuries are most frequently non-contact injuries, most commonly sustained by pitchers. An understanding of these injuries can guide athletic trainers and management in expectation management and decision making, in addition to directing future efforts at injury prevention.

Pediatric orthopedic mythbusters: the truth about flexible flatfeet, tibial and femoral torsion, W-sitting, and idiopathic toe-walking.

PURPOSE OF REVIEW: Myths, widely held but false or unproven beliefs, exist in pediatric orthopedics, with the most common examples related to flexible flatfeet, in-toeing/out-toeing, W-sitting, and toe-walking. Concerns regarding these findings and suggested treatments, unfounded in science, may be passed along verbally or published through various media, without citation. The current review investigates these myths and provides up to date recommendations on diagnosis and treatment (or lack of necessary treatment) for these common pediatric orthopedic findings. RECENT FINDINGS: Orthotics used in childhood do not alter foot development for flexible flatfeet. W-sitting is not associated with developmental dysplasia of the hip, and there is no scientific evidence to support that it leads to contractures, hip dislocations, or functional deficits. SUMMARY: Misinformation about normal variants of growth in childhood and suggested treatments are rampant and can be found published through various media without citation, as supportive scientific studies do not exist or existing studies refute the claims. Flexible flatfeet, in-toeing/out-toeing, W-sitting, and toe-walking typically improve throughout childhood without intervention. Physical therapy, orthotics and bracing have not been proven effective. Treatment is required in rare scenarios and should be directed by the orthopedic surgeon.

Clinical and Research Medical Applications of Artificial Intelligence.

Artificial intelligence (AI), including machine learning (ML), has transformed numerous industries through newfound efficiencies and supportive decision-making. With the exponential growth of computing power and large datasets, AI has transitioned from theory to reality in teaching machines to automate tasks without human supervision. AI-based computational algorithms analyze "training sets" using pattern recognition and learning from inputted data to classify and predict outputs that otherwise could not be effectively analyzed with human processing or standard statistical methods. Though widespread understanding of the fundamental principles and adoption of applications have yet to be achieved, recent applications and research efforts implementing AI have demonstrated great promise in predicting future injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting telehealth. With appreciation, caution, and experience applying AI, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. The purpose of this review is to discuss the pearls, pitfalls, and applications associated with AI.

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Knee.

BACKGROUND: Revisions and reoperations for patients who have undergone total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), and distal femoral replacement (DFR) necessitates accurate identification of implant manufacturer and model. Failure risks delays in care, increased morbidity, and further financial burden. Deep learning permits automated image processing to mitigate the challenges behind expeditious, cost-effective preoperative planning. Our aim was to investigate whether a deep-learning algorithm could accurately identify the manufacturer and model of arthroplasty implants about the knee from plain radiographs. METHODS: We trained, validated, and externally tested a deep-learning algorithm to classify knee arthroplasty implants from one of 9 different implant models from retrospectively collected anterior-posterior (AP) plain radiographs from four sites in one quaternary referral health system. The performance was evaluated by calculating the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, and accuracy when compared with a reference standard of implant model from operative reports. RESULTS: The training and validation data sets were comprised of 682 radiographs across 424 patients and included a wide range of TKAs from the four leading implant manufacturers. After 1000 training epochs by the deep-learning algorithm, the model discriminated nine implant models with an AUC of 0.99, accuracy 99%, sensitivity of 95%, and specificity of 99% in the external-testing data set of 74 radiographs. CONCLUSIONS: A deep learning algorithm using plain radiographs differentiated between 9 unique knee arthroplasty implants from four manufacturers with near-perfect accuracy. The iterative capability of the algorithm allows for scalable expansion of implant discriminations and represents an opportunity in delivering cost-effective care for revision arthroplasty.

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip.

BACKGROUND: The surgical management of complications surrounding patients who have undergone hip arthroplasty necessitates accurate identification of the femoral implant manufacturer and model. Failure to do so risks delays in care, increased morbidity, and further economic burden. Because few arthroplasty experts can confidently classify implants using plain radiographs, automated image processing using deep learning for implant identification may offer an opportunity to improve the value of care rendered. METHODS: We trained, validated, and externally tested a deep-learning system to classify total hip arthroplasty and hip resurfacing arthroplasty femoral implants as one of 18 different manufacturer models from 1972 retrospectively collected anterior-posterior (AP) plain radiographs from 4 sites in one quaternary referral health system. From these radiographs, 1559 were used for training, 207 for validation, and 206 for external testing. Performance was evaluated by calculating the area under the receiver-operating characteristic curve, sensitivity, specificity, and accuracy, as compared with a reference standard of implant model from operative reports with implant serial numbers. RESULTS: The training and validation data sets from 1715 patients and 1766 AP radiographs included 18 different femoral components across four leading implant manufacturers and 10 fellowship-trained arthroplasty surgeons. After 1000 training epochs by the deep-learning system, the system discriminated 18 implant models with an area under the receiver-operating characteristic curve of 0.999, accuracy of 99.6%, sensitivity of 94.3%, and specificity of 99.8% in the external-testing data set of 206 AP radiographs. CONCLUSIONS: A deep-learning system using AP plain radiographs accurately differentiated among 18 hip arthroplasty models from four industry leading manufacturers.

The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty.

HYPOTHESIS/PURPOSE: The objective is to develop and validate an artificial intelligence model, specifically an artificial neural network (ANN), to predict length of stay (LOS), discharge disposition, and inpatient charges for primary anatomic total (aTSA), reverse total (rTSA), and hemi- (HSA) shoulder arthroplasty to establish internal validity in predicting patient-specific value metrics. METHODS: Using data from the National Inpatient Sample between 2003 and 2014, 4 different ANN models to predict LOS, discharge disposition, and inpatient costs using 39 preoperative variables were developed based on diagnosis and arthroplasty type: primary chronic/degenerative aTSA, primary chronic/degenerative rTSA, primary traumatic/acute rTSA, and primary acute/traumatic HSA. Models were also combined into diagnosis type only. Outcome metrics included accuracy and area under the curve (AUC) for a receiver operating characteristic curve. RESULTS: A total of 111,147 patients undergoing primary shoulder replacement were included. The machine learning algorithm predicting the overall chronic/degenerative conditions model (aTSA, rTSA) achieved accuracies of 76.5%, 91.8%, and 73.1% for total cost, LOS, and disposition, respectively; AUCs were 0.75, 0.89, and 0.77 for total cost, LOS, and disposition, respectively. The overall acute/traumatic conditions model (rTSA, HSA) had accuracies of 70.3%, 79.1%, and 72.0% and AUCs of 0.72, 0.78, and 0.79 for total cost, LOS, and discharge disposition, respectively. CONCLUSION: Our ANN demonstrated fair to good accuracy and reliability for predicting inpatient cost, LOS, and discharge disposition in shoulder arthroplasty for both chronic/degenerative and acute/traumatic conditions. Machine learning has the potential to preoperatively predict costs, LOS, and disposition using patient-specific data for expectation management between health care providers, patients, and payers.

Newer Generation of Cementless Total Knee Arthroplasty: A Systematic Review.

INTRODUCTION: Although the use of cementless implants in total knee arthroplasty (TKA) has increased in recent years, there is still ongoing debate regarding the optimal method of fixation. The purpose of this review was to evaluate the evidence regarding cementless versus cemented total knee arthroplasty (TKA) with regard to: (1) all-cause survivorship and aseptic survivorship; and (2) patient-reported outcome measures (PROMs) of newer generation TKAs. MATERIALS AND METHODS: A systematic review of all reports on cementless TKA published from January 2010 to February 2019 was performed. A total of 221 articles were evaluated and 39 studies met inclusion criteria for final analysis. Metrics evaluated included all-cause survivorship, aseptic survivorship, and Knee Society Scores (KSS). RESULTS: Modern cementless TKA provides excellent survivorship and patient-reported outcomes as compared to cemented designs. CONCLUSIONS: Recent studies have demonstrated that newer generation cementless TKAs provide similar functional outcomes and survivorship as compared to cemented TKA. However, additional prospective, randomized trials with long-term follow up are necessary to further compare the outcomes of cementless versus cemented TKA.

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.

Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring.

BACKGROUND: Driven by the recent ubiquity of big data and computing power, we established the Machine Learning Arthroplasty Laboratory (MLAL) to examine and apply artificial intelligence (AI) to musculoskeletal medicine. METHODS: In this review, we discuss the 2 core objectives of the MLAL as they relate to the practice and progress of orthopedic surgery: (1) patient-specific, value-based care and (2) human movement. RESULTS: We developed and validated several machine learning-based models for primary lower extremity arthroplasty that preoperatively predict patient-specific, risk-adjusted value metrics, including cost, length of stay, and discharge disposition, to provide improved expectation management, preoperative planning, and potential financial arbitration. Additionally, we leveraged passive, ubiquitous mobile technologies to build a small data registry of human movement surrounding TKA that permits remote patient monitoring to evaluate therapy compliance, outcomes, opioid intake, mobility, and joint range of motion. CONCLUSION: The rapid rate with which we in arthroplasty are acquiring and storing continuous data, whether passively or actively, demands an advanced processing approach: AI. By carefully studying AI techniques with the MLAL, we have applied this evolving technique as a first step that may directly improve patient outcomes and practice of orthopedics.

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.