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Purpose: To (1) characterize the various forms of wearable sensor devices (WSDs) and (2) review the peer-reviewed literature of applied wearable technology within sports medicine., Methods: A systematic search of PubMed and EMBASE databases, from inception through 2023, was conducted to identify eligible studies using WSDs within sports medicine. Data extraction was performed of study demographics and sensor specifications. Included studies were categorized by application: athletic training, rehabilitation, and research., Results: In total, 43 studies met criteria for inclusion in this review. Forms of WSDs include pedometers, accelerometers, encoders (consisting of magnetometers and gyroscopes), force sensors, global positioning system trackers, and inertial measurement units. Outcome metrics include step counts; gait, limb motion, and angular positioning; foot and skin pressure; change of direction and inclination, including analysis of both body parts and athletes on a field; displacement and velocity of body segments and joints; heart rate; plethysmography; sport-specific kinematics; range of motion, symmetry, and alignment; head impact; sleep; throwing biomechanics; and kinetic and spatiotemporal running metrics. WSDs are used in athletic training to assess sport-specific biomechanics and workload with a goal of injury prevention and training optimization, as well as for rehabilitation monitoring and research such as for risk predicting and aiding diagnosis., Conclusions: WSDs enable real-time monitoring of human performance across a variety of implementations and settings, allowing collection of metrics otherwise not achievable. WSDs are powerful tools with multiple applications within athletic training, patient rehabilitation, and orthopaedic and sports medicine research., Clinical Relevance: Wearable technology may represent the missing link to quantitatively addressing return to play and previous performance. WSDs are commercially available and portable adjuncts that allow clinicians, trainers, and individual athletes to monitor biomechanical parameters, workload, and recovery status to better contextualize personalized training, injury risk, and rehabilitation., Competing Interests: Disclosure The authors report the following potential conflicts of interest or sources of funding: P.N.R. reports a relationship with Intelligent Health Analytics Inc and Globus that includes: equity or stocks; and is an Associate Editor of Arthroscopy. All other authors (R.J.O., S.S.H., J.J.W., D.H.N.) declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Full ICMJE author disclosure forms are available for this article online, as supplementary material., (Copyright © 2024 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.)

Background: Post anesthesia care units (PACU) await return of motor function in lower extremities, prior to discharge for patients undergoing spinal anesthesia. The purpose of this study was to assess the impact of a newly utilized recovery protocol that facilitated early discharges of patients undergoing total hip and knee arthroplasties (THA/TKA) to the floor before full motor recovery from spinal anesthesia is achieved., Methods: A total of 647 patients undergoing spinal anesthesia for primary THA (n = 190) and TKA (n = 457) were divided into 2 groups: (1) Early PACU discharge group: patients with partial or full motor blockade at discharge. (2) Control PACU discharge group: patients with full motor recovery at discharge. Readiness for discharge was assessed using a modified Aldrete Score system. The primary outcome was incidences of hypotension or rapid responses post-operatively., Results: There was no significant difference in the incidence of hypotension between the two groups (1.4 versus 1.39%, P = 1.0) and zero rapid responses were noted. Early discharge shortened mean PACU LOS time from 86.50 minutes to 70.27 minutes (P < .01). There was no difference in the incidence of nausea (0.55 versus 0%; P = .51) ordizziness (2.22 versus 0.35%; P = .09)., Conclusion: In this retrospective observational study, we found that early PACU discharge did not result in an increase in hemodynamic consequences on the surgical floor. Thus, discharge from PACU can be safely and more expeditiously performed without waiting for return of motor function in patients receiving spinal anesthesia for THA/TKA using a modified Aldrete Score recovery protocol., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Competing Interests: Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article ( http://links.lww.com/JBJS/H691 ).

Background: Software-infused services, from robot-assisted and wearable technologies to artificial intelligence (AI)-laden analytics, continue to augment clinical orthopaedics - namely hip and knee arthroplasty. Extended reality (XR) tools, which encompass augmented reality, virtual reality, and mixed reality technology, represent a new frontier for expanding surgical horizons to maximize technical education, expertise, and execution. The purpose of this review is to critically detail and evaluate the recent developments surrounding XR in the field of hip and knee arthroplasty and to address potential future applications as they relate to AI., Methods: In this narrative review surrounding XR, we discuss (1) definitions, (2) techniques, (3) studies, (4) current applications, and (5) future directions. We highlight XR subsets (augmented reality, virtual reality, and mixed reality) as they relate to AI in the increasingly digitized ecosystem within hip and knee arthroplasty., Results: A narrative review of the XR orthopaedic ecosystem with respect to XR developments is summarized with specific emphasis on hip and knee arthroplasty. The XR as a tool for education, preoperative planning, and surgical execution is discussed with future applications dependent upon AI to potentially obviate the need for robotic assistance and preoperative advanced imaging without sacrificing accuracy., Conclusion: In a field where exposure is critical to clinical success, XR represents a novel stand-alone software-infused service that optimizes technical education, execution, and expertise but necessitates integration with AI and previously validated software solutions to offer opportunities that improve surgical precision with or without the use of robotics and computed tomography-based imaging., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Surgical management of complications following knee arthroplasty demands accurate and timely identification of implant manufacturer and model. Automated image processing using deep machine learning has been previously developed and internally validated; however, external validation is essential prior to scaling clinical implementation for generalizability., Methods: We trained, validated, and externally tested a deep learning system to classify knee arthroplasty systems as one of the 9 models from 4 manufacturers derived from 4,724 original, retrospectively collected anteroposterior plain knee radiographs across 3 academic referral centers. From these radiographs, 3,568 were used for training, 412 for validation, and 744 for external testing. Augmentation was applied to the training set (n = 3,568,000) to increase model robustness. Performance was determined by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated. The training and testing sets were drawn from statistically different populations of implants (P < .001)., Results: After 1,000 training epochs by the deep learning system, the system discriminated 9 implant models with a mean area under the receiver operating characteristic curve of 0.989, accuracy of 97.4%, sensitivity of 89.2%, and specificity of 99.0% in the external testing dataset of 744 anteroposterior radiographs. The software classified implants at a mean speed of 0.02 seconds per image., Conclusion: An artificial intelligence-based software for identifying knee arthroplasty implants demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents a responsible and meaningful clinical application of artificial intelligence with immediate potential to globally scale and assist in preoperative planning prior to revision knee arthroplasty., (Copyright © 2023 Elsevier Inc. All rights reserved.)

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., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Despite a growing understanding of spinopelvic biomechanics in total hip arthroplasty (THA), there is no validated approach for executing patient-specific acetabular component positioning. The purpose of this study was to (1) validate quantitative, patient-specific acetabular "safe zone" component positioning from spinopelvic parameters and (2) characterize differences between quantitative patient-specific acetabular targets and qualitative hip-spine classification targets., Methods: From 2,457 consecutive primary THA patients, 22 (0.88%) underwent revision for instability. Spinopelvic parameters were measured prior to index THA. Acetabular position was measured following index and revision arthroplasty. Using a mathematical proof, we developed an open-source tool translating a surgeon-selected, preoperative standing acetabular target to a patient-specific safe zone intraoperative acetabular target. Difference between the patient-specific safe zone and the actual component position was compared before and after revision. Hip-spine classification targets were compared to patient-specific safe zone targets., Results: Of the 22 who underwent revision, none dislocated at follow-up (4.6 [range, 1 to 6.9]). Patient-specific safe zone targets differed from prerevision acetabular component position by 9.1 ± 4.2° inclination/13.3 ± 6.7° version; after revision, the mean difference was 3.2 ± 3.0° inclination/5.3 ± 2.7° version. Differences between patient-specific safe zones and the median and extremes of recommended hip-spine classification targets were 2.2 ± 1.9° inclination/5.6 ± 3.7° version and 3.0 ± 2.3° inclination/7.9 ± 3.5° version, respectively., Conclusion: A mathematically derived, patient-specific approach accommodating spinopelvic biomechanics for acetabular component positioning was validated by approximating revised, now-stable hips within 5° version and 3° inclination. These patient-specific safe zones augment the hip-spine classification with prescriptive quantitative targets for nuanced preoperative planning., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Artificial intelligence (AI) aims to simulate human intelligence using automated computer algorithms. There has been a rapid increase in research applying AI to various subspecialties of orthopedic surgery, including shoulder surgery. The purpose of this review is to assess the scope and validity of current clinical AI applications in shoulder surgery literature., Methods: A systematic literature review was conducted using PubMed for all articles published between January 1, 2010 and June 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (shoulder OR shoulder surgery OR rotator cuff) . All studies that examined AI application models in shoulder surgery were included and evaluated for model performance and validation (internal, external, or both)., Results: A total of 45 studies were included in the final analysis. Eighteen studies involved shoulder arthroplasty, 13 rotator cuff, and 14 other areas. Studies applying AI to shoulder surgery primarily involved (1) automated imaging analysis including identifying rotator cuff tears and shoulder implants (2) risk prediction analyses including perioperative complications, functional outcomes, and patient satisfaction. Highest model performance area under the curve ranged from 0.681 (poor) to 1.00 (perfect). Only 2 studies reported external validation., Conclusion: Applications of AI in the field of shoulder surgery are expanding rapidly and offer patient-specific risk stratification for shared decision-making and process automation for resource preservation. However, model performance is modest and external validation remains to be demonstrated, suggesting increased scientific rigor is warranted prior to deploying AI-based clinical applications., (© 2023 The Author(s).)

Purpose: To assess the clinical utility of preoperative magnetic resonance imaging (MRI) and quantify the delay in surgical care for patients aged ≤40 years undergoing primary hip arthroscopy with history, physical examination, and radiographs concordant with femoroacetabular impingement syndrome (FAIS)., Methods: From August 2015 to December 2020, 1,786 consecutive patients were reviewed from the practice of 1 fellowship-trained hip arthroscopist. Inclusion criteria were FAIS, primary surgery, and age ≤40 years. Exclusion criteria were MRI contraindication, reattempt of conservative management, or concomitant periacetabular osteotomy. After nonoperative treatment options were exhausted and a surgical plan was established, patients were stratified by those who presented with versus without MRI. Those without existing MRI received one, and any deviations from the surgical plan were noted. All preoperative MRIs were compared with office evaluation and intraoperative findings to assess agreement. Demographic data, Hip Disability and Osteoarthritis Outcome Score (HOOS)-Pain, and time from office to MRI or arthroscopy were recorded., Results: Of the patients indicated by history, physical examination, and radiographs alone (70% female, body mass index 24.8 kg/m 2 , age 25.9 years), 198 patients presented without MRI and 934 with MRI. None of the 198 had surgical plans altered after MRI. Patients in both groups had MRI findings demonstrating anterosuperior labral tears that were visualized and repaired intraoperatively. Mean time from office to arthroscopy for patients without MRI versus those with was 107.0 ± 67 and 85.0 ± 53 days, respectively (P < .001). Time to MRI was 22.8 days. No difference between groups was observed among the 85% of patients who surpassed the HOOS-Pain minimal clinically important difference (MCID)., Conclusion: Once indicated for surgery based on history, physical examination, and radiographs, preoperative MRI did not alter the surgical plan for patients aged ≤40 years with FAIS undergoing primary hip arthroscopy. Moreover, preoperative MRI delayed time to arthroscopy. The necessity of routine preoperative MRI in the young primary FAIS population should be challenged., (Copyright © 2022 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.)

There exists great hope and hype in the literature surrounding applications of artificial intelligence (AI) to orthopaedic surgery. Between 2018 and 2021, a total of 178 AI-related articles were published in orthopaedics. However, for every 2 original research papers that apply AI to orthopaedics, a commentary or review is published (30.3%). AI-related research in orthopaedics frequently fails to provide use cases that offer the uninitiated an opportunity to appraise the importance of AI by studying meaningful questions, evaluating unknown hypotheses, or analyzing quality data. The hype perpetuates a feed-forward cycle that relegates AI to a meaningless buzzword by rewarding those with nascent understanding and rudimentary technical knowhow into committing several basic errors: (1) inappropriately conflating vernacular ("AI/machine learning"), (2) repackaging registry data, (3) prematurely releasing internally validated algorithms, (4) overstating the "black box phenomenon" by failing to provide weighted analysis, (5) claiming to evaluate AI rather than the data itself, and (6) withholding full model architecture code. Relevant AI-specific guidelines are forthcoming, but forced application of the original Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines designed for regression analyses is irrelevant and misleading. To safeguard meaningful use, AI-related research efforts in orthopaedics should be (1) directed toward administrative support over clinical evaluation and management, (2) require the use of the advanced model, and (3) answer a question that was previously unknown, unanswered, or unquantifiable., (Copyright © 2022 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2022 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.)

Background: Psoriasis is a dermatologic condition characterized by erythematous plaques that may increase wound complications and deep infections following total knee arthroplasty (TKA). There is a paucity of evidence concerning the association of this disease and complications after TKA. This study aimed to determine if patients who have psoriasis vs non-psoriatic patients have differences in demographics and various comorbidities as well as post-operative infections, specifically the following: (1) wound complications; (2) cellulitic episodes; and (3) deep surgical site infections (SSIs)., Methods: We identified 10,727 patients undergoing primary TKA utilizing an institutional database between January 1, 2017 and April 1, 2019. A total of 133 patients who had psoriasis (1.2%) were identified using International Classification of Diseases, Tenth Revision codes and compared to non-psoriatic patients. The rate of wound complications, cellulitic episodes, and deep SSIs were determined. After controlling for age and various comorbidities, multivariate analyses were performed to identify the associated risks for post-operative infections., Results: Psoriasis patients showed an increased associated risk of deep SSIs (3.8%) compared to non-psoriasis patients (1.2%, P = .023). Multivariate analyses demonstrated a significant associated risk of deep SSIs (odds ratio 7.04, 95% confidence interval 2.38-20.9, P < .001) and wound complications (odds ratio 4.44, 95% confidence interval 1.02-19.2, P = .047)., Conclusion: Psoriasis is an inflammatory dermatologic condition that warrants increased pre-operative counseling, shared decision-making, and infectious precautions in the TKA population given the increased risk of wound complications and deep SSIs. Increased vigilance is required given the coexistence of certain comorbidities with this population, including depression, substance use disorder, smoking history, gastroesophageal reflux disease, and inflammatory bowel disease., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Purpose: The purpose of this study was to determine the cost of the episode of care for primary rotator cuff repair (RCR) from day of surgery to 90 days postoperatively using the time-driven activity-based costing (TDABC) method. The secondary purpose of this study was to identify the main drivers of cost for both phases of care., Methods: This retrospective case series study used the TDABC method to determine the bundled cost of care for an RCR. First, a process map of the RCR episode of care was constructed in order to determine drivers of fixed (i.e., rent, power), direct variable (i.e., healthcare personnel), and indirect costs (i.e., marketing, building maintenance). The study was performed at a Midwestern tertiary care medical system, and patients were included in the study if they underwent an RCR from January 2018 to January 2019 with at least 90 days of postoperative follow-up. In this article, all costs were included, but we did not account for fees to provider and professional groups., Results: The TDABC method calculated a cost of $10,569 for a bundled RCR, with 76% arising from the operative phase and 24% from the postoperative phase. The main driver of cost within the operative phase was the direct fixed costs, which accounted for 35% of the cost in this phase, and the largest contributor to cost within this category was the cost of implants, which accounted for 55%. In the postoperative phase of care, physical therapy visits were the greatest contributor to cost at 59%., Conclusion: In a bundled cost of care for RCR, the largest cost driver occurs on the day of surgery for direct fixed costs, in particular, the implant. Physical therapy represents over half of the costs of the episode of care. Better understanding the specific cost of care for RCR will facilitate optimization with appropriately designed payment models and policies that safeguard the interests of the patient, physician, and payer., Level of Evidence: IV, therapeutic case series., (Copyright © 2022. Published by Elsevier Inc.)

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., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: The primary purpose of this study was to compare the cost of care of one of the most common sports medicine surgical procedures, anterior cruciate ligament reconstruction (ACLR), using the time-driven activity-based costing (TDABC) method to traditional accounting methods such as activity-based costing (ABC). Our secondary purpose was to identify the main drivers of the cost of ACLR using both of these techniques., Methods: A process map of ACLR was constructed through direct observation in the clinical setting according to established techniques to identify drivers of fixed, direct variable, and indirect costs. An episode of care consisted of each step in the surgical process from admission to discharge. Personnel costs were combined with the process map to determine the cost drivers and overall cost of the procedure. The cost generated from the TDABC method was compared with the cost from our institution's internal accounting system, which used an ABC method., Results: The total cost of ACLR was $5,242.25 when using TDABC versus $10,318 when using the traditional ABC method. The largest difference between the 2 methods was within the domain of direct variable costs., Conclusions: When compared with TDABC, the hospital's traditional cost-accounting estimate for ACLR is nearly twice as costly. These findings highlight the variability of cost calculation for the same clinical episode between the 2 accounting methods. For the traditional accounting method, the direct variable cost was the main cost driver, whereas for the TDABC method, the direct fixed cost was the main cost driver., Clinical Relevance: This study is important because it elucidates important cost drivers for one of the most common sports medicine orthopaedic surgical procedures and attempts to identify the true overall cost of the procedure., (© 2020 Published by Elsevier on behalf of the Arthroscopy Association of North America.)

Background: Shoulder injuries account for a large portion of all recorded injuries in professional baseball. Much is known about other shoulder pathologies in the overhead athlete, but the incidence and impact of acromioclavicular (AC) joint injuries in this population are unknown. We examined the epidemiology of AC joint injuries in Major League Baseball (MLB) and Minor League Baseball (MiLB) players and determined the impact on time missed., Methods: The MLB Health and Injury Tracking System was used to compile records of all MLB and MiLB players from 2011 to 2017 with documented AC joint injuries. These injuries were classified as acute (sprain or separation) or chronic (AC joint arthritis or distal clavicular osteolysis), and associated data extracted included laterality, date of injury, player position, activity, mechanism of injury, length of return to play, and need for surgical intervention., Results: A total of 312 AC joint injuries (183 in MiLB players and 129 in MLB players; range, 39-60 per year) were recorded: 201 acute (64.4%) and 111 chronic (35.6%). A total of 81% of acute and 59% of chronic injuries resulted in time missed, with a mean length of return to play of 21 days for both. Of the injuries in outfielders, 79.6% were acute (P < .0001), as were 66.3% of injuries in infielders (P = .004). Pitchers and catchers had more equal proportions of acute and chronic AC injuries (P > .05 for all). Acute AC injuries occurred most often while fielding (n = 100, 84.7%), running (n = 25, 80.6%), and hitting (n = 19, 61.3%), whereas chronic injuries tended to be more common while pitching (n = 26, 68.4%). Of contact injuries, 82.5% were acute (P < .0001), whereas 59.0% of noncontact injuries were chronic (P = .047). MLB players showed consistently higher regular-season rates of both acute and chronic AC injuries than MiLB players (P < .0001 for each)., Conclusion: Acute AC joint injuries are contact injuries occurring most commonly among infielders and outfielders while fielding that result in 3 weeks missed before return to play, whereas chronic AC joint injuries occur more commonly in pitchers and catchers from noncontact repetitive overhead activity. Knowledge of these data can better guide expectation management in this elite population to better elucidate the prevalence of 2 common injury patterns in the AC joint., (Copyright © 2020 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2020 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.)

Background Context: With the increasing emphasis on value-based healthcare in Centers for Medicare and Medicaid Services reimbursement structures, bundled payment models have been adopted for many orthopedic procedures. Immense variability of patients across hospitals and providers makes these models potentially less viable in spine surgery. Machine-learning models have been shown reliable at predicting patient-specific outcomes following lumbar spine surgery and could, therefore, be applied to developing stratified bundled payment schemes., Purpose: (1) Can a Naïve Bayes machine-learning model accurately predict inpatient payments, length of stay (LOS), and discharge disposition, following dorsal and lumbar fusion? (2) Can such a model then be used to develop a risk-stratified payment scheme?, Study Design: A Naïve Bayes machine-learning model was constructed using an administrative database., Patient Sample: Patients undergoing dorsal and lumbar fusion for nondeformity indications from 2009 through 2016 were included. Preoperative inputs included age group, gender, ethnicity, race, type of admission, All Patients Refined (APR) risk of mortality, APR severity of illness, and Clinical Classifications Software diagnosis code., Outcome Measures: Predicted resource utilization outcomes included LOS, discharge disposition, and total inpatient payments. Model validation was addressed via reliability, model output quality, and decision speed, based on application of training and validation sets. Risk-stratified payment models were developed according to APR risk of mortality and severity of illness., Results: A Naïve Bayes machine-learning algorithm with adaptive boosting demonstrated high reliability and area under the receiver-operating characteristics curve of 0.880, 0.941, and 0.906 for cost, LOS, and discharge disposition, respectively. Patients with increased risk of mortality or severity of illness incurred costs resulting in greater inpatient payments in a patient-specific tiered bundled payment, reflecting increased risk on institutions caring for these patients. We found that a large range in expected payments due to individuals' preoperative comorbidities indicating an individualized risk-based model is warranted., Conclusions: A Naïve Bayes machine-learning model was shown to have good-to-excellent reliability and responsiveness for cost, LOS, and discharge disposition. Based on APR risk of mortality and APR severity of illness, there was a significant difference in episode costs from lowest to highest risk strata. After using normalized model error to develop a risk-adjusted proposed payment plan, it was found that institutions incur significantly more financial risk in flat bundled payment models for patients with higher rates of comorbidities., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (Copyright © 2018 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.)

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.

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., (Copyright © 2018 Elsevier Inc. All rights reserved.)

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., (Copyright © 2018 Elsevier Inc. All rights reserved.)

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., (Copyright © 2018 Elsevier Inc. All rights reserved.)

In this article, we report on the differences in the healing biology of biceps tenodesis performed on either bone or soft tissue in a rat model. This work provides further insight into what may be the optimal strategy for managing biceps-labrum complex disease., Competing Interests: Authors’ Disclosure Statement: The authors report no actual or potential conflict of interest in relation to this article.

To address the lack of consensus on the leading reasons for readmissions after primary elective unilateral total joint arthroplasties (TJAs), we performed a systematic review and a meta-analysis to identify overall and cause-specific readmission rates. We performed structured searches of the Medline and Cochrane databases for original reports-published between January 1982 and January 2013-on both 30- and 90-day follow-ups of unique patient populations that underwent elective primary TJA. Random-effects meta-analyses were performed to obtain pooled estimates. For total hip arthroplasty (THA), the overall pooled readmission rate was 5.6% at 30 days and 7.7% at 90 days. For total knee arthroplasty (TKA), the overall rate was 3.3% at 30 days and 9.7% at 90 days. The leading reason for THA readmission was joint-specific at both 30 and 90 days, and the leading reason for TKA readmission was surgical site infection. Our systematic review and meta-analysis established baseline readmission rates in the literature on primary TJA and identified the most common reasons driving readmission for TJA.