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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., (Copyright © 2024 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.)

Cast application is a critical portion of pediatric orthopaedic surgery training and is being performed by a growing number of non-orthopaedic clinicians including primary care physicians and advanced practice providers (APPs). Given the tremendous remodeling potential of pediatric fractures, correct cast placement often serves as the definitive treatment in this age population as long as alignment is maintained. Proper cast application technique is typically taught through direct supervision from more senior clinicians, with little literature and few resources available for providers to review during the learning process. Given the myriad complications that can result from cast application or removal, including pressure sores and cast saw burns, a thorough review of proper cast technique is warranted. This review and technique guide attempts to illustrate appropriate upper and lower extremity fiberglass cast application (and waterproof casts), including pearls and pitfalls of cast placement. This basic guide may serve as a resource for all orthopaedic and non-orthopaedicproviders, including residents, APPs, and medical students in training. Level of Evidence: IV., Competing Interests: Disclosures: The authors report no potential conflicts of interest related to this study., (Copyright © The Iowa Orthopaedic Journal 2023.)

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: The use of cementless femoral stems in patients undergoing primary total hip arthroplasty (THA) with Dorr C bone remains controversial for fear of fracture or subsidence. Purpose of this multicenter study was to compare clinical outcomes and complications of THA using a tapered femoral prosthesis in patients with Dorr C bone versus Dorr A/B bone., Methods: A total of 1,030 patients underwent primary THA with a tapered wedge femoral stem at a minimum one year follow up. Forty-eight patients with Dorr C bone (mean age 68.7 years) were compared with a matched cohort of patients with Dorr A/B bone (mean age 69.9 years). Mean follow-up was approximately 4 years in both cohorts. There were no differences in sex, age, body mass index (BMI), Harris Hip Score (HHS), complications, and radiographic outcomes including subsidence and aseptic loosening were evaluated., Results: Postoperative HHSs were a mean of 82 points in the Dorr C cohort compared to 84 points in the Door A/B cohort (P=0.2653). There was no significant difference in complication or revision rates for any reason (P=0.23). Mean subsidence for the Dorr C and Dorr A/B was 1.4 and 1.2 mm, respectively (P=0.5164), and there was no aseptic loosening of the femoral component found in either group., Conclusions: Current generation tapered wedge cementless femoral stems provide stable fixation for patients with Dorr C bone quality without increased complications with respect to fracture or subsidence and can be considered an alternative to cemented stems in patients with compromised bone quality., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm.2019.08.124/coif). RD declares the following outside of the submitted work: Baltimore City Medical Society: board or committee member; Flexion Therapeutics: research support; Orthofix, Inc.: research support; Stryker: research support; tissue Gene: research support; United Orthopedics: research support. MAM declares the following outside of the submitted work: AAOS: board or committee member; American Association of Hip and Knee Surgeons: board or committee member; Cymedica: paid consultant; DJ Orthopaedics: paid consultant; research support; Flexion Therapeutics: paid consultant; Johnson & Johnson: paid consultant; research support; Journal of Arthroplasty: editorial or governing board; Journal of Knee Surgery: editorial or governing board; Knee Society: board or committee member; Medicus Works LLC: publishing royalties, financial or material support; Microport: IP royalties; National Institutes of Health (NIAMS & NICHD): research support; Ongoing Care Solutions: paid consultant; research support; Orthopedics: editorial or governing board; Orthosensor: paid consultant; research support; Pacira: paid consultant; Peerwell: paid consultant; stock or stock options; Performance Dynamics: paid consultant; Pfizer: paid consultant; Skye Biologics: paid consultant; Stryker: IP royalties; paid consultant; research support; Surgical Techniques International: editorial or governing board; TissueGene: paid consultant; research support; Up-to-Date: publishing royalties, financial or material support; USMI: stock or stock options; Wolters Kluwer Health-Lippincott Williams & Wilkins: publishing royalties, financial or material support. ALM declares the following outside of the submitted work: AAOS: board or committee member; Journal of Arthroplasty: editorial or governing board; Stryker: IP royalties; paid consultant; paid presenter or speaker; research support. The other authors have no conflicts of interest to declare., (2023 Annals of Translational Medicine. All rights reserved.)

Aims: The aims of the study were to report for a cohort aged younger than 40 years: 1) indications for HRA; 2) patient-reported outcomes in terms of the modified Harris Hip Score (HHS); 3) dislocation rate; and 4) revision rate., Methods: This retrospective analysis identified 267 hips from 224 patients who underwent an hip resurfacing arthroplasty (HRA) from a single fellowship-trained surgeon using the direct lateral approach between 2007 and 2019. Inclusion criteria was minimum two-year follow-up, and age younger than 40 years. Patients were followed using a prospectively maintained institutional database., Results: A total of 217 hips (81%) were included for follow-up analysis at a mean of 3.8 years. Of the 23 females who underwent HRA, none were revised, and the median head size was 46 mm (compared to 50 mm for males). The most common indication for HRA was femoroacetabular impingement syndrome (n = 133), and avascular necrosis ( (n = 53). Mean postoperative HHS was 100 at two and five years. No dislocations occurred. A total of four hips (1.8%) required reoperation for resection of heterotopic ossification, removal of components for infection, and subsidence with loosening. The overall revision rate was 0.9%., Conclusion: For younger patients with higher functional expectations and increased lifetime risk for revision, HRA is an excellent bone preserving intervention carrying low complication rates, revision rates, and excellent patient outcomes without lifetime restrictions allowing these patients to return to activity and sport. Thus, in younger male patients with end-stage hip disease and higher demands, referral to a high-volume HRA surgeon should be considered., Competing Interests: P. J. Brooks reports royalties or licenses, whereby the author licensed mushroom templates to Smith & Nephew (instruments used in the procedure), and being a consultant for Smith & Nephew, which is related to this work. P. N. Ramkumar declares consulting fees from Stryker and Globus, royalties from Globus, support for attending meetings and/or travel from Stryker and Arthrex, and stock or stock options from Intelligent Health Analytics Inc., Overture Resurfacing, Johnson & Johnson, and Conformis, all of which is unrelated to this article., (© 2023 Author(s) et al.)

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).)

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.)

Artificial intelligence (AI) represents the fourth industrial revolution and the next frontier in medicine poised to transform the field of orthopaedics and sports medicine, though widespread understanding of the fundamental principles and adoption of applications remain nascent. Recent research efforts into implementation of AI in the field of orthopaedic surgery and sports medicine have demonstrated great promise in predicting athlete injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting the patient experience. Not unlike the recent emphasis thrust upon physicians to understand the business of medicine, the future practice of sports medicine specialists will require a fundamental working knowledge of the strengths, limitations, and applications of AI-based tools. With appreciation, caution, and experience applying AI to sports medicine, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. In this Current Concepts review, we discuss the definitions, strengths, limitations, and applications of AI from the current literature as it relates to orthopaedic sports medicine.

Background: The number of patients requiring reoperation has increased as the volume of hip arthroscopy for femoroacetabular impingement syndrome (FAIS) has increased. The factors most important in determining patients who are likely to require reoperation remain elusive., Purpose: To leverage machine learning to better characterize the complex relationship across various preoperative factors (patient characteristics, radiographic parameters, patient-reported outcome measures [PROMs]) for patients undergoing primary hip arthroscopy for FAIS to determine which features predict the need for future ipsilateral hip reoperation, namely, revision hip arthroscopy, total hip arthroplasty (THA), hip resurfacing arthroplasty (HRA), or periacetabular osteotomy (PAO)., Study Design: Cohort study; Level of evidence, 3., Methods: A cohort of 3147 patients undergoing 3748 primary hip arthroscopy procedures were included from an institutional hip preservation registry. Preoperative computed tomography of the hip was obtained for each patient, from which the following parameters were calculated: the alpha angle; the coronal center-edge angle; the neck-shaft angle; the acetabular version angle at 1, 2, and 3 o'clock; and the femoral version angle. Preoperative PROMs included the modified Harris Hip Score (mHHS), the Hip Outcome Score (HOS)-Activities of Daily Living subscale (HOS-ADL) and the Sport Specific subscale, and the international Hip Outcome Tool (iHOT-33). Random forest models were created for revision hip arthroscopy, the THA, the HRA, and the PAO. Area under the curve (AUC) for the receiver operating characteristic curve and accuracy were calculated to evaluate each model., Results: A total of 171 patients (4.6%) underwent subsequent hip surgery after primary hip arthroscopy for FAIS. The AUC and accuracy, respectively, were 0.77 (fair) and 76% for revision hip arthroscopy (mean, 26.4-month follow-up); 0.80 (good) and 81% for THA (mean, 32.5-month follow-up); 0.62 (poor) and 69% for HRA (mean, 45.4-month follow-up); and 0.76 (fair) and 74% for PAO (mean, 30.4-month follow-up). The most important factors in predicting reoperation after primary hip arthroscopy were higher body mass index (BMI) and lower preoperative HOS-ADL for revision hip arthroscopy, greater age and lower preoperative iHOT-33 for THA, increased BMI for HRA, and larger neck-shaft angle and lower preoperative mHHS for PAO., Conclusion: Despite the low failure rate of hip arthroscopy for FAIS, our study demonstrated that machine learning has the capability to identify key preoperative risk factors that may predict subsequent ipsilateral hip surgery before the index hip arthroscopy. Knowledge of these demographic, radiographic, and patient-reported outcome data may aid in preoperative counseling and expectation management to better optimize hip preservation.

Background: Fresh osteochondral allograft transplantation (OCA) is an effective method of treating symptomatic cartilage defects of the knee. This restoration technique involves the single-stage implantation of viable, mature hyaline cartilage into a chondral or osteochondral lesion. The extent to which preoperative imaging and patient factors predict achieving clinically meaningful outcomes among patients undergoing OCA for cartilage lesions of the knee remains unknown., Purpose: To determine the predictive relationship of preoperative imaging, preoperative patient-reported outcome measures (PROMs), and patient demographics with achievement of the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) for functional and quality-of-life PROMs at 2 years after OCA for symptomatic cartilage defects of the knee., Study Design: Case-control study; Level of evidence, 3., Methods: Data were analyzed for patients who underwent OCA before May 1, 2018, by 2 high-volume fellowship-trained cartilage surgeons. The International Knee Documentation Committee (IKDC) subjective form, Knee Outcome Survey-Activities of Daily Living (KOS-ADL), and mental and physical component summaries of the SF-36 were administered preoperatively and at 2 years postoperatively. A total of 42 predictive models were created using 7 unique architectures to detect achievement of the MCID for each of the 4 outcome measures and the SCB for the IKDC and KOS-ADL. Data inputted into the models included sex, age, body mass index, baseline PROMs, lesion size, concomitant ligamentous or meniscal tear, and presence of "bone bruise" or osseous edema. Shapley additive explanations plot analysis identified predictors of reaching the MCID and SCB., Results: Of the 185 patients who underwent OCA for the knee and met eligibility criteria from an institutional cartilage registry, 153 (83%) had 2-year follow-up. Preoperative magnetic resonance imaging (MRI), baseline PROMs, and patient demographics best predicted reaching the 2-year MCID and SCB of the IKDC and KOS-ADL PROMs, with areas under the receiver operating characteristic curve of the top-performing models ranging from good (0.88) to excellent (0.91). MRI faired poorly (areas under the curve, 0.60-0.68) in predicting the MCID for the mental and physical component summaries. Higher body mass index, knee malalignment, absence of preoperative osseous edema, concomitant anterior cruciate ligament or meniscal injury, larger defect size, and the implantation of >1 OCA graft were consistent findings contributing to failure to achieve the MCID or SCB at 2 years postoperatively., Conclusion: Our machine learning models demonstrated that preoperative MRI, baseline PROMs, and patient demographics reliably predict the ability to reach clinically meaningful thresholds for functional knee outcomes 2 years after OCA for cartilage defects. Although clinical improvement in knee function can be reliably predicted, improvements in quality of life after OCA depend on a comprehensive preoperative assessment of the patient's perception of his or her mental and physical health. Absence of osseous edema, concomitant anterior cruciate ligament or meniscal injury, larger lesion size on MRI, knee malalignment, and elevated body mass index are predictive of failure to achieve 2-year functional benefits after OCA of the knee.

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

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., (Copyright © 2020 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.)

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.)

Background: Fresh osteochondral allograft transplantation (OCA) is an effective method of treating symptomatic cartilage defects of the knee. This cartilage restoration technique involves the single-stage implantation of viable, mature hyaline cartilage into the chondral or osteochondral lesion. Predictive models for reaching the clinically meaningful outcome among patients undergoing OCA for cartilage lesions of the knee remain under investigation., Purpose: To apply machine learning to determine which preoperative variables are predictive for achieving the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) at 1 and 2 years after OCA for cartilage lesions of the knee., Study Design: Case-control study; Level of evidence, 3., Methods: Data were analyzed for patients who underwent OCA of the knee by 2 high-volume fellowship-trained cartilage surgeons before May 1, 2018. The International Knee Documentation Committee questionnaire (IKDC), Knee Outcome Survey-Activities of Daily Living (KOS-ADL), and Mental Component (MCS) and Physical Component (PCS) Summaries of the 36-Item Short Form Health Survey (SF-36) were administered preoperatively and at 1 and 2 years postoperatively. A total of 84 predictive models were created using 7 unique architectures to detect achievement of the MCID for each of the 4 outcome measures and the SCB for the IKDC and KOS-ADL at both time points. Data inputted into the models included previous and concomitant surgical history, laterality, sex, age, body mass index (BMI), intraoperative findings, and patient-reported outcome measures (PROMs). Shapley Additive Explanations (SHAP) analysis identified predictors of reaching the MCID and SCB., Results: Of the 185 patients who underwent OCA for the knee and met eligibility criteria from an institutional cartilage registry, 135 (73%) patients were available for the 1-year follow-up and 153 (83%) patients for the 2-year follow-up. In predicting outcomes after OCA in terms of the IKDC, KOS-ADL, MCS, and PCS at 1 and 2 years, areas under the receiver operating characteristic curve (AUCs) of the top-performing models ranged from fair (0.72) to excellent (0.94). Lower baseline mental health (MCS), higher baseline physical health (PCS) and knee function scores (KOS-ADL, IKDC Subjective), lower baseline activity demand (Marx, Cincinnati sports), worse pain symptoms (Cincinnati pain, SF-36 pain), and higher BMI were thematic predictors contributing to failure to achieve the MCID or SCB at 1 and 2 years postoperatively., Conclusion: Our machine learning models were effective in predicting outcomes and elucidating the relationships between baseline factors contributing to achieving the MCID for OCA of the knee. Patients who preoperatively report poor mental health, catastrophize pain symptoms, compensate with higher physical health and knee function, and exhibit lower activity demands are at risk for failing to reach clinically meaningful outcomes after OCA of the knee.

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., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Machine learning (ML) allows for the development of a predictive algorithm capable of imbibing historical data on a Major League Baseball (MLB) player to accurately project the player's future availability., Purpose: To determine the validity of an ML model in predicting the next-season injury risk and anatomic injury location for both position players and pitchers in the MLB., Study Design: Descriptive epidemiology study., Methods: Using 4 online baseball databases, we compiled MLB player data, including age, performance metrics, and injury history. A total of 84 ML algorithms were developed. The output of each algorithm reported whether the player would sustain an injury the following season as well as the injury's anatomic site. The area under the receiver operating characteristic curve (AUC) primarily determined validation., Results: Player data were generated from 1931 position players and 1245 pitchers, with a mean follow-up of 4.40 years (13,982 player-years) between the years of 2000 and 2017. Injured players spent a total of 108,656 days on the disabled list, with a mean of 34.21 total days per player. The mean AUC for predicting next-season injuries was 0.76 among position players and 0.65 among pitchers using the top 3 ensemble classification. Back injuries had the highest AUC among both position players and pitchers, at 0.73. Advanced ML models outperformed logistic regression in 13 of 14 cases., Conclusion: Advanced ML models generally outperformed logistic regression and demonstrated fair capability in predicting publicly reportable next-season injuries, including the anatomic region for position players, although not for pitchers., Competing Interests: One or more of the authors has declared the following potential conflicts of interest or source of funding: P.M.S. has received educational support from Arthrex, consulting fees from DJO and DePuy, nonconsulting fees from Arthrex, and hospitality payments from the Musculoskeletal Transplant Foundation. S.J.F. has received grant payments from Arthrex and DJO and educational support from Arthrex and Rock Medical. K.L.S. has received educational support from Arthrex; consulting fees from Molnlycke Health Care; nonconsulting fees from Horizon Pharma; honoraria from Fidia Pharma; and hospitality payments from Biomet Orthopedics, the Musculoskeletal Transplant Foundation, Ramsay Medical, and Stryker. L.D.F. has received consulting fees from Zimmer Biomet and hospitality payments from the Musculoskeletal Transplant Foundation. B.U.N. has received educational support from Smith & Nephew and hospitality payments from Stryker, Wright Medical, and Zimmer Biomet. N.N.V. has received educational support from Medwest; consulting fees from Arthrex, Medacta, and Smith & Nephew; nonconsulting fees from Arthrex and Smith & Nephew; and royalties from Smith & Nephew. E.C.M. has received educational support from Pinnacle (Arthrex), consulting fees from Smith & Nephew, and hospitality payments from Stryker. M.S.S. has received consulting fees and nonconsulting fees from Arthrex. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto., (© The Author(s) 2020.)

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: The relationship between the preoperative radiographic indices for femoroacetabular impingement syndrome (FAIS) and postoperative patient-reported outcome measure (PROM) scores continues to be under investigation, with inconsistent findings reported., Purpose: To apply a machine learning model to determine which preoperative radiographic indices, if any, among patients indicated for the arthroscopic correction of FAIS predict whether a patient will achieve the minimal clinically important difference (MCID) for 1- and 2-year PROM scores., Study Design: Cohort study; Level of evidence, 3., Methods: A total of 1735 consecutive patients undergoing primary hip arthroscopic surgery for FAIS were included from an institutional hip preservation registry. Patients underwent preoperative computed tomography of the hip, from which the following radiographic indices were calculated by a musculoskeletal radiologist: alpha angle, beta angle, sagittal center-edge angle, coronal center-edge angle, neck shaft angle, acetabular version angle, and femoral version angle. PROM scores were collected preoperatively, at 1 year postoperatively, and at 2 years postoperatively for the modified Harris Hip Score (mHHS), the Hip Outcome Score (HOS)-Activities of Daily Living (HOS-ADL) and -Sport Specific (HOS-SS), and the International Hip Outcome Tool (iHOT-33). Random forest models were created for each PROM at 1 and 2 years' follow-up, with each PROM's MCID used to establish clinical meaningfulness. Data inputted into the models included ethnicity, laterality, sex, age, body mass index, and radiographic indices. Comprehensive and separate models were built specifically to assess the association of the alpha angle, femoral version angle, coronal center-edge angle, McKibbin index, and hip impingement index with respect to each PROM., Results: As evidenced by poor area under the curves and P values >.05 for each model created, no combination of radiographic indices or isolated index (alpha angle, coronal center-edge angle, femoral version angle, McKibbin index, hip impingement index) was a significant predictor of a clinically meaningful improvement in scores on the mHHS, HOS-ADL, HOS-SS, or iHOT-33. The mean difference between 1- and 2-year PROM scores compared with preoperative values exceeded the respective MCIDs for the cohort., Conclusion: In patients appropriately indicated for FAIS corrective surgery, clinical improvements can be achieved, regardless of preoperative radiographic indices, such as the femoral version angle, coronal center-edge angle, and alpha angle. No specific radiographic parameter or combination of indices was found to be predictive of reaching the MCID for any of the 4 studied hip-specific PROMs at either 1 or 2 years' follow-up.

Background: The opportunity to quantitatively predict next-season injury risk in the National Hockey League (NHL) has become a reality with the advent of advanced computational processors and machine learning (ML) architecture. Unlike static regression analyses that provide a momentary prediction, ML algorithms are dynamic in that they are readily capable of imbibing historical data to build a framework that improves with additive data., Purpose: To (1) characterize the epidemiology of publicly reported NHL injuries from 2007 to 2017, (2) determine the validity of a machine learning model in predicting next-season injury risk for both goalies and position players, and (3) compare the performance of modern ML algorithms versus logistic regression (LR) analyses., Study Design: Descriptive epidemiology study., Methods: Professional NHL player data were compiled for the years 2007 to 2017 from 2 publicly reported databases in the absence of an official NHL-approved database. Attributes acquired from each NHL player from each professional year included age, 85 performance metrics, and injury history. A total of 5 ML algorithms were created for both position player and goalie data: random forest, K Nearest Neighbors, Naïve Bayes, XGBoost, and Top 3 Ensemble. LR was also performed for both position player and goalie data. Area under the receiver operating characteristic curve (AUC) primarily determined validation., Results: Player data were generated from 2109 position players and 213 goalies. For models predicting next-season injury risk for position players, XGBoost performed the best with an AUC of 0.948, compared with an AUC of 0.937 for LR ( P < .0001). For models predicting next-season injury risk for goalies, XGBoost had the highest AUC with 0.956, compared with an AUC of 0.947 for LR ( P < .0001)., Conclusion: Advanced ML models such as XGBoost outperformed LR and demonstrated good to excellent capability of predicting whether a publicly reportable injury is likely to occur the next season., Competing Interests: One or more of the authors has declared the following potential conflicts of interest or source of funding: Internal funding for this study was provided by the Cleveland Clinic. M.S.S. has received educational support, consulting fees, and speaking fees from Arthrex. E.C.M. has received educational support from Pinnacle (Arthrex), consulting fees from Smith & Nephew, hospitality payments from Smith & Nephew and Stryker, and publishing royalties from Springer. B.U.N. has received educational support from Smith & Nephew and hospitality payments from Wright Medical and Zimmer Biomet. R.J.W. has received consulting fees from Arthrex. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto., (© The Author(s) 2020.)

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.

Background: Studies have shown high-volume surgeons and hospitals deliver higher value care. The aims of this study were to establish meaningful thresholds defining high-volume surgeons and hospitals performing hip fracture surgery and to examine the relative market share of hip fracture cases using these surgeon and hospital strata., Methods: We performed a retrospective cohort study in a database of 103,935 patients undergoing hip fracture repair. We generated stratum-specific likelihood ratio (SSLR) models of a receiver operating characteristic (ROC) curve using length of stay (LOS) and cost value metrics. Volume thresholds predictive of decreased LOS and costs for surgeons and hospitals were identified., Results: Analysis of annual surgeon hip fracture volume produced two volume categories for LOS and cost: 0-30 (low) and 31+ (high). Analysis of LOS by annual hospital hip fracture volume produced strata at: 0-59 (low), 60-146 (medium), and 147 or more (high). Analysis of cost by annual hospital volume produced strata at: 0-125 (low) and 126+ (high). LOS and cost both decreased significantly ( p  < 0.05) in progressively higher volume categories. Low-volume surgeons performed the majority of hip fracture cases, although they were performed at medium- or high-volume centres., Conclusions: This study demonstrates a direct relationship between volume and value, translating to improvement in hip fracture care delivery for both surgeons and hospitals. Higher volume hospitals while lower volume surgeons perform the majority of hip fracture cases, suggesting optimisation opportunities. However, systems-based practices at the hospital level likely drive value to a greater extent than individual surgeons.

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.)

Purpose of Review: With the unprecedented advancement of data aggregation and deep learning algorithms, artificial intelligence (AI) and machine learning (ML) are poised to transform the practice of medicine. The field of orthopedics, in particular, is uniquely suited to harness the power of big data, and in doing so provide critical insight into elevating the many facets of care provided by orthopedic surgeons. The purpose of this review is to critically evaluate the recent and novel literature regarding ML in the field of orthopedics and to address its potential impact on the future of musculoskeletal care., Recent Findings: Recent literature demonstrates that the incorporation of ML into orthopedics has the potential to elevate patient care through alternative patient-specific payment models, rapidly analyze imaging modalities, and remotely monitor patients. Just as the business of medicine was once considered outside the domain of the orthopedic surgeon, we report evidence that demonstrates these emerging applications of AI warrant ownership, leverage, and application by the orthopedic surgeon to better serve their patients and deliver optimal, value-based care.

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.

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: The incidence and effect of sports-related concussions (SRCs) within the global sport of professional soccer is poorly described., Purpose: To comparatively examine the effects of SRC on athletes in Major League Soccer (MLS) and the English Premier League (EPL) in terms of incidence, return to play (RTP), performance, and career longevity., Study Design: Cohort study; Level of evidence, 3., Methods: Contracts, transactions, injury reports, and performance statistics from 2008 to 2017 were obtained and cross-referenced across 6 publicly available websites detailing MLS and EPL data, including official league publications. For each league, players who sustained a concussion were compared with the 2008-2017 uninjured player pool. RTP and games missed were analyzed and compared. Career length was analyzed with Kaplan-Meier survival curves. Player performance changes were evaluated before and after concussion., Results: Of the 1784 eligible MLS and 2001 eligible EPL players evaluated over the 10-year period, the incidence of publicly reported concussions per 1000 athlete-exposures was 20.22 and 18.68, respectively ( P = .53). The incidence of reported concussions steadily increased in both leagues. MLS players missed a mean 7.3 games after concussion (37.0 days missed); EPL players missed a mean 0.6 games after concussion (10.9 days missed) ( P < .0001, P < .0001). Statistical performance in terms of games started, assists, shots on goal, and total shots after concussion was significantly reduced at all nongoalie positions for players in the EPL; however, MLS nongoalie positions with concussion had no significant decreases in these categories. Goalies in both leagues had no significant change in performance or games started. The probability of playing a full season after concussion was not significantly decreased when compared with the uninjured pool in both leagues., Conclusion: This study established the SRC incidence among elite soccer players in 2 international professional leagues and identified major RTP and performance differences between EPL and MLS players. While career longevity was unaffected, the approach to managing concussion as in MLS may better promote player safety and preserve on-field performance.

Objectives: With the transition to a value-based model of care delivery, bundled payment models have been implemented with demonstrated success in elective lower extremity joint arthroplasty. Yet, hip fracture outcomes are dependent on patient-level factors that may not be optimized preoperatively due to acuity of care. The objectives of this study are to (1) develop a supervised naive Bayes machine-learning algorithm using preoperative patient data to predict length of stay and cost after hip fracture and (2) propose a patient-specific payment model to project reimbursements based on patient comorbidities., Methods: Using the New York Statewide Planning and Research Cooperative System database, we studied 98,562 Medicare patients who underwent operative management for hip fracture from 2009 to 2016. A naive Bayes machine-learning model was built using age, sex, ethnicity, race, type of admission, risk of mortality, and severity of illness as predictive inputs., Results: Accuracy was demonstrated at 76.5% and 79.0% for length of stay and cost, respectively. Performance was 88% for length of stay and 89% for cost. Model error analysis showed increasing model error with increasing risk of mortality, which thus increased the risk-adjusted payment for each risk of mortality., Conclusions: Our naive Bayes machine-learning algorithm provided excellent accuracy and responsiveness in the prediction of length of stay and cost of an episode of care for hip fracture using preoperative variables. This model demonstrates that the cost of delivery of hip fracture care is dependent on largely nonmodifiable patient-specific factors, likely making bundled care an implausible payment model for hip fractures.

Background: The rate of hip arthroscopy has increased significantly in recent years, although understanding of patient perception remains limited., Purpose: To analyze posts shared on Instagram and Twitter referencing hip arthroscopy to evaluate perspective, tone, timing, content, visibility, and location., Study Design: Cross-sectional study; Level of evidence, 3., Methods: A search of public posts on Instagram and Twitter was performed over a 1-year period, selected through use of the following hashtags: #hiparthroscopy, #hipscope, and #labralrepair. A total of 1850 Instagram posts and 163 Twitter posts were included in the analysis. A categorical classification system was used for media format (picture or video), perspective (patient, family or friend, physician, hospital or physical therapy group, professional organization, news media, or industry), timing (preoperative, postoperative, nonoperative), perioperative period (within 1 week before or after surgery), tone (positive, negative, or neutral), and content (surgical site, hospital or surgeon, imaging, rehabilitation, activities of daily living (ADLs), return to work, surgical instruments, or education). Post popularity (number of likes) and geographic location were also recorded., Results: Of the 1850 Instagram posts analyzed, 91.2% were made by patients, and 52.9% were positive. The most common content included in Instagram posts was rehabilitation, ADLs, and hospital or surgeon. A total of 163 Twitter posts were analyzed, with 59.0% of posts made by physicians. A majority of posts had a positive tone, and the most frequently referenced themes included education, hospital or surgeon, and rehabilitation. Overall, posts originated from 24 different countries., Conclusion: The majority of patients who undergo hip arthroscopy have a positive tone when discussing their procedure. Posts commonly focused on rehabilitation, ADLs, hospital or surgeon, and education. This analysis provides insight into patient perspectives toward hip arthroscopy., Competing Interests: One or more of the authors has declared the following potential conflict of interest or source of funding: J.R. is a paid speaker for and has received hospitality payments from Smith & Nephew. R.W.W. has received educational support from Smith & Nephew and Arthrex and hospitality payments from Smith & Nephew and Medical Device Business Services. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto.

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: Despite the many reports of injury rates in Major League Baseball (MLB), little is known about the epidemiology or impact of prior musculoskeletal injuries and surgical procedures among players entering the MLB draft., Purpose: To determine the (1) epidemiology of all musculoskeletal injuries and surgical procedures among players entering the MLB draft, (2) impact of injury or surgery on draft rank, (3) impact of injury or surgery on availability within the first 2 years of play in the MLB, and (4) impact of injury or surgery on performance., Study Design: Cohort study; Level of evidence, 3., Methods: We retrospectively reviewed 1890 medical records that were completed by MLB team physicians as preparticipation physical assessment prior to the draft from 2014 to 2018. Players were divided into 3 groups: noninjured, nonoperative, and operative. Draft status, overall draft rank, missed games, batting average, and earned run average for the first 2 seasons of MLB play were obtained for all available players. Players across all 3 groups were compared with linear, logistic, and beta regression models, controlling for age, position, injury status, and draft rank. Unadjusted differences among groups were assessed with 1-way analysis of variance., Results: Overall, 750 position players and 1140 pitchers were included, of whom 22.8% had no reported injury history; 48.8% reported injury treated nonoperatively; and 28.5% were treated operatively. The most common predraft injuries were elbow tendinitis (n = 312), ulnar collateral ligament injury (n = 212), and shoulder labral tear (n = 76). The most common predraft treatments were physical therapy (n = 922), ulnar collateral ligament reconstruction (n = 115), and fracture fixation (n = 69). Of the 1890 players, 719 were drafted and played for at least 2 years. No difference was found among noninjured, nonoperative, and operative groups in terms of draft rank, games missed, or performance. Players with a nonoperative injury had a decreased odds ratio of being drafted (0.738; P = .017)., Conclusion: More than half of the players entering the MLB reported a history of musculoskeletal injury requiring treatment, and the most commonly affected joints were the shoulder and elbow. Musculoskeletal history did not affect draft rank, short-term availability, or performance for MLB prospects., Competing Interests: One or more of the authors has declared the following potential conflict of interest or source of funding: K.L.S. has received consulting fees from Fidia Pharma, Horizon Pharma, and Stryker; educational support from Arthrex, Biomet, Maxx Orthopedics, and Stryker; and hospitality payments from Musculoskeletal Transplant Foundation. S.J.F. has received grants from Arthrex and DJO. M.S.S. has received educational support and consulting fees from Arthrex. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto.

Study Design: Retrospective cohort study., Objective: The objective of the present study was to establish evidence-based volume thresholds for surgeons and hospitals predictive of enhanced value in the setting of laminectomy., Summary of Background Data: Previous studies have attempted to characterize the relationship between volume and value; however, none to the authors' knowledge has employed an evidence-based approach to identify thresholds yielding enhanced value., Methods: In total, 67,758 patients from the New York Statewide Planning and Research Cooperative System database undergoing laminectomy in the period 2009 to 2015 were included. We used stratum-specific likelihood ratio analysis of receiver operating characteristic curves to establish volume thresholds predictive of increased length of stay (LOS) and cost for surgeons and hospitals., Results: Analysis of LOS by surgeon volume produced strata at: <17 (low), 17 to 40 (medium), 41 to 71 (high), and >71 (very high). Analysis of cost by surgeon volume produced strata at: <17 (low), 17 to 33 (medium), 34 to 86 (high), and >86 (very high). Analysis of LOS by hospital volume produced strata at: <43 (very low), 43 to 96 (low), 97 to 147 (medium), 148 to 172 (high), and >172 (very high). Analysis of cost by hospital volume produced strata at: <43 (very low), 43 to 82 (low), 83 to 115 (medium), 116 to 169 (high), and >169 (very high). LOS and cost decreased significantly (P < 0.05) in progressively higher volume categories for both surgeons and hospitals. For LOS, medium-volume surgeons handle the largest proportion of laminectomies (36%), whereas very high-volume hospitals handle the largest proportion (48%)., Conclusion: This study supports a direct volume-value relationship for surgeons and hospitals in the setting of laminectomy. These findings provide target-estimated thresholds for which hospitals and surgeons may receive meaningful return on investment in our increasingly value-based system. Further value-based optimization is possible in the finding that while the highest volume hospitals handle the largest proportion of laminectomies, the highest volume surgeons do not., Level of Evidence: 3.

Background: A recently introduced classification system of medial ulnar collateral ligament (UCL) tears accounting for location and severity has demonstrated high interobserver and intraobserver reliability, but little is known about its clinical utility., Purpose: The primary purpose of this study was to assess the relationship of the magnetic resonance imaging (MRI)-based classification system in predicting which athletes had success with nonoperative versus operative treatment after completing a standardized rehabilitation program. A secondary objective included return to play (RTP) and return to prior performance (RPP) analyses of baseball players., Study Design: Cohort study; Level of evidence, 3., Methods: After an a priori power analysis, 58 consecutive patients with UCL tears and a minimum of 2-year follow-up were retrospectively divided into 2 groups: those who successfully completed operative treatment and those who completed nonoperative treatment. The MRI-based classification stages accounting for UCL tear location and severity were compared between the nonoperative and operative groups. A subanalysis for baseball players, including RTP and RPP, was performed., Results: A total of 58 patients (40 baseball players [34 pitchers]) met inclusion criteria. Of these patients 35 (32 baseball players [27 pitchers]) underwent surgery, and 23 (8 baseball players [7 pitchers]) underwent nonoperative management. No patients in the nonoperative arm crossed over to surgery after completing the rehabilitation program. Patients with distal tears (odds ratio, 48.0; P = .0004) and complete tears (odds ratio, 5.4; P = .004) were more likely to undergo surgery. Baseball players, regardless of position, were confounding determinants of operative management, although there was no difference in RTP and RPP between treatment arms., Conclusion: A 6-stage MRI-based classification system addressing UCL tear location and severity may help early decision making, as patients likely to fail nonoperative treatment have complete, distal tears, whereas those with proximal, partial tears may be more amenable to nonoperative management., Competing Interests: One or more of the authors has declared the following potential conflict of interest or source of funding: S.J.F. has received research support from Arthrex and DJO. L.D.F. has received consulting fees from MTF and Zimmer Biomet. M.S.S. has received educational support and speaking fees from Arthrex. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto.

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: Patients receiving a total knee arthroplasty (TKA) who fail to meet inpatient criteria for outpatient physical therapy or an acute rehabilitation facility are increasingly being discharged to skilled nursing facilities (SNFs). However, in some facilities, postoperative care and therapy may be suboptimal. In order to recognize the circumstances predisposing patients to a SNF discharge and quantify perioperative risks, we used a nationwide TKA database to compare those who were and were not discharged to SNFs with respect to: (I) patient and hospital characteristics; (II) comorbidities; (III) lengths of stay (LOS); and (IV) inpatient complications., Methods: The National Inpatient Sample database was queried for all individuals who received primary TKA (ICD-9-CM 81.54) between January 1 st , 2009 and December 31st, 2013, yielding a total of 3,218,419 patients. Discharge disposition was readily identifiable, and the SNF patients numbered 403,575 (12.5%) vs. 2,814,574 discharged to home or a non-SNF setting (87.5%). A multi-level logistic regression analysis was conducted using patient and hospital specific factors as predictor variables in order to see if differences existed between the two cohorts. A two-tailed P value was set as the threshold for statistical significance., Results: Patients discharged to SNFs post-TKA were older (mean, 72 vs. 65 years, P<0.001), more often female [odds ratio (OR) 1.74, P<0.001], black (OR 1.246, P<0.001), from the South (OR 1.856, P<0.001), and had various comorbidities including: preexisting psychoses (OR 1.703, P<0.001), history of drug abuse (OR 1.682, P<0.001), neurological disorders (OR 1.359, P<0.001), and depression (OR 1.334, P<0.001). The mean LOS for TKA patients discharged to SNFs was 17% longer (P<0.001). Patients discharged to SNFs were more likely to endure inpatient medical complications (OR 1.3, P<0.001), specifically pulmonary congestion or edema., Conclusions: Patients discharged to SNF had specific demographic characteristics and risk factors, increased LOS, more frequent inpatient medical complications, and greater hospital costs. Knowledge of these risk factors may be critical from the perspective of the new value-based reimbursement system for orthopaedic surgeon to intervene early and appropriately select the patients likely and capable of completing the rigorous postoperative TKA rehabilitation., Competing Interests: Conflicts of Interest: The authors have no conflicts of interest to declare.

Background: The outcomes of anatomic total shoulder arthroplasty (TSA) for glenohumeral osteoarthritis (OA) have been extensively reported in the literature. However, there is a paucity of short-term outcomes data on perioperative and post-operative outcomes in patients receiving TSA for either glenoid or humeral head osteonecrosis (ON). We compared the short-term outcomes of TSA performed on patients who had glenohumeral OA and those who had ON. Specifically, we compared: (I) demographics; (II) length-of-stay (LOS); and (III) 30-day postoperative complication rates., Methods: Data from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) was used to select 7,066 patients with a diagnosis of glenohumeral OA or ON who underwent TSA between January 1 st , 2008 and December 31 st , 2015. Propensity score matching was performed based on age, sex, race, and body mass index (BMI) with a 3:1 ratio of OA to ON patients. As a result, 387 patients who had a primary diagnosis of OA were compared with 129 patients who had ON. Analyses addressed perioperative complications and hospital disposition factors-including LOS and 30-day post-operative complications-using bivariate and logistic regression models., Results: Overall complication rates were low in both groups, at 6.1% and 5.4% for the ON and OA cohorts respectively. The ON cohort had more comorbidities, including higher Charlson/Devo and American Society of Anesthesiologists (ASA) scores (P<0.05). Hospital disposition factors were not statistically different between the 2 groups. While not statistically significant, the ON cohort had a longer average (21.1% longer, P>0.05), an increased risk for developing any complication [odds ratio (OR) =2.07; 95% confidence interval (CI), 0.94 to 4.57; P=0.07], and a 134% higher risk for developing a minor complication compared to the OA (OR =2.34; 95% CI, 1.01 to 5.42; P=0.047)., Conclusions: Although the patient populations who develop ON or shoulder OA have differences, there are not statistically or clinically meaningful differences in the short-term outcomes after anatomic TSA. Future studies are required to examine if differences exist in the long-term follow-up between the two groups., Competing Interests: Conflicts of Interest: The authors have no conflicts of interest to declare.

Background: The short-term outcomes of concussions within Major League Baseball (MLB) warrant further consideration beyond a medical standpoint given that performance, career, and financial data remain unknown. The perception of this injury directly affects decision making from the perspective of both player and franchise., Purpose: To evaluate the effect of concussion on MLB players by (1) establishing return-to-play (RTP) time after concussion; (2) comparing the career length and performance of players with concussion versus those who took nonmedical leave; and (3) analyzing player financial impact after concussion., Study Design: Cohort study; Level of evidence, 3., Methods: Contracts, transactions, injury reports, and performance statistics from 2005 to 2017 were analyzed by comparing matched players who sustained a concussion versus those who took nonmedical leave. Of the 4186 eligible MLB players, 145 sustained concussions resulting in the activation of concussion protocol and 538 took nonmedical leave. RTP time was recorded. Career length was analyzed in reference to an experience-based stratification of full seasons remaining after the concussion. Changes in player performance and salary before and after concussion were compared with the same parameters for players who took nonmedical leave., Results: The mean RTP time was 26 days (95% CI, 20-32 days) for athletes with concussion and 8 days (95% CI, 6-10 days) for those who took nonmedical leave. Athletes with concussion had a mean of 2.8 full seasons remaining, whereas athletes who took nonmedical leave had 3.1 seasons remaining ( P = .493). The probability of playing in the MLB after concussion compared with the nonmedical leave pool was not significantly lower ( P = .534, log-rank test; hazard ratio, 1.108). Postconcussion performance decreased significantly in position players, including a lower batting average and decreased on-base percentage in the players with concussion compared with those returning from nonmedical leave. Players who sustained a concussion lost a mean of US$654,990 annually compared with players who took nonmedical leave., Conclusion: This study of the short-term outcomes after concussion in limited-contact MLB athletes demonstrates that concussions may not decrease career spans but may result in decreased performance in addition to financial loss when compared with matched controls who took nonmedical leave. In sports such as baseball that are not subject to repetitive head trauma, career spans may not decrease after a single concussive event. However, sentinel concussions have deleterious short-term effects on performance and compensation among MLB players., Competing Interests: One or more of the authors has declared the following potential conflicts of interest or source of funding: S.J.F. has received grants from DJO and Arthrex. M.A.M. is a consultant for Stryker, Pacira, Encore Medical, Mallinckrodt, OrthoSensor, Medtronic Xomed, Think Surgical, Merz Pharmaceuticals, and Ferring Pharmaceuticals; has received educational support from Stryker, Ethicon, and OrthoSensor; and receives royalties from Microport Orthopedics. L.D.F. is a consultant for MTF and Zimmer Biomet. M.S.S. has received educational support from and is a paid speaker/presenter for Arthrex. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto.

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.)

Peroxide is a strong oxidizing agent and disinfectant frequently used in orthopedic surgery. The authors conducted a systematic literature review of peroxide in orthopedic surgery, evaluating use, complications, efficacy, and appropriate concentrations. One hundred seventy-five reports were identified, with 24 being eligible for analysis. Orthopedic surgeons used peroxide for irrigation and bacterial reduction in various procedures. Complications included cytotoxicity, allergic reactions, suture damage, and inflammation. Use of the standard concentration of 3% peroxide and standard time in situ are without evidence. Laboratory studies suggest that diluted concentrations retain the benefit of bacterial decolonization without increasing the risk for complications. [Orthopedics. 2018; 41(6):e756-e764.]., (Copyright 2018, SLACK Incorporated.)

Many studies have focused on the long-term impact of concussions in professional sports, but few have investigated short-term effects. This study examines concussion effects on individual players in the National Hockey League (NHL) by assessing career length, performance, and salary. Contracts, transactions, injury reports, and performance statistics from 2008-17 were obtained from the official NHL online publication. Players who sustained a concussion were compared with the 2008-17 non-concussed player pool. Career length was analyzed using Kaplan-Meier survival curves and stratification of player age, experience, and longevity. Player performance and salary changes were evaluated between the years before versus after concussion. Performance and salary changes were compared against non-concussed NHL athletes before/after their career midpoints. Of the 2194 eligible NHL players in the 9-year period, 309 sustained 399 concussions resulting in injury protocol. The probability of playing a full NHL season post-concussion was significantly decreased compared with the non-concussed pool (p < 0.05), specifically 65.0% versus 81.2% at 1 year into a player's career, 49.8% versus 67.4% at 2 years, and 14.6% versus 43.7% at 5 years. Performance was reduced at all non-goalie positions post-concussion (p < 0.05). Players scored 2.5 points/year less following a concussion. The total annualized financial impact from salary reductions after 1 concussion was $57.0 million, with a decrease of $292,000 per year in contract value per athlete. This retrospective study demonstrates that NHL concussions resulting in injury protocol activation lead to shorter career lengths, earnings reductions, and decreased performance when compared with non-concussed controls.

Improving value in shoulder arthroplasty has gained increasing importance as procedure volume increases. To enhance the value of shoulder arthroplasty, an improvement of outcomes or a decrease in associated costs must occur. With the recent shift to a value-based care delivery model, analysis of the effects of surgical volume presents an opportunity to improve outcomes and reduce costs in shoulder arthroplasty. There are multiple reports in the literature expanding on the relationship between increased surgeon and hospital procedure volume and increased value for shoulder arthroplasty, by way of improved outcomes or decreased cost. This article highlights these studies., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: Cycling injuries are increasing, particularly among elite athletes during major events such as the Tour de France. Many athletes miss considerable time from sport and require surgical intervention. Little is known about the epidemiology of injuries to guide practice participation strategies, return-to-competition expectations, and injury prevention protocols., Purpose: To evaluate the injury epidemiology, operative incidence, and return-to-competition timeline among all elite cyclists participating in the 21-stage Tour de France race over a span of 8 years., Study Design: Descriptive epidemiological study., Methods: All injuries sustained during 1584 unique rides by cyclists participating in the Tour de France from 2010 to 2017 were evaluated. In the absence of an established Tour de France injury database, demographic, injury, surgical, and return-to-competition details from all athletes who withdrew because of a traumatic injury were retrospectively compiled using publicly available data, which were cross-referenced for validity. The inclusion criterion consisted of any cyclist who withdrew from the Tour de France because of an injury; cyclists who withdrew for noninjury reasons were excluded. Independent variables included injury demographics, missed days, and whether the injury required surgery. Injury prevalence, relative frequency distributions, and sample proportions were dependent metrics for this investigation., Results: Among the 1584 cycling entries evaluated over the 8-year study period, there were 259 cyclists (16%) who withdrew (17 cyclists/year). A total of 138 withdrawals were caused by acute trauma, 49% of which were fractures (n = 67), which represented the most common reason for withdrawal. A total of 29 (43%) cyclists with fractures underwent surgery. The most commonly fractured bones were the clavicle (n = 21), followed by the wrist (n = 6), hand (n = 5), femur (n = 5), humerus (n = 5), and ribs (n = 5). Cyclists who underwent operative fracture fixation had a longer time between the injury and their next race compared with those who did not undergo surgery (77 vs 44 days, respectively; P = .065)., Conclusion: The most common injury leading to withdrawal from the Tour de France over the study period was acute fracture, comprising 49% of all injuries. Almost half of the cyclists with fractures underwent surgery. The clavicle was the most commonly fractured bone. For cyclists who underwent operative treatment of their fractures, return to competition was more than 1 month longer than for those who did not. A prospective database is warranted to catalog injuries among these elite cyclists., Competing Interests: One or more of the authors has declared the following potential conflict of interest or source of funding: M.S.S. has received educational support from Arthrex and is a paid speaker/presenter for Arthrex. L.D.F. is a consultant for MTF and Zimmer Biomet. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto.