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Introduction: Matching into an orthopaedic surgery residency consistently reinforces a competitive landscape, challenging the applicants and programs. A group of orthopaedic surgery residency programs implemented video prompts asking applicants to respond to a standardized question by video recording. Assessing the impact of this video on the decision to offer an interview can help guide programs and applicants through the interview process., Methods: Twenty residency applications to one institution requiring video prompts were randomly selected and deidentified. Thirteen experienced faculty from various orthopaedic surgery programs served as applicant reviewers. The reviewers evaluated the electronic residency application service (ERAS) application and determined whether they would grant the applicant an interview ("no," "maybe," or "yes") before and after watching the video prompt. The reviewer also scored the impact of the applicant's dress, facial presentation, and video background distractions on their evaluation of the video. Multivariable logistic regressions were conducted using a group of applicants where interview status was not impacted by the video compared with the group of applicants where the interview status changed after video review. An alpha value of 0.05 was used to define significance., Results: The video prompt impacted the decision to offer an interview 29.3% of the time; 15.8% were switched from "yes" or "maybe" to "no" and 13.5% were switched from "no" or "maybe" to "yes." For the positively impacted applicants, facial presentation score was significantly higher ( P = 0.005). No recorded variables were associated with decreased chance of interview., Discussion: Video prompts impacted the decision to offer interviews to orthopaedic surgery applicants approximately one-third of the time, with a similar number of applicants being positively and negatively impacted. Facial presentation score was associated with increased chance of interview, and no variables were associated with decreased chance of interview. Thus, the answer to the videos presumably negatively impacted applicants., (Copyright © 2024 by the American Academy of Orthopaedic Surgeons.)

Introduction: Postoperative fracture site infection can lead to notable patient morbidity, increase cost of care, and further contribute to healthcare disparities globally. Dogma suggests surgical blades as a vehicle for introducing bacteria into the surgical site; however, there is a paucity of literature to support this claim. This study uses advanced DNA sequencing to detect bacterial DNA on surgical blades used in upper extremity fracture surgeries., Methods: This was a prospective study, conducted at a high-volume level 1 trauma center. All acute, closed upper extremity fractures requiring surgical stabilization were consecutively enrolled in a prospective fashion. The primary end point was the presence of bacterial DNA on the surgical blade using next-generation sequencing (NGS). At the time of surgery, two blades were sterilely opened. One blade served as the control while the other was used for the initial skin incision. Two negative control blades were opened directly into a sterile container. Two positive control blades were used for skin incision through known infections. All samples were sent for NGS analysis., Results: Forty patients were enrolled in this study. The median age was 33.5 years, and 30% were female; the median body mass index was 26.52. Humerus fractures were the most common injury (N = 17, 42.5%), followed by clavicle fractures (13, 32.5%) and radius/ulna fractures (10, 25.0%). NGS analysis revealed no contamination of test blades used for skin incision. Three control blades tested positive for bacterial DNA. Negative control blades tested negative for bacterial DNA (0/2); the positive control blades resulted positive for bacterial DNA contamination (2/2)., Conclusion: Surgical blades used for skin incision in the upper extremity are not contaminated with bacterial DNA as analyzed by NGS. This finding challenges previous surgical dogma regarding surgical blade contamination and supports that the same surgical blade can safely be used for deeper dissection., Level of Evidence: Level II study: IRB approval-IRB#848938., (Copyright © 2024 by the American Academy of Orthopaedic Surgeons.)

Background: Periprosthetic fractures can be devastating complications after total joint arthroplasty (TJA). The management of periprosthetic fractures is complex, spanning expertise in arthroplasty and trauma. The purpose of this study was to examine and project trends in the operative treatment of periprosthetic fractures in the United States., Methods: A large, public and private payer database was queried to capture all International Classification of Diseases, Tenth Revision (ICD-10) diagnosis codes for periprosthetic femoral and tibial fractures. Statistical models were created to assess trends in treatment for periprosthetic fractures and to predict future surgical rates. An alpha value of 0.05 was used to assess significance. A Bonferroni correction was applied where applicable to account for multiple comparisons., Results: In this study, from 2016 to 2021, 121,298 patients underwent surgical treatment for periprosthetic fractures. There was a significant increase in the total number of periprosthetic fractures. The incidence of periprosthetic hip fractures rose by 38% and that for periprosthetic knee fractures rose by 73%. The number of periprosthetic fractures is predicted to rise 212% from 2016 to 2032. There was a relative increase in open reduction and internal fixation (ORIF) compared with revision arthroplasty for both periprosthetic hip fractures and periprosthetic knee fractures., Conclusions: Periprosthetic fractures are anticipated to impose a substantial health-care burden in the coming decades. Periprosthetic knee fractures are predominantly treated with ORIF rather than revision total knee arthroplasty (TKA), whereas periprosthetic hip fractures are predominantly treated with revision total hip arthroplasty (THA) rather than ORIF. Both periprosthetic knee fractures and periprosthetic hip fractures demonstrated increasing trends in this study. The proportion of periprosthetic hip fractures treated with ORIF relative to revision THA has been increasing., Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence., Competing Interests: Disclosure: No external funding was received for this work. The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJS/H943)., (Copyright © 2024 by The Journal of Bone and Joint Surgery, Incorporated.)

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

Background: The surgical management of complications after total hip arthroplasty (THA) necessitates accurate identification of the femoral implant manufacturer and model. Automated image processing using deep learning has been previously developed and internally validated; however, external validation is necessary prior to responsible application of artificial intelligence (AI)-based technologies., Methods: We trained, validated, and externally tested a deep learning system to classify femoral-sided THA implants as one of the 8 models from 2 manufacturers derived from 2,954 original, deidentified, retrospectively collected anteroposterior plain radiographs across 3 academic referral centers and 13 surgeons. From these radiographs, 2,117 were used for training, 249 for validation, and 588 for external testing. Augmentation was applied to the training set (n = 2,117,000) to increase model robustness. Performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated., Results: The training and testing sets were drawn from statistically different populations of implants (P < .001). After 1,000 training epochs by the deep learning system, the system discriminated 8 implant models with a mean area under the receiver operating characteristic curve of 0.991, accuracy of 97.9%, sensitivity of 88.6%, and specificity of 98.9% in the external testing dataset of 588 anteroposterior radiographs. The software classified implants at a mean speed of 0.02 seconds per image., Conclusion: An AI-based software demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents responsible and meaningful clinical application of AI with immediate potential to globally scale and assist in preoperative planning prior to revision THA., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Competing Interests: The author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research ® editors and board members are on file with the publication and can be viewed on request.

Background: Instrumented posterior lumbar spinal fusion (IPLSF) has been demonstrated to contribute to instability following total hip arthroplasty (THA). It is unclear whether a supine direct anterior (DA) approach reduces the risk of instability., Methods: A retrospective review of 1,773 patients who underwent THA through either a DA approach or a posterior approach at our institution over a 7-year period was performed. Radiographic and chart reviews were then used to identify our primary group of interest comprised of 111 patients with previous IPLSF. Radiographic review, chart review, and phone survey was performed. Dislocation rates in each approach group were then compared within this cohort of patients with IPLSF., Results: Within the group of patients with IPLSF, 33.3% (n = 37) received a DA approach while 66.6% (n = 74) received a posterior approach. None of the 9 total dislocations in the DA group had IPLSF, whereas 4 of the 16 total dislocations in the posterior approach group had IPLSF (P = .78). When examining the larger group of patients, including those without IPLSF, patients undergoing a DA approach had a lower BMI and were likely have a smaller head size implanted (P < .001 for both). Using Fischer's exact test, fusion was associated with dislocation in the posterior approach group (P < .01), whereas fusion was not associated with dislocation in the anterior approach group (P = 1.0)., Conclusions: While there was no significant difference in dislocation rates between posterior and anterior approach groups, in patients with IPLSF, the anterior approach had a lower percentage of dislocation events compared to the posterior approach., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The purpose of this study was to compare (1) operative time, (2) in-hospital pain scores, (3) opioid medication use, (4) length of stay (LOS), (5) discharge disposition at 90-day postoperative, (6) range of motion (ROM), (7) number of physical therapy (PT) visits, (8) emergency department (ED) visits, (9) readmissions, (10) reoperations, (11) complications, and (12) 1-year patient-reported outcome measures (PROMs) in propensity matched patient cohorts who underwent robotic arm-assisted (RA) versus manual total knee arthroplasty (TKA). Using a prospectively collected institutional database, patients who underwent RA- and manual TKA were the nearest neighbor propensity score matched 3:1 (255 manual TKA:85 RA-TKA), accounting for various preoperative characteristics. Data were compared using analysis of variance (ANOVA), Kruskal-Wallis, Pearson's Chi-squared, and Fisher's exact tests, when appropriate. Postoperative pain scores, opioid use, ED visits, readmissions, and 1-year PROMs were similar between the cohorts. Manual TKA patients achieved higher maximum flexion ROM (120.3 ± 9.9 versus 117.8 ± 10.2, p  = 0.043) with no statistical differences in other ROM parameters. Manual TKA had shorter operative time (105 vs.113 minutes, p  < 0.001), and fewer PT visits (median [interquartile range] = 10.0 [8.0-13.0] vs. 11.5 [9.5-15.5] visits, p  = 0.014). RA-TKA had shorter LOS (0.48 ± 0.59 vs.1.2 ± 0.59 days, p  < 0.001) and higher proportion of home discharges ( p  < 0.001). RA-TKA and manual TKA had similar postoperative complications and 1-year PROMs. Although RA-TKA patients had longer operative times, they had shorter LOS and higher propensity for home discharge. In an era of value-based care models and the steady shift to outpatient TKA, these trends need to be explored further. Long-term and randomized controlled studies may help determine potential added value of RA-TKA versus manual TKA. This study reflects level of evidence III., Competing Interests: M.A.M. reports personal fees from CyMedica, Flexion Therapeutics , DJ Orthopaedics, Johnson and Johnson, Ongoing Care Solutions, Orthosensor, Pacira, Peerwell, Performance Dynamics, Pfizer, Stryker, Skye Biologics, TissueGene, and nonfinancial support from U.S. Medical Innovations, outside the submitted work; consultant for Stryker. N.W.M. reports personal fees from Stryker Orthopaedics and KCI Acelity, outside the submitted work. T.G.M. reports personal fees from Zimmer Biomet, outside the submitted work. All the other authors report no conflict of interest., (Thieme. All rights reserved.)

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular ("AI/machine learning"), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.Cite this article: Bone Joint J  2022;104-B(12):1292-1303.

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

Background: Standard preoperative protocols in total joint arthroplasty utilise the international normalised ratio (INR) to determine patient coagulation profiles. However, the relevance of preoperative INR values in joint arthroplasty remains controversial. Therefore, we examined (1) the relationship between preoperative INR values and various outcome measures, including, but not limited to: surgical site complications, medical complications, bleeding, number of readmissions, and mortality. Additionally, we sought to determine (2) specific INR values associated with these complications and (3) cutoff INR levels which correlated with specific outcomes. We additionally applied these analyses to (4) examine the relationship between INR and length-of-stay (LOS)., Methods: The American College of Surgeons National Surgical Quality Improvement Program database (ACS-NSQIP) was queried for rTHA procedures performed between 2006 and 2017. INR ranges were used to stratify cohorts: ⩽1.0, 1.0-⩽1.25, 1.25-⩽1.5, >1.5. INR values were determined using receiver operating characteristics (ROC) curves for each outcome of interest. Optimal cutoff INR values for each outcome were then obtained using univariate/multivariate models. 2012 patients who underwent rTHA met inclusion criteria., Results: Patients with progressively higher INR values had a significantly different risk of mortality within 30 days ( p = 0.005), bleeding requiring transfusion ( p < 0.001), sepsis ( p = 0.002), stroke ( p < 0.001), failure to wean from ventilator within 48 hours ( p = 0.001), readmission ( p = 0.01), and hospital length of stay ( p < 0.001). Similar results were obtained when utilising optimal INR cutoff values. When correcting for other factors, the following poor outcomes were significantly associated with the respective INR cutoff values (Estimate, 95% CI, p- value): LOS >4 days (1.67, 1.34-2.08, p < 0.001), bleeding requiring transfusion (1.65, 1.30-2.09, p < 0.001), sepsis (2.15, 1.11-4.17, p = 0.022), and any infection (1.82, 1.01-3.29, p = 0.044)., Conclusions: Our analysis illustrates a direct relationship between specific preoperative INR levels and poor outcomes following rTHA, including increased LOS, transfusion requirements and infection. Therefore, current INR guideline targets may need to be re-examined when optimising patients for revision arthroplasty.

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.

Purpose: Recently, the Centers for Medicare and Medicaid have announced the decision to review "potentially misvalued" Current Procedural Terminology codes, including those for primary total hip arthroplasty (THA). While recent studies have suggested that THA operative times have remained stable in recent years, there is an absence of information regarding how operative times are expected to change in the future. Therefore, the purpose of our analysis was to produce 2- and 10-year prediction models developed from contemporary operative time data., Methods: Utilizing the American College of Surgeons National Surgical Quality Improvement patient database, all primary THA procedures performed between January 1st, 2008 and December 31st, 2017 were identified (n = 85,808 THA patients). Autocorrelation fit significance was determined through Box-Ljung lack of fit tests. Time series stationarity was evaluated using augmented Dickey-Fuller tests. After adjusting non-stationary time series for seasonality-dependent changes, 2-year and 10-year operative times were predicted using Autoregressive integrated moving average forecasting models., Results: Our models indicate that operative time will continue to remain stable. Specifically, operative time for ASA Class 2 is projected to fall within 1 min of the previously calculated weighted mean. Additionally, ASA Class 3 projections fall within 3 min of this value., Conclusion: Operative time will remain within 3 min of the most recently reported mean up to the year 2027. Therefore, our findings do not support lowering physician compensation based on this metric. Future analyses should evaluate if operative times adjust over in light of changing patient demographics and alternative reimbursement models., (© 2021. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.)

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.

Recently, the Centers for Medicare & Medicaid Services announced its decision to review "potentially misvalued" Current Procedural Terminology (CPT) codes, including those for primary total knee arthroplasty (TKA). CPT 27447 is being reevaluated to determine contemporary relative value units for work value, with operative time considered a primary factor in this revaluation. Despite broader indications for TKA, including extension of the procedure to more complex patient populations, it is unknown whether operative times may remain stable in the future. Therefore, the purpose of this study was to specifically evaluate future trends in TKA operative times across a large sample from a national database. The American College of Surgeons National Surgical Quality Improvement Project database was queried from January 1, 2008 to December 31, 2017 to identify 286,816 TKAs using the CPT code 27447. Our final analysis included 140,890 TKAs. Autoregressive integrated moving average forecasting models were built to predict 2- and 10-year operative times. While operative times were significantly different between American Society of Anesthesiologists (ASA) classes 1 and 2 ( p  = 0.035), there were not enough patients in ASA class 1 to perform rigorous inference. Additionally, operative times were not significantly different between ASA classes 3 and the combined ASA classes 4 and 5 cohort ( p  = 0.95). Therefore, we were only able to perform forecasts for ASA classes 2 and 3. Operative time was found to be nonstationary for both ASA class 2 ( p  = 0.08269) and class 3 ( p  = 0.2385). As a whole, the projection models indicated that operative time will remain within 2 minutes of the present operative time, up to the year 2027. Our projections indicate that operative times will remain stable over the next decade. This suggests that there is a lack of evidence for reducing the valuation of CPT code 27477 based on intraservice time for TKA. Further study should examine operative time trends in the setting of evolving alternative payment models, increasing patient complexity, and governmental restrictions., Competing Interests: A.F.K. reports other from AAOS, other from American Association of Hip and Knee Surgeons, other from BMC Musculoskeletal Disorders, personal fees from Corin U.S.A., personal fees and other from DePuy, A Johnson & Johnson Company, personal fees from Heraeus Medical, other from Innomed, other from Johnson & Johnson, other from Orthofix, Inc, other from Pacira Pharmaceuticals, other from Procter & Gamble, other from Zimmer, outside the submitted work., (Thieme. All rights reserved.)

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.)

The purpose of this study was to examine the relationship between elevated preoperative international normalized ratio (INR) and (1) mortality, (2) postoperative bleeding, and (3) other postoperative complications in a national cohort of patients who underwent revision total knee arthroplasty (rTKA). The American College of Surgeons National Surgical Quality Improvement Program was queried for rTKA procedures conducted between 2006 and 2017. Cohorts were based on INR ranges: <1, 1 < INR ≤ 1.25, 1.25 < INR ≤ 1.5, and >1.5. Univariate/multivariate statistics were calculated to analyze associations between INR value and designated covariates. These statistics were additionally applied to optimal cutoff values of INR calculated using a receiver operating characteristics curve. The final cohort consisted of 1,676 patients. Progressively higher INR values were associated with an increased risk of mortality within 30 days ( p  < 0.006), bleeding requiring transfusion ( p  < 0.001), sepsis ( p  < 0.001), return to the operating room (Odds Ratio [OR], p  = 0.011), reintubation ( p  < 0.001), pneumonia ( p  < 0.001), failure to wean from mechanical ventilation ≤48 hours ( p  < 0.001), acute renal failure ( p  = 0.001), and hospital length of stay (LOS). Statistically significant associations were similarly seen when calculated optimal INR values were used. Optimal INR turn point was found to be associated with a significant increased risk of long LOS (optimal INR = 1.03, OR: 1.7, 95% confidence interval [CI]: 1.33-2.18; p  < 0.001) and a significant decreased risk of bleeding requiring transfusion (INR = 1.005, OR: 0.732, 95% CI: 0.681-0.786; p  < 0.001). High preoperative INR values were independently and significantly associated with an increased risk of multiple postoperative complications. Current guidelines for INR <1.5 should be reassessed for patients undergoing rTKA., Competing Interests: A.F.K. reports the following disclosures: research support (Zimmer Biomet, DePuy Synthes), paid presenter or speaker (Corin, DePuy Synthes, Heraeus Medical, and Zimmer Biomet), paid consultant (Pacira Pharmaceuticals, Heraeus Medical, DePuy Synthes, and Zimmer Biomet), stock or stock options (Zimmer Biomet, Johnson & Johnson, and Procter & Gamble), IP royalties (Innomed), board or committee member (AAOS and AAHKS), and editorial or governing board (BMC Musculoskeletal Disorders). All the other author reports no conflict of interest., (Thieme. All rights reserved.)

Study Design: This was a retrospective consecutive cohort analysis., Objective: This study aimed to examine the association between commonly prescribed medications and outcomes following posterior lumbar spine surgery., Summary of Background Data: Postoperative complications and prolonged length of stay significantly increase costs following posterior lumbar spine surgery and worsen patient outcomes. To control costs and complications, providers should focus on modifiable risk factors, such as preoperative medications. Antihypertensive and anticholinergic drugs are among the most commonly prescribed medications but can carry significant risks in the perioperative period., Materials and Methods: This study was a retrospective cohort analysis of patients undergoing posterior lumbar spine surgery from January 2014 through December 2015 at a large tertiary care center. The variable selection followed by multivariable logistic and negative binomial regressions were performed. An α threshold of 0.0056 was used for significance after correction for multiple comparisons. A secondary analysis was performed to evaluate confounding or effect modifying variables., Results: This study included 1577 patients. Postoperative urinary retention risk was increased in patients taking loop diuretics. Acute kidney injury risk was increased for patients on nondihydropyridine calcium-channel blockers. Surgical site infection risk was increased for patients on aldosterone receptor blockers. Urinary tract infection risk was increased for patients on anticholinergics for urinary incontinence. Length of stay was decreased for patients on angiotensin II antagonists and angiotensin-converting enzyme inhibitors., Conclusion: A care path should be established in the perioperative period for patients who are deemed to be at higher risk due to medication status to either modify medications or improve postoperative monitoring., Level of Evidence: Level III., Competing Interests: M.P.S. receives royalties from Zimmer Biomet and acts in a consulting role to Globus and Intellirod. The remaining authors declare no conflict of interest., (Copyright © 2020 Wolters Kluwer Health, 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: Opioid use and public insurance have been correlated with worse outcomes in a number of orthopaedic surgeries. These factors have not been investigated with anterior cruciate ligament reconstruction (ACLR)., Purpose/hypothesis: To evaluate if narcotic use, physical therapy location, and insurance type are predictors of patient-reported outcomes after ACLR. It was hypothesized that at 1 year postsurgically, increased postoperative narcotic use would be associated with worse outcomes, physical therapy obtained within the authors' integrated health care system would lead to better outcomes, and public insurance would lead to worse outcomes and athletic activity., Study Design: Cohort study; Level of evidence, 2., Methods: All patients undergoing unilateral, primary ACLR between January 2015 and February 2016 at a large health system were enrolled in a standard-of-care prospective cohort. Knee injury and Osteoarthritis Score (KOOS) and the Hospital for Special Surgery Pediatric-Functional Activity Brief Scale (HSS Pedi-FABS) were collected before surgery and at 1 year postoperatively. Concomitant knee pathology was assessed arthroscopically and electronically captured. Patient records were analyzed to determine physical therapy location, insurance status, and narcotic use. Multivariable regression analyses were used to identify significant predictors of the KOOS and HSS Pedi-FABS score., Results: A total of 258 patients were included in the analysis (mean age, 25.8; 51.2% women). In multivariable regression analysis, narcotic use, physical therapy location, and insurance type were not independent predictors of any KOOS subscales. Public insurance was associated with a lower HSS Pedi-FABS score (-4.551, P = .047) in multivariable analysis. Narcotic use or physical therapy location was not associated with the HSS Pedi-FABS score., Conclusion: Increased narcotic use surrounding surgery, physical therapy location within the authors' health care system, and public versus private insurance were not associated with disease-specific KOOS subscale scores. Patients with public insurance had worse HSS Pedi-FABS activity scores compared with patients with private insurance, but neither narcotic use nor physical therapy location was associated with activity scores. Physical therapy location did not influence outcomes, suggesting that patients be given a choice in the location they received physical therapy (as long as a standardized protocol is followed) to maximize compliance., Competing Interests: One or more of the authors has declared the following potential conflict of interest or source of funding: Research reported in this publication was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health (award K23AR066133), which supported a portion of M.H.J.’s professional effort. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. L.D.F. has received consulting fees from Zimmer Biomet and hospitality payments from Musculoskeletal Transplant Foundation. M.H.J. is on the scientific advisory board for Samumed. A.A.M. has received educational support from Rock Medical; consulting fees from Arthrosurface, Amniox Medical, Linvatec, Stryker, and Trice; speaking fees from Trice; royalties from Arthrosurface, Zimmer Biomet, and Wolters Kluwer; and hospitality payments from Arthrex, DJO, and Smith & Nephew; and has stock/stock options in Arthrosurface and Trice. R.D.P. has received royalties from Zimmer Biomet and hospitality payments from Smith & Nephew and Musculoskeletal Transplant Foundation. J.T.R. has received consulting fees from Smith & Nephew. P.S. has received educational support from Rock Medical; consulting fees from Arthrex, DJO, and DePuy; speaking fees from Arthrex; and hospitality payments from Musculoskeletal Transplant Foundation. G.S. has received royalties from Oberd. K.P.S. has received research support from Smith & Nephew and DJO; consulting fees from NFL, Cytori, Mitek, Samumed, and Flexion Therapeutics; hospitality payments from DePuy and Biosense Webster; and royalties from Oberd. J.S.W. has received educational support 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) 2021.)

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.

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.)

Background: With the recent reevaluation of surgeon reimbursement for total hip arthroplasty (THA) by the Centers for Medicare and Medicaid Services, there is increasing need for information regarding trends in operative time. While single-institutional analyses exist, there is a lack of large-scale, nationally representative, multi-institutional data. Therefore, the purpose of our study is to (1) evaluate past/present operative time trends for THA and (2) investigate factors influencing operative times from a 10-year, large multi-institutional database., Methods: All primary THAs conducted between 2008 and 2018 were queried using Current Procedural Terminology code 27130 from the American College of Surgeons-National Surgical Quality Improvement Program database, yielding 157,574 patients. Operative time, demographics, and comorbidity data were collected and analyzed. Multivariable linear models were created, and trend analyses were used where appropriate., Results: Median operative time was 87 minutes. Operative time was stable across included study years, with all calculated values within 5 minutes of the median (range, 86-92 minutes). Operative time was statistically stable over the last 3 years (P = .121). Age, body mass index, resident involvement, modified Charlson comorbidity index, and preoperative laboratory values influenced operative time (P < .001). Length of stay, readmission, superficial wound infection, and sepsis decreased over the study period. Nonelective procedures were statistically longer than elective (P < .0001)., Conclusion: While numerous factors influence the duration of THA, this study found that THA operative time has remained stable in recent years. Therefore, revaluation for THA based on intraservice time is not supported. Future analyses should continue to analyze factors that influence operative time in order to ensure patient safety and maintain positive outcomes., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objectives: In adolescent idiopathic scoliosis (AIS) patients undergoing posterior spinal instrumented fusion (PSIF), we aimed to answer these questions: (1) is there a difference in postoperative urinary retention (UR) rates among patients who had removal of their Foley catheters before vs. after discontinuation of epidural analgesia (EA)? (2) Can the timing of Foley catheter removal be an independent risk factor for postoperative UR requiring recatheterization? (3) Is there an incurred cost related to treating UR?, Study Design: Retrospective cohort., Background: EA has been widely used for postoperative pain control after PSIF for AIS. In these patients, removing the Foley catheter, inserted for intraoperative monitoring of urine output, is indicated in the early postoperative period. However, a controversy exists as to whether it should be removed before or after the EA has been discontinued., Methods: A single-institution, longitudinally maintained database was queried to identify 297 patients who met specific inclusion and exclusion criteria. Patient characteristics and the order and timing of removing the urinary and epidural catheters were collected. Rates of UR were statistically compared in patients who had early vs. late urinary catheter removal. A univariate and multivariate regression analysis was conducted to identify independent risk factors. Hospital episode costs were analyzed., Results: Patients who had early (n = 66, 22%) vs. late (n = 231, 78%) urinary catheter removal had a significantly higher incidence of UR requiring recatheterization (15 vs. 4.7%, p = 0.007). Patient with early removal were almost 4 times more likely to develop UR requiring recatheterization [odds ratio (OR) 3.8, 95% confidence interval (CI) 1.5-9.7, p = 0.005]. UR incurred additional costs averaging $15,000/patient (p = 0.204)., Conclusion: In patients who had PSIF for AIS, removal of a urinary catheter before discontinuation of EA is an independent risk factor for UR, requiring recatheterization and associated with increased cost., Level of Evidence: III.

Background: Changes in reimbursement in total knee arthroplasty (TKA) by Centers for Medicare and Medicaid Services (CMS) have been tied to a perceived decrease in the total surgical time required to perform these operations. However, little information is available to CMS about recorded surgical times for TKA across the United States and the variables that drive these values. Therefore, the purpose of our study, is to evaluate (1) changes in operative time over time and (2) factors associated with variations in operative time., Methods: The National Surgical Quality Improvement Program database was queried to identify all primary TKAs conducted between January 1, 2008, and December 31, 2017. All TKAs conducted within our study period that had operative time data available were included. Multivariable linear models were created to assess factors that influence operative time over the study period., Results: Our final analysis included 140,890 TKAs. The mean operative time across the study period was found to be 92.60 minutes. Examining quarterly values, operative time stayed within 5 minutes of this mean (range, 89.80-97.51 minutes). Age, sex, functional status, anesthesia type, body mass index, operative year, transfusion requirements, and preoperative laboratory findings significantly influenced operative time (P < .05 for all)., Conclusion: Our analysis indicates that while there are numerous factors that influence procedure duration, operative times have remained stable. This information should be heavily considered in regard to physician reimbursement, because providers are maintaining operative times and work effort while mitigating factors that influence outcomes in the perioperative period., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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

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.

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.)

Despite progress in intensification of therapy, outcomes for patients with metastatic osteosarcoma (OS) have not improved in thirty years. We developed a system that enabled preclinical screening of compounds against metastatic OS cells in the context of the native lung microenvironment. Using this strategy to screen a library of epigenetically targeted compounds, we identified inhibitors of CDK12 to be most effective, reducing OS cell outgrowth in the lung by more than 90% at submicromolar doses. We found that knockout of CDK12 in an in vivo model of lung metastasis significantly decreased the ability of OS to colonize the lung. CDK12 inhibition led to defects in transcription elongation in a gene length- and expression-dependent manner. These effects were accompanied by defects in RNA processing and altered the expression of genes involved in transcription regulation and the DNA damage response. We further identified OS models that differ in their sensitivity to CDK12 inhibition in the lung and provided evidence that upregulated MYC levels may mediate these differences. Our studies provided a framework for rapid preclinical testing of compounds with antimetastatic activity and highlighted CDK12 as a potential therapeutic target in OS.

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.)

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.

Study Design: Retrospective cohort study., Objective: The aims of this study were to (1) compare patient and procedure-specific characteristics among those who had short versus long hospital stays and (2) identify independent risk factors that may correlate with extended length of hospital stay (LOS) in patients with adolescent idiopathic scoliosis (AIS) who underwent posterior segmental instrumented fusion (PSIF)., Summary of Background Data: Reducing the LOS and identifying risk factors associated with extended admission have become increasingly relevant to healthcare policy makers. There is currently limited research identifying risk factors that correlate with extended stay in patients undergoing PSIF for AIS., Methods: A single-institution, longitudinally maintained database was queried to identify 407 patients who met specific inclusion and exclusion criteria. Based on the distribution and median LOS in the cohort (4 days), patients were divided into those who had long versus short LOS. In both groups, patient demographics, comorbidities, preoperative scoliosis curve measurements, surgery-related characteristics, and complications were analyzed. A univariate and multivariate regression analysis was then conducted to identify independent risk factors associated with extended LOS., Results: Patients who had extended LOS tended to be women (84.6% vs. 75%, P = 0.01), had more levels fused (9 ± 2 vs. 7 ± 2 levels, P < 0.001), had more major postoperative complications (0.8% vs. 7.4%, P = 0.002), had more blood loss during surgery (723 ± 548 vs. 488 ± 341 cm, P < 0.001), and received less epidural analgesia for pain control (69% vs. 89%, P < 0.001). Except for higher thoracic kyphosis, long LOS patients did not have worse preoperative radiographic curve parameters. Multivariate logistic analysis identified female sex, having ≥9 ± 2 levels of fusion, operative blood loss, major postoperative complications, lack of epidural analgesia, and higher thoracic kyphosis as independent risk factors correlating for extended LOS., Conclusion: Independent risk factors identified by this study may be used to recognize patients with AIS at risk of prolonged hospital stay., Level of Evidence: 3.

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.

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.

OBJECTIVEThe position of neurosurgery department chair undergoes constant evolution as the health care landscape changes. The authors' aim in this paper was to characterize career attributes of neurosurgery department chairs in order to define temporal trends in qualities being sought in neurosurgical leaders. Specifically, they investigated the hypothesis that increased qualifications in the form of additional advanced degrees and research acumen are becoming more common in recently hired chairs, possibly related to the increased complexity of their role.METHODSThe authors performed a retrospective study in which they collected data on 105 neurosurgeons who were neurosurgery department chairs as of December 31, 2016, at accredited academic institutions with a neurosurgery residency program in the United States. Descriptive data on the career of neurosurgery chairs, such as the residency program attended, primary subspecialty focus, and age at which they accepted their position as chair, were collected.RESULTSThe median age and number of years in practice postresidency of neurosurgery chairs on acceptance of the position were 47 years (range 36-63 years) and 14 years (range 6-33 years), respectively, and 87% (n = 91) were first-time chairs. The median duration that chairs had been holding their positions as of December 31, 2016, was 10 years (range 1-34 years). The most common subspecialties were vascular (35%) and tumor/skull base (27%), although the tendency to hire from these specialties diminished over time (p = 0.02). More recently hired chairs were more likely to be older (p = 0.02), have more publications (p = 0.007), and have higher h-indices (p < 0.001) at the time of hire. Prior to being named chair, 13% (n = 14) had a PhD, 4% (n = 4) had an MBA, and 23% (n = 24) were awarded a National Institutes of Health R01 grant, tendencies that were stable over time (p = 0.09-0.23), although when additional degrees were analyzed as a binary variable, chairs hired in 2010 or after were more likely to have an MBA and/or PhD versus those hired before 2010 (26% vs 10%, p = 0.04). The 3 most common residency programs attended by the neurosurgery chairs were Massachusetts General Hospital (n = 8, 8%), University of California, San Francisco (n = 8, 8%), and University of Michigan (n = 6, 6%). Most chairs (n = 63, 61%) attended residency at the institution and/or were staff at the institution before they were named chair, a tendency that persisted over time (p = 0.86).CONCLUSIONSMost neurosurgery department chairs matriculated into the position before the age of 50 years and, despite selection processes usually involving a national search, most chairs had a previous affiliation with the department, a phenomenon that has been relatively stable over time. In recent years, a large increase has occurred in the proportion of chairs with additional advanced degrees and more extensive research experience, underscoring how neurosurgical leadership has come to require scientific skills and the ability to procure grants, as well as the financial skills needed to navigate the ever-changing financial health care landscape.

Enhancers are a class of regulatory elements essential for precise spatio-temporal control of gene expression during development and in terminally differentiated cells. This review highlights signature features of enhancer elements as well as new advances that provide mechanistic insights into enhancer-mediated gene control in the context of three-dimensional chromatin. We detail the various ways in which non-coding mutations can instigate aberrant gene control and cause a variety of Mendelian disorders, common diseases and cancer.