Costs of revision operations for distal junctional kyphosis following thoracic posterior spinal fusion for adolescent idiopathic scoliosis.

PURPOSE: To assess direct costs and risks associated with revision operations for distal junctional kyphosis/failure (DJK) following thoracic posterior spinal instrumented fusions (TPSF) for adolescent idiopathic scoliosis (AIS). METHODS: Children who underwent TPSF for AIS by a single surgeon (2014-2020) were reviewed. Inclusion criteria were minimum follow-up of 2 years, thoracolumbar posterior instrumented fusion with a lower instrumented vertebra (LIV) cranial to L2. Patients who developed DJK requiring revision operations were identified and compared with those who did not develop DJK. RESULTS: Seventy-nine children were included for analysis. Of these, 6.3% developed DJK. Average time to revision was 20.8 ± 16.2 months. Comparing index operations, children who developed DJK had significantly greater BMIs, significantly lower thoracic kyphosis postoperatively, greater post-operative lumbar Cobb angles, and significantly more LIVs cranial to the sagittal stable vertebrae (SSV), despite having statistically similar pre-operative coronal and sagittal alignment parameters and operative details compared with non-DJK patients. Revision operations for DJK, when compared with index operations, involved significantly fewer levels, longer operative times, greater blood loss, and longer hospital lengths of stay. These factors resulted in significantly greater direct costs for revision operations for DJK ($76,883 v. $46,595; p < 0.01). CONCLUSIONS: In this single-center experience, risk factors for development of DJK were greater BMI, lower post-operative thoracic kyphosis, and LIV cranial to SSV. As revision operations for DJK were significantly more costly than index operations, all efforts should be aimed at strategies to prevent DJK in the AIS population.

The Number of Patients Lost to Follow-Up May Exceed the Fragility Index of a Randomized Controlled Trial Without Reversing Statistical Significance: A Systematic Review and Statistical Model.

PURPOSE: To (1) analyze trends in the publishing of statistical fragility index (FI)-based systematic reviews in the orthopaedic literature, including the prevalence of misleading or inaccurate statements related to the statistical fragility of randomized controlled trials (RCTs) and patients lost to follow-up (LTF), and (2) determine whether RCTs with relatively "low" FIs are truly as sensitive to patients LTF as previously portrayed in the literature. METHODS: All FI-based studies published in the orthopaedic literature were identified using the Cochrane Database of Systematic Reviews, Web of Science Core Collection, PubMed, and MEDLINE databases. All articles involving application of the FI or reverse FI to study the statistical fragility of studies in orthopaedics were eligible for inclusion in the study. Study characteristics, median FIs and sample sizes, and misleading or inaccurate statements related to the FI and patients LTF were recorded. Misleading or inaccurate statements-defined as those basing conclusions of trial fragility on the false assumption that adding patients LTF back to a trial has the same statistical effect as existing patients in a trial experiencing the opposite outcome-were determined by 2 authors. A theoretical RCT with a sample size of 100, P = .006, and FI of 4 was used to evaluate the difference in effect on statistical significance between flipping outcome events of patients already included in the trial (FI) and adding patients LTF back to the trial to show the true sensitivity of RCTs to patients LTF. RESULTS: Of the 39 FI-based studies, 37 (95%) directly compared the FI with the number of patients LTF. Of these 37 studies, 22 (59%) included a statement regarding the FI and patients LTF that was determined to be inaccurate or misleading. In the theoretical RCT, a reversal of significance was not observed until 7 patients LTF (nearly twice the FI) were added to the trial in the distribution of maximal significance reversal. CONCLUSIONS: The claim that any RCT in which the number of patients LTF exceeds the FI could potentially have its significance reversed simply by maintaining study follow-ups is commonly inaccurate and prevalent in orthopaedic studies applying the FI. Patients LTF and the FI are not equivalent. The minimum number of patients LTF required to flip the significance of a typical RCT was shown to be greater than the FI, suggesting that RCTs with relatively low FIs may not be as sensitive to patients LTF as previously portrayed in the literature; however, only a holistic approach that considers the context in which the trial was conducted, potential biases, and study results can determine the merits of any particular RCT. CLINICAL RELEVANCE: Surgeons may benefit from re-examining their interpretation of prior FI reviews that have made claims of substantial RCT fragility based on comparisons between the FI and patients LTF; it is possible the results are more robust than previously believed.

Medical Imaging Applications Developed Using Artificial Intelligence Demonstrate High Internal Validity Yet Are Limited in Scope and Lack External Validation.

PURPOSE: To (1) review definitions and concepts necessary to interpret applications of deep learning (DL; a domain of artificial intelligence that leverages neural networks to make predictions on media inputs such as images) and (2) identify knowledge and translational gaps in the literature to provide insight into specific areas for improvement as adoption of this technology continues. METHODS: A comprehensive search of the literature was performed in December 2023 for articles regarding the use of DL in sports medicine. For each study, information regarding the joint of focus, specific anatomic structure/pathology to which DL was applied, imaging modality utilized, source of images used for model training and testing, data set size, model performance, and whether the DL model was externally validated was recorded. A numerical scale was used to rate each DL model's clinical impact, with 1 corresponding to proof-of-concept studies with little to no direct clinical impact and 5 corresponding to practice-changing clinical impact and ready for clinical deployment. RESULTS: Fifty-five studies were identified, all of which were published within the past 5 years, while 82% were published within the past 3 years. Of the DL models identified, 84% were developed for classification tasks, 9% for automated measurements, and 7% for segmentation. A total of 62% of studies utilized magnetic resonance imaging as the imaging modality, 25% radiographs, and 7% ultrasound, while 1 study each used computed tomography, arthroscopic images, or arthroscopic video. Sixty-five percent of studies focused on the detection of tears (anterior cruciate ligament [ACL], rotator cuff [RC], and meniscus). The diagnostic performance of ACL tears, as determined by the area under the receiver operator curve (AUROC), ranged from 0.81 to 0.99 for ACL tears (excellent to near perfect), 0.83 to 0.94 for RC tears (excellent), and from 0.75 to 0.96 for meniscus tears (acceptable to excellent). In addition, 3 studies focused on detection of cartilage lesions had AUROC ranging from 0.90 to 0.92 (excellent performance). However, only 4 (7%) studies externally validated their models, suggesting that they may not be generalizable or may not perform well when applied to populations other than that used to develop the model. Finally, the mean clinical impact score was 2 (range, 1-3) on scale of 1 to 5, corresponding to limited clinical applicability. CONCLUSIONS: DL models in orthopaedic sports medicine show generally excellent performance (high internal validity) but require external validation to facilitate clinical deployment. In addition, current models have low clinical applicability and fail to advance the field due to a focus on routine tasks and a narrow conceptual framework. LEVEL OF EVIDENCE: Level IV, scoping review of Level I to IV studies.

A Machine Learning Model Demonstrates Excellent Performance in Predicting Subscapularis Tears Based on Pre-Operative Imaging Parameters Alone.

PURPOSE: To develop a machine learning model capable of identifying subscapularis tears before surgery based on imaging and physical examination findings. METHODS: Between 2010 and 2020, 202 consecutive shoulders underwent arthroscopic rotator cuff repair by a single surgeon. Patient demographics, physical examination findings (including range of motion, weakness with internal rotation, lift/push-off test, belly press test, and bear hug test), and imaging (including direct and indirect signs of tearing, biceps status, fatty atrophy, cystic changes, and other similar findings) were included for model creation. RESULTS: Sixty percent of the shoulders had partial or full thickness tears of the subscapularis verified during surgery (83% of these were upper third). Using only preoperative imaging-related parameters, the XGBoost model demonstrated excellent performance at predicting subscapularis tears (c-statistic, 0.84; accuracy, 0.85; F1 score, 0.87). The top 5 features included direct signs related to the presence of tearing as evidenced on magnetic resonance imaging (MRI) (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. CONCLUSIONS: In this study, machine learning was successful in predicting subscapularis tears by MRI alone in 85% of patients, and this accuracy did not decrease by isolating the model to the top features. The top five features included direct signs related to the presence of tearing as evidenced on MRI (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Last, in advanced modeling, the addition of physical examination or patient characteristics did not make a significant difference in the predictive ability of this model. LEVEL OF EVIDENCE: Level III, diagnostic case-control study.

ChatGPT-4 Performs Clinical Information Retrieval Tasks Utilizing Consistently More Trustworthy Resources Than Does Google Search for Queries Concerning the Latarjet Procedure.

PURPOSE: To assess the ability for ChatGPT-4, an automated Chatbot powered by artificial intelligence (AI), to answer common patient questions concerning the Latarjet procedure for patients with anterior shoulder instability and compare this performance to Google Search Engine. METHODS: Using previously validated methods, a Google search was first performed using the query "Latarjet." Subsequently, the top ten frequently asked questions (FAQs) and associated sources were extracted. ChatGPT-4 was then prompted to provide the top ten FAQs and answers concerning the procedure. This process was repeated to identify additional FAQs requiring discrete-numeric answers to allow for a comparison between ChatGPT-4 and Google. Discrete, numeric answers were subsequently assessed for accuracy based on the clinical judgement of two fellowship-trained sports medicine surgeons blinded to search platform. RESULTS: Mean (±standard deviation) accuracy to numeric-based answers were 2.9±0.9 for ChatGPT-4 versus 2.5±1.4 for Google (p=0.65). ChatGPT-4 derived information for answers only from academic sources, which was significantly different from Google Search Engine (p=0.003), which used only 30% academic sources and websites from individual surgeons (50%) and larger medical practices (20%). For general FAQs, 40% of FAQs were found to be identical when comparing ChatGPT-4 and Google Search Engine. In terms of sources used to answer these questions, ChatGPT-4 again used 100% academic resources, while Google Search Engine used 60% academic resources, 20% surgeon personal websites, and 20% medical practices (p=0.087). CONCLUSION: ChatGPT-4 demonstrated the ability to provide accurate and reliable information about the Latarjet procedure in response to patient queries, using multiple academic sources in all cases. This was in contrast to Google Search Engine, which more frequently used single surgeon and large medical practice websites. Despite differences in the resources accessed to perform information retrieval tasks, the clinical relevance and accuracy of information provided did not significantly differ between ChatGPT-4 and Google Search Engine.

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy.

Deep learning is a subset of artificial intelligence (AI) with enormous potential to transform orthopaedic surgery. As has already become evident with the deployment of Large Language Models (LLMs) like ChatGPT (OpenAI Inc.), deep learning can rapidly enter clinical and surgical practices. As such, it is imperative that orthopaedic surgeons acquire a deeper understanding of the technical terminology, capabilities and limitations associated with deep learning models. The focus of this series thus far has been providing surgeons with an overview of the steps needed to implement a deep learning-based pipeline, emphasizing some of the important technical details for surgeons to understand as they encounter, evaluate or lead deep learning projects. However, this series would be remiss without providing practical examples of how deep learning models have begun to be deployed and highlighting the areas where the authors feel deep learning may have the most profound potential. While computer vision applications of deep learning were the focus of Parts I and II, due to the enormous impact that natural language processing (NLP) has had in recent months, NLP-based deep learning models are also discussed in this final part of the series. In this review, three applications that the authors believe can be impacted the most by deep learning but with which many surgeons may not be familiar are discussed: (1) registry construction, (2) diagnostic AI and (3) data privacy. Deep learning-based registry construction will be essential for the development of more impactful clinical applications, with diagnostic AI being one of those applications likely to augment clinical decision-making in the near future. As the applications of deep learning continue to grow, the protection of patient information will become increasingly essential; as such, applications of deep learning to enhance data privacy are likely to become more important than ever before. Level of Evidence: Level IV.

Diagnostic Performance of Artificial Intelligence for Detection of Scaphoid and Distal Radius Fractures: A Systematic Review.

PURPOSE: To review the existing literature to (1) determine the diagnostic efficacy of artificial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the efficacy to human clinical experts. METHODS: PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded. RESULTS: A total of 21 studies were identified, of which 12 (57.1%) used AI to detect fractures of the distal radius, and nine (42.9%) used AI to detect fractures of the scaphoid. AI models demonstrated good diagnostic performance on average, with AUROC values ranging from 0.77 to 0.96 for scaphoid fractures and from 0.90 to 0.99 for distal radius fractures. Accuracy of AI models ranged between 72.0% to 90.3% and 89.0% to 98.0% for scaphoid and distal radius fractures, respectively. When compared to clinical experts, 13 of 14 (92.9%) studies reported that AI models demonstrated comparable or better performance. The type of fracture influenced model performance, with worse overall performance on occult scaphoid fractures; however, models trained specifically on occult fractures demonstrated substantially improved performance when compared to humans. CONCLUSIONS: AI models demonstrated excellent performance for detecting scaphoid and distal radius fractures, with the majority demonstrating comparable or better performance compared with human experts. Worse performance was demonstrated on occult fractures. However, when trained specifically on difficult fracture patterns, AI models demonstrated improved performance. CLINICAL RELEVANCE: AI models can help detect commonly missed occult fractures while enhancing workflow efficiency for distal radius and scaphoid fracture diagnoses. As performance varies based on fracture type, future studies focused on wrist fracture detection should clearly define whether the goal is to (1) identify difficult-to-detect fractures or (2) improve workflow efficiency by assisting in routine tasks.

Deep learning to automatically classify very large sets of preoperative and postoperative shoulder arthroplasty radiographs.

BACKGROUND: Joint arthroplasty registries usually lack information on medical imaging owing to the laborious process of observing and recording, as well as the lack of standard methods to transfer the imaging information to the registries, which can limit the investigation of various research questions. Artificial intelligence (AI) algorithms can automate imaging-feature identification with high accuracy and efficiency. With the purpose of enriching shoulder arthroplasty registries with organized imaging information, it was hypothesized that an automated AI algorithm could be developed to classify and organize preoperative and postoperative radiographs from shoulder arthroplasty patients according to laterality, radiographic projection, and implant type. METHODS: This study used a cohort of 2303 shoulder radiographs from 1724 shoulder arthroplasty patients. Two observers manually labeled all radiographs according to (1) laterality (left or right), (2) projection (anteroposterior, axillary, or lateral), and (3) whether the radiograph was a preoperative radiograph or showed an anatomic total shoulder arthroplasty or a reverse shoulder arthroplasty. All these labeled radiographs were randomly split into developmental and testing sets at the patient level and based on stratification. By use of 10-fold cross-validation, a 3-task deep-learning algorithm was trained on the developmental set to classify the 3 aforementioned characteristics. The trained algorithm was then evaluated on the testing set using quantitative metrics and visual evaluation techniques. RESULTS: The trained algorithm perfectly classified laterality (F1 scores [harmonic mean values of precision and sensitivity] of 100% on the testing set). When classifying the imaging projection, the algorithm achieved F1 scores of 99.2%, 100%, and 100% on anteroposterior, axillary, and lateral views, respectively. When classifying the implant type, the model achieved F1 scores of 100%, 95.2%, and 100% on preoperative radiographs, anatomic total shoulder arthroplasty radiographs, and reverse shoulder arthroplasty radiographs, respectively. Visual evaluation using integrated maps showed that the algorithm focused on the relevant patient body and prosthesis parts for classification. It took the algorithm 20.3 seconds to analyze 502 images. CONCLUSIONS: We developed an efficient, accurate, and reliable AI algorithm to automatically identify key imaging features of laterality, imaging view, and implant type in shoulder radiographs. This algorithm represents the first step to automatically classify and organize shoulder radiographs on a large scale in very little time, which will profoundly enrich shoulder arthroplasty registries.

The Relationship Between Design-Based Lateralization, Humeral bearing design, Polyethylene Angle, and Patient-Related Factors on Surgical Complications after Reverse Shoulder Arthroplasty: A Machine Learning Analysis.

INTRODUCTION: Technological advancements in implant design and surgical technique have focused on diminishing complications and optimizing performance of reverse shoulder arthroplasty (RSA). Despite this, there remains a paucity of literature correlating prosthetic features and clinical outcomes. This investigation utilized a machine learning approach to evaluate the effect of select implant design features and patient-related factors on surgical complications after RSA. METHODS: Over a 16-year period (2004 - 2020), all primary RSA performed at a single institution for elective and traumatic indications with a minimum follow-up of 2 years were identified. Parameters related to implant design evaluated in this study included inlay vs onlay humeral bearing design, glenoid lateralization (medialized or lateralized), humeral lateralization (medialized, minimally lateralized, or lateralized), global lateralization (medialized, minimally lateralized, lateralized, highly lateralized, or very highly lateralized), stem to metallic bearing neck shaft angle (NSA), and polyethylene NSA. Machine learning models predicting surgical complications were constructed for each patient and Shapley additive explanation (SHAP) values were calculated to quantify feature importance. RESULTS: A total of 3,837 RSAs were identified, of which 472 (12.3%) experienced a surgical complication. Those experiencing a surgical complication were more likely to be current smokers (Odds ratio [OR] = 1.71; P = .003), have prior surgery (OR = 1.60; P < .001), have an underlying diagnosis of sequalae of instability (OR = 4.59; P < .001) or non-union (OR = 3.09; P < .001), and required longer OR times (98 vs. 86 minutes; P < .001). Notable implant design features at an increased odds for complications included an inlay humeral component (OR = 1.67; P < .001), medialized glenoid (OR = 1.43; P = .001), medialized humerus (OR = 1.48; P = .004), a minimally lateralized global construct (OR = 1.51; P < .001), and glenohumeral constructs consisting of a medialized glenoid and minimally lateralized humerus (OR = 1.59; P < .001), and a lateralized glenoid and medialized humerus (OR = 2.68; P < .001). Based on patient- and implant-specific features, the machine learning model predicted complications after RSA with an area under the receiver operating characteristic curve (AUC ROC) of 0.61. CONCLUSIONS: This study demonstrated that patient-specific risk factors had a more substantial effect than implant design configurations on the predictive ability of a machine learning model on surgical complications after RSA. However, certain implant features appeared to be associated with a higher odd of surgical complications.

Pre-existing Mental Health Diagnoses Are Associated With Higher Rates of Postoperative Complications, Readmissions, and Reoperations Following Arthroscopic Rotator Cuff Repair.

PURPOSE: To investigate the association between preoperative mental health disorders and postoperative complications, readmissions, and ipsilateral revision procedures among patients undergoing arthroscopic rotator cuff repair (RCR). METHODS: A retrospective cohort study from 2010 to 2020 was performed using the PearlDiver database. Current Procedural Terminology and International Classification of Diseases codes were used to compare patients with and without mental health disorders who underwent arthroscopic RCR. Mental health disorders evaluated in this study include depressive disorder, major depressive disorder, major depressive affective disorder, bipolar disorder, dysthymic disorder, adjustment disorder, separation anxiety disorder, and posttraumatic stress disorder. Patients were matched at a 1:1 ratio based on age, sex, Charlson Comorbidity Index, body mass index, and tobacco use. Rates of complications and subsequent surgeries were compared between patients with and without a preoperative diagnosis of a mental health disorder. RESULTS: The 1-year preoperative prevalence of a mental health disorder from 2010 to 2020 was 14.6%. After 1:1 matching, patients with a mental health disorder who underwent arthroscopic RCR were nearly twice as likely to undergo a revision procedure (odds ratio 1.94, 95% confidence interval 1.76-2.14, P < .001) and more than twice as likely to experience conversion to shoulder arthroplasty (odds ratio 2.29, 95% confidence interval 1.88-2.80, P < .001) within 2 years of initial arthroscopy when compared with patients without a mental disorder. Patients with a mental disorder also experienced increased risk for 90-day readmission (1.9% vs 0%, P < .001) as well as multiple postoperative medical complications. CONCLUSIONS: Patients with pre-existing mental health diagnoses experience increased rates of 90-day postoperative complications and readmissions following arthroscopic RCR. In addition, patients with mental health diagnoses are more likely to undergo revision repair and conversion to shoulder arthroplasty within 2 years of the index procedure. LEVEL OF EVIDENCE: Level III.

Platelet-Rich Plasma and Marrow Venting May Serve as Cost-Effective Augmentation Techniques for Isolated Meniscal Repair: A Decision-Analytical Markov Model-Based Analysis.

PURPOSE: To evaluate the cost-effectiveness of 3 isolated meniscal repair (IMR) treatment strategies: platelet-rich plasma (PRP)-augmented IMR, IMR with a marrow venting procedure (MVP), and IMR without biological augmentation. METHODS: A Markov model was developed to evaluate the baseline case: a young adult patient meeting the indications for IMR. Health utility values, failure rates, and transition probabilities were derived from the published literature. Costs were determined based on the typical patient undergoing IMR at an outpatient surgery center. Outcome measures included costs, quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio (ICER). RESULTS: Total costs of IMR with an MVP were $8,250; PRP-augmented IMR, $12,031; and IMR without PRP or an MVP, $13,326. PRP-augmented IMR resulted in an additional 2.16 QALYs, whereas IMR with an MVP produced slightly fewer QALYs, at 2.13. Non-augmented repair produced a modeled gain of 2.02 QALYs. The ICER comparing PRP-augmented IMR versus MVP-augmented IMR was $161,742/QALY, which fell well above the $50,000 willingness-to-pay threshold. CONCLUSIONS: IMR with biological augmentation (MVP or PRP) resulted in a higher number of QALYs and lower costs than non-augmented IMR, suggesting that biological augmentation is cost-effective. Total costs of IMR with an MVP were significantly lower than those of PRP-augmented IMR, whereas the number of additional QALYs produced by PRP-augmented IMR was only slightly higher than that produced by IMR with an MVP. As a result, neither treatment dominated over the other. However, because the ICER of PRP-augmented IMR fell well above the $50,000 willingness-to-pay threshold, IMR with an MVP was determined to be the overall cost-effective treatment strategy in the setting of young adult patients with isolated meniscal tears. LEVEL OF EVIDENCE: Level III, economic and decision analysis.

Autologous Osteochondral Transfer of the Knee Demonstrates Continued High Rates of Return to Sport and Low Rates of Conversion to Arthroplasty at Long-Term Follow-Up: A Systematic Review.

PURPOSE: To perform a systematic review of the literature to evaluate (1) activity level and knee function, (2) reoperation and failure rates, and (3) risk factors for reoperation and failure of autologous osteochondral transfer (AOT) at long-term follow-up. METHODS: A comprehensive review of the long-term outcomes of AOT was performed. Studies reported on activity-based outcomes (Tegner Activity Scale) and clinical outcomes (Lysholm score and International Knee Documentation Committee score). Reoperation and failure rates as defined by the publishing authors were recorded for each study. Modified Coleman Methodology Scores were calculated to assess study methodological quality. RESULTS: Twelve studies with a total of 495 patients and an average age of 32.5 years at the time of surgery and a mean follow-up of 15.1 years (range, 10.4-18.0 years) were included. The mean defect size was 3.2 cm2 (range, 1.9-6.9 cm2). The mean duration of symptoms before surgery was 5.1 years. Return to sport rates ranged from 86% to 100%. Conversion to arthroplasty rates ranged from 0% to 16%. The average preoperative International Knee Documentation Committee scores ranged from 32.9 to 36.8, and the average postoperative International Knee Documentation Committee scores at final follow-up ranged from 66.3 to 77.3. The average preoperative Lysholm scores ranged from 44.5 to 56.0 and the average postoperative Lysholm scores ranged from 70.0 to 96.5. The average preoperative Tegner scores ranged from 2.5 to 3.0, and the average postoperative scores ranged from 4.1 to 7.0. CONCLUSIONS: AOT of the knee resulted in high rates of return to sport with correspondingly low rates of conversion to arthroplasty at long-term follow-up. In addition, AOT demonstrated significant improvements in long-term patient-reported outcomes from baseline. LEVEL OF EVIDENCE: Level IV, systematic review of Level I-IV studies.

Complications and re-operations after extensor mechanism repair surgery in a large cross-sectional cohort: females and tobacco-users at highest risk for adverse outcomes.

PURPOSE: There is little information on patients most at risk for poor outcomes following surgical repair of extensor mechanism tendon injuries. The purpose of this study is to provide an epidemiological overview of patients undergoing patellar or quadriceps tendon repair and to assess the incidence of postoperative complications, readmissions, and revision repairs among this population. METHODS: Retrospective data were obtained using the PearlDiver database for patellar tendon repair and quadriceps tendon repair patients between 2010 and 2020. Baseline demographics, incidences of 90-day readmissions and postoperative complications, and reoperation rates were collected for each group. Multivariate logistic regression was performed to assess the predictive power of each demographic variable on the incidence of postoperative complications and reoperations. RESULTS: In total, 1543 patients underwent patellar tendon repair and 601 underwent quadriceps tendon repair. Complications within 90-days were observed in 33.7% of patients with patellar tendon repair and 39.2% of patients with quadriceps tendon repair. Reoperation rates were 4.2% and 4.8% for patellar tendon repair and quadriceps tendon repair, respectively. Females in both patellar tendon repair and quadriceps tendon repair groups were at significantly higher risk for post-operative complications (patellar tendon repair OR 3.0, 95% CI 2.4-3.7; quadriceps tendon repair OR 2.9, 95% CI 1.9-4.6; p < 0.001 for both). Older age (p < 0.001), female gender (p < 0.001), CCI (p < 0.001), tobacco use (p < 0.001), and obesity (p < 0.01) were all predictors of experiencing at least one complication following patellar tendon repair. For quadriceps tendon repair, female gender (p < 0.001) and CCI (p < 0.001) were the strongest predictors of experiencing at least one complication, while older age, tobacco use, and obesity (p < 0.05 for all) were also significant independent predictors. CONCLUSION: Patellar tendon repair patients are younger on average than quadriceps tendon repair patients. Although females are less likely to sustain extensor mechanism ruptures compared to males, females are significantly more likely to have at least one complication after quadriceps or patellar tendon repair. These findings may be used by surgeons, patients, and payors to understand who is most at risk for adverse outcomes following extensor mechanism repair surgery, resulting in earlier intervention and counseling to reduce the likelihood of a poor outcome following extensor mechanism repair surgery. LEVEL OF EVIDENCE: Level III.

A practical guide to the development and deployment of deep learning models for the orthopedic surgeon: part II.

Deep learning has the potential to be one of the most transformative technologies to impact orthopedic surgery. Substantial innovation in this area has occurred over the past 5 years, but clinically meaningful advancements remain limited by a disconnect between clinical and technical experts. That is, it is likely that few orthopedic surgeons possess both the clinical knowledge necessary to identify orthopedic problems, and the technical knowledge needed to implement deep learning-based solutions. To maximize the utilization of rapidly advancing technologies derived from deep learning models, orthopedic surgeons should understand the steps needed to design, organize, implement, and evaluate a deep learning project and its workflow. Equipping surgeons with this knowledge is the objective of this three-part editorial review. Part I described the processes involved in defining the problem, team building, data acquisition, curation, labeling, and establishing the ground truth. Building on that, this review (Part II) provides guidance on pre-processing and augmenting the data, making use of open-source libraries/toolkits, and selecting the required hardware to implement the pipeline. Special considerations regarding model training and evaluation unique to deep learning models relative to "shallow" machine learning models are also reviewed. Finally, guidance pertaining to the clinical deployment of deep learning models in the real world is provided. As in Part I, the focus is on applications of deep learning for computer vision and imaging.

A practical guide to the development and deployment of deep learning models for the Orthopedic surgeon: part I.

Deep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of what is necessary to design and implement deep learning-based project pipelines. This review provides a practical guide for the Orthopedic surgeon to understand the steps needed to design, develop, and deploy a deep learning pipeline for clinical applications. A detailed description of the processes involved in defining the problem, building the team, acquiring and curating the data, labeling the data, establishing the ground truth, pre-processing and augmenting the data, and selecting the required hardware is provided. In addition, an overview of unique considerations involved in the training and evaluation of deep learning models is provided. This review strives to provide surgeons with the groundwork needed to identify gaps in the clinical landscape that deep learning models may be able to fill and equips them with the knowledge needed to lead an interdisciplinary team through the process of creating novel deep-learning-based solutions to fill those gaps.

Supervised machine learning and associated algorithms: applications in orthopedic surgery.

Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on "big data" develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.

Incidence and 30- and 90-day readmission rates after primary shoulder arthroplasty in the United States: an analysis using the National Readmissions Database.

BACKGROUND: The field of shoulder arthroplasty has experienced a substantial increase in the number of procedures performed annually and a shift toward more common implantation of reverse shoulder arthroplasties (RSAs). Same-day discharge is perceived as beneficial for most patients as well as our health care system, and the number of shoulder procedures performed as same-day surgery has increased substantially. However, the potential benefits of same-day discharge after shoulder arthroplasty may be negatively influenced by unexpected readmissions. As such, an in-depth analysis of readmission rates after primary shoulder arthroplasty is particularly timely. METHODS: The National Readmissions Database was queried for primary shoulder arthroplasty procedures performed in the United States between 2016 and 2018. National incidences were calculated, and indications, patient demographic characteristics, comorbidities, facility characteristics, and rates and causes of 30- and 90-day readmissions were determined for all procedures and compared between anatomic total shoulder arthroplasty (TSA), anatomic hemiarthroplasty (HA), and RSA. RESULTS: During the study period, 336,672 primary shoulder arthroplasties were performed (37% TSAs, 57% RSAs, and 6% HAs). In 2018, national incidences per 100,000 inhabitants were 22.64 for RSA, 12.70 for TSA, and 1.50 for HA. The utilization of these procedures between 2016 and 2018 increased for RSA, decreased for HA, and remained constant for TSA, but these changes did not reach the level of statistical significance. The average all-cause 30-day readmission rates were 3.63%, 1.92%, and 3.81% for RSA, TSA, and HA, respectively, and the average all-cause 90-day readmission rates were 7.76%, 4.37%, and 9.18%, respectively. For both RSA and HA, the most common surgical diagnosis for 30-day and 90-day readmissions was dislocation (0.45% and 0.99%, respectively, for RSA and 0.21% and 0.67%, respectively, for HA). For TSA, the most common surgical diagnosis for 30-day readmission was infection (0.11%); however, this was surpassed by dislocation (0.28%) at 90 days. CONCLUSION: RSA surpassed TSA as the most frequently performed shoulder arthroplasty procedure in the United States between 2016 and 2018. During this period, the 90-day readmission rate was not negligible, with dislocation and infection as the leading orthopedic causes of readmission.

Understanding risk for early dislocation resulting in reoperation within 90 days of reverse total shoulder arthroplasty: extreme rare event detection through cost-sensitive machine learning.

BACKGROUND: Reliable prediction of postoperative dislocation after reverse total shoulder arthroplasty (RSA) would inform patient counseling as well as surgical and postoperative decision making. Understanding interactions between multiple risk factors is important to identify those patients most at risk of this rare but costly complication. To better understand these interactions, a game theory-based approach was undertaken to develop machine learning models capable of predicting dislocation-related 90-day readmission following RSA. MATERIAL & METHODS: A retrospective review of the Nationwide Readmissions Database was performed to identify patients who underwent RSA between 2016 and 2018 with a subsequent readmission for prosthetic dislocation. Of the 74,697 index procedures included in the data set, 740 (1%) experienced a dislocation resulting in hospital readmission within 90 days. Five machine learning algorithms were evaluated for their ability to predict dislocation leading to hospital readmission within 90 days of RSA. Shapley additive explanation (SHAP) values were calculated for the top-performing models to quantify the importance of features and understand variable interaction effects, with hierarchical clustering used to identify cohorts of patients with similar risk factor combinations. RESULTS: Of the 5 models evaluated, the extreme gradient boosting algorithm was the most reliable in predicting dislocation (C statistic = 0.71, F2 score = 0.07, recall = 0.84, Brier score = 0.21). SHAP value analysis revealed multifactorial explanations for dislocation risk, with presence of a preoperative humerus fracture; disposition involving discharge or transfer to a skilled nursing facility, intermediate care facility, or other nonroutine facility; and Medicaid as the expected primary payer resulting in strong, positive, and unidirectional effects on increasing dislocation risk. In contrast, factors such as comorbidity burden, index procedure complexity and duration, age, sex, and presence or absence of preoperative glenohumeral osteoarthritis displayed bidirectional influences on risk, indicating potential protective effects for these variables and opportunities for risk mitigation. Hierarchical clustering using SHAP values identified patients with similar risk factor combinations. CONCLUSION: Machine learning can reliably predict patients at risk for postoperative dislocation resulting in hospital readmission within 90 days of RSA. Although individual risk for dislocation varies significantly based on unique combinations of patient characteristics, SHAP analysis revealed a particularly at-risk cohort consisting of young, male patients with high comorbidity burdens who are indicated for RSA after a humerus fracture. These patients may require additional modifications in postoperative activity, physical therapy, and counseling on risk-reducing measures to prevent early dislocation after RSA.

Non-English Speakers and Socioeconomic Minorities are Significantly Less Likely to Complete Patient-Reported Outcome Measures for Total Hip and Knee Arthroplasty: Analysis of 16,119 Cases.

BACKGROUND: The Comprehensive Care for Joint Replacement requires patient-reported outcome measure (PROM) completion for total knee/hip arthroplasty (TKA/THA) patients. A 90% completion rate to avoid penalties was planned for 2023 but has been delayed. Our analysis compares TKA/THA PROM completion and results across demographics. We hypothesized that minority groups would be less likely to complete PROMs. METHODS: A retrospective review was performed from 2018 to 2021 of 16,119 patients who underwent primary elective TKA or THA at a single institution. Pairwise chi-squared tests, t-tests, analysis of variance, and multiple logistic regression analyses were used to compare PROM completion rates and scores across demographics and surgery type (TKA/THA). RESULTS: Comparing patients who had (N = 7,664) and did not have (N = 8,455) documented PROMs, completion rates were significantly lower in patients who were women, Black, Hispanic, less educated, used Medicaid insurance, lived in lower income neighborhoods, spoke non-English languages, required an interpreter, and underwent TKA versus THA. After regression analyses, odds ratios for PROM completion remained significantly lower in non-English speakers, Hispanic and Medicaid patients, lower income groups, and patients undergoing TKA. For the 31.8% of patients who completed both preoperative/postoperative PROMs, women, Black, and non-English speaking patients had significantly lower PROM scores for most measures preoperatively and postoperatively despite similar or better improvements after surgery. CONCLUSION: Patients undergoing TKA and non-English speaking, ethnic, and socioeconomic minorities are less likely to complete PROMs. Strategies to create, validate, and collect PROMs for these populations are needed to avoid exacerbation of healthcare disparities.

Rapid Adoption of Telemedicine Increases Opioid Prescribing in Orthopedic Surgery.

Background: The COVID-19 pandemic led to health care practitioners utilizing new technologies to deliver health care, including telemedicine. The purpose of this study was to examine the effect of rapidly proliferative use of video visits on opioid prescribing to orthopedic patients at a large academic health system that had existing procedure-specific opioid prescribing guidelines. Methods: This IRB-exempt study examined 651 opioid prescriptions written to patients who had video (visual and audio), telephone (audio only), or in-person encounters at our institution from March 1 to June 1, 2020 and compared them with 963 prescriptions written during the same months in 2019. Prescriptions were converted into daily milligram morphine equivalents (MMEs) to facilitate direct comparison. Chi-square testing was used to compare categorical data, whereas analysis of variance and Mann-Whitney tests were used to compare numerical data between groups. Statistical significance was set at <0.05. Results: Six hundred fifty-one of 1,614 prescriptions analyzed (40.3%) occurred during the pandemic. Patients prescribed opioids during video visits were prescribed 53.3 ± 37 MME, significantly higher than in-person (p = 0.002) or audio visits (p < 0.001) before or during the pandemic. Prepandemic, significantly higher MME were prescribed for in-person versus audio only visits (41.6 ± 89 vs. 30.2 ± 28 MME; p = 0.026); during the pandemic, there was no difference between these groups (p = 0.91). Significantly higher MME were prescribed by Nurse Practitioners and Physician Associates versus MD or DO prescribers for both time periods (51.3 ± 109 vs. 27.9 ± 42 MME; p < 0.001; 42.9 ± 70 vs. 28.2 ± 42 MME; p < 0.001). Conclusion: During crisis and with new technology, we should be vigilant about prescribing of opioid analgesics. Despite well-established protocols, patients received significantly higher MME through video than for other encounter types, including in-person encounters. In addition, significantly higher MME were prescribed by mid-level prescribers compared with DOs or MDs. Institutions should ensure these prescribers are involved during creation of opioid prescribing protocols after orthopedic surgery.