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.

The Large Language Model ChatGPT-4 Demonstrates Excellent Triage Capabilities and Diagnostic Performance for Patients Presenting with Various Causes of Knee Pain.

PURPOSE: To provide a proof-of-concept analysis of the appropriateness and performance of ChatGPT-4 to triage, synthesize differential diagnoses, and generate treatment plans concerning common presentations of knee pain. METHODS: Twenty knee complaints warranting triage and expanded scenarios were input into ChatGPT-4, with memory cleared prior to each new input to mitigate bias. For the 10 triage complaints, ChatGPT-4 was asked to generate a differential diagnosis which was graded for accuracy and suitability in comparison to a differential created by two orthopaedic sports medicine physicians. For the 10 clinical scenarios, ChatGPT-4 was prompted to provide treatment guidance for the patient, which was again graded. To test the higher-order capabilities of ChatGPT-4, further inquiry into these specific management recommendations was performed and graded. RESULTS: All ChatGPT-4 diagnoses were deemed appropriate within the spectrum of potential pathologies on a differential. The top diagnosis on the differential was identical between surgeons and ChatGPT-4 for 70% of scenarios, and the top diagnosis provided by the surgeon appeared as either the first or second diagnosis in 90% of scenarios. Overall, 16/30 (53.3%) of diagnoses in the differential were identical. When provided with 10 expanded vignettes with a single diagnosis, the accuracy of ChatGPT-4 increased to 100%, with the suitability of management graded as appropriate in 90% of cases. Specific information pertaining to conservative management, surgical approaches, and related treatments was appropriate and accurate in 100% of cases. CONCLUSION: ChatGPT-4 provided clinically reasonable diagnoses to triage patient complaints of knee pain due to various underlying conditions that was generally consistent with differentials provided by sports medicine physicians. Diagnostic performance was enhanced when providing additional information, allowing ChatGPT-4 to reach high predictive accuracy for recommendations concerning management and treatment options. However, ChatGPT-4 may demonstrate clinically important error rates for diagnosis depending on prompting strategy and information provided; therefore, further are necessary to prior to implementation into clinical workflows.

Robotic-arm-assisted lateral unicompartmental knee arthroplasty leads to high implant survival and patient satisfaction at mean 10-year follow-up.

BACKGROUND: There is a lack of literature reporting on long-term outcomes following robotic-arm-assisted lateral unicompartmental knee arthroplasty (UKA). This study assessed the long-term survivorship, patient-reported satisfaction and pain scores following robotic-arm-assisted lateral UKA for lateral compartment osteoarthritis (OA). METHODS: A single surgeon's database was reviewed to identify all patients who underwent robotic-arm-assisted lateral UKA with a cemented, fixed-bearing prosthesis prior to May 2015. Patients were contacted to determine implant survivorship, satisfaction and pain. Kaplan-Meier models were applied to analyse survival. RESULTS: A total of 77 knees (70 patients) with a mean follow-up of 10.2 ± 1.5 years (range: 8.1-13.3) were included. Five knees were revised, corresponding to a 10-year survivorship of 96.1% and estimated survival time of 12.7 ± 0.3 years (95% confidence interval: 12.2-13.2) with all-cause revision as the endpoint. Unexplained pain (40.0%) and progression of OA (40.0%) in contralateral compartments were the most reported reasons for revision. Among patients without revision, 94.4% were either satisfied or very satisfied with their lateral UKA and the average pain score was 1.1. CONCLUSION: Robotic-arm-assisted lateral UKA led to high implant survivorship and patient satisfaction, and low pain scores at long-term follow-up. Progression of OA in contralateral compartments and unexplained pain were the most frequent reasons for revision. These findings support the continued use of robotic-arm-assisted lateral UKA for lateral compartment OA; however, its clinical value over conventional techniques remains to be established in prospective comparative studies. LEVEL OF EVIDENCE: Therapeutic Level IV.

The phenotypic diversity of anteromedial osteoarthritis before and after treatment with medial unicompartmental knee arthroplasty: A radiographic analysis of 1000 knees.

PURPOSE: This study aimed to assess phenotypic variation in the coronal plane of knees with anteromedial osteoarthritis using the functional knee phenotype classification, before and after treatment with medial unicompartmental knee arthroplasty (UKA). METHODS: The study comprised 1000 knees of 835 patients (45% females, 55% males, 90% Caucasian) who underwent medial UKA for anteromedial osteoarthritis. Pre and postoperative alignment was evaluated through the hip-knee-ankle angle (HKA), femoral mechanical angle (FMA), and tibial mechanical angle (TMA). Knees were classified according to the functional knee phenotype system which combines limb phenotype (HKA), and femoral and tibial knee phenotypes (FMA and TMA, respectively). Restoration of prearthritic coronal alignment following medial UKA was evaluated by phenotype. RESULTS: Preoperatively, 76 distinct and 25 relevant (prevalence ≥1%) functional knee phenotypes were identified, of which VARHKA 6°VARFMA 3°NEUTMA 0° was the most common (9.4% of knees). The most prevalent limb phenotype, VARHKA 6°, comprised 15 distinct knee phenotypes (FMA and TMA combinations). Postoperatively, 58 distinct and 17 relevant functional knee phenotypes were observed, of which VARHKA 3°NEUFMA 0°NEUTMA 0° had the highest prevalence at 18.3%. Knees with combined tibial and femoral deformities were associated with a lower probability of restoration of prearthritic coronal alignment following medial UKA, compared to knees without extra-articular deformity, or knees with an isolated tibial or femoral deformity. CONCLUSION: Phenotype analysis using the functional knee phenotype system demonstrated a wide diversity of coronal alignment phenotypes among knees with anteromedial osteoarthritis in a predominantly Caucasian population. Following medial UKA, a reduction from 25 preoperative to 17 postoperative relevant phenotypes was observed. Consideration of phenotypic variation can be of importance when aiming to restore prearthritic coronal alignment during medial UKA. LEVEL OF EVIDENCE: Level III, retrospective cohort study.

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.

Preservation of prearthritic coronal knee phenotype and prearthritic coronal alignment yielded improved Kujala scores following ligament-guided medial unicompartmental knee arthroplasty.

PURPOSE: There is a lack of literature evaluating outcomes of the ligament-guided approach in medial unicompartmental knee arthroplasty (UKA). An improved comprehension of the distribution of coronal plane alignment of the knee (CPAK) phenotypes and sagittal tibial wear patterns and their associations with patient-reported outcome measures (PROMs) and implant survivorship could provide insights into its further application in daily practice. METHODS: A registry was reviewed for patients with a minimal 2-year follow-up who underwent robotic-assisted, ligament-guided, medial UKA between 2008 and 2016. Survivorship and postoperative PROMs were collected. CPAK phenotypes and sagittal tibial wear patterns were determined. Survivorship, Knee Injury and Osteoarthritis Outcome Score (KOOS), Kujala and patient satisfaction were compared between phenotypes and sagittal tibial wear patterns. RESULTS: A total of 618 knees were included at a mean follow-up of 4.1 [2.0-9.6] years. Four-year conversion to the TKA survival rate was 98.9% [98.4%-99.3%] and 94.3% [93.3%-95.3%] for all-cause revision. Patients with preservation of the CPAK phenotype (84.5 ± 14.9, 81.8 ± 15.5, p = 0.033) and restoration of prearthritic coronal alignment (84.1 ± 14.9, 81.7 ± 15.9, p = 0.045) had a significantly higher Kujala score. No other significant differences in survivorship or PROMs were observed between phenotypes or sagittal tibial wear patterns. Additionally, no difference in survival rates was observed between preserved or altered phenotypes. CONCLUSION: This study demonstrated that preservation of CPAK phenotype and preservation of prearthritic coronal alignment yielded a significantly higher Kujala score. No other significant differences in PROMs or implant survivorship were observed, suggesting that robotic-assisted, ligament-guided medial UKA provides equal outcomes for all observed phenotypes and sagittal tibial wear patterns in medial compartment OA as long as preoperative CPAK phenotype is preserved postoperatively. LEVEL OF EVIDENCE: Level III.

Bayesian Calibration of Computational Knee Models to Estimate Subject-Specific Ligament Properties, Tibiofemoral Kinematics, and Anterior Cruciate Ligament Force With Uncertainty Quantification.

High-grade knee laxity is associated with early anterior cruciate ligament (ACL) graft failure, poor function, and compromised clinical outcome. Yet, the specific ligaments and ligament properties driving knee laxity remain poorly understood. We described a Bayesian calibration methodology for predicting unknown ligament properties in a computational knee model. Then, we applied the method to estimate unknown ligament properties with uncertainty bounds using tibiofemoral kinematics and ACL force measurements from two cadaver knees that spanned a range of laxities; these knees were tested using a robotic manipulator. The unknown ligament properties were from the Bayesian set of plausible ligament properties, as specified by their posterior distribution. Finally, we developed a calibrated predictor of tibiofemoral kinematics and ACL force with their own uncertainty bounds. The calibrated predictor was developed by first collecting the posterior draws of the kinematics and ACL force that are induced by the posterior draws of the ligament properties and model parameters. Bayesian calibration identified unique ligament slack lengths for the two knee models and produced ACL force and kinematic predictions that were closer to the corresponding in vitro measurement than those from a standard optimization technique. This Bayesian framework quantifies uncertainty in both ligament properties and model outputs; an important step towards developing subject-specific computational models to improve treatment for ACL injury.

Successful same-day discharge in 88% of patients after unicompartmental knee arthroplasty: a systematic review and meta-analysis.

PURPOSE: The purpose of this study was to evaluate the effectiveness of day-case unicompartmental knee arthroplasty (UKA) by assessment of successful same-day discharge (SDD), readmission, complication and reoperation rates in the recent literature. METHODS: For this systematic review and meta-analysis, PubMed, Embase and Cochrane Library were comprehensively searched to identify all eligible studies reporting outcomes of day-case UKA. Studies with intended same-day home discharge after UKA were included. A meta-analysis of proportions, using a random-effects model, was performed to estimate overall rates of successful SDD and adverse events. Subgroup analyses were performed for studies including selected patients (i.e., patients had to meet certain patient-specific criteria to be eligible for day-case UKA) and unselected patients (i.e., no additional criteria for day-case UKA), as well as for clinical and registry-based studies. Additional outcomes included reasons for the failure of SDD and patient satisfaction. RESULTS: A total of 29 studies and 9694 patients were included with a mean age of 66 ± 9 years and mean follow-up of 59 days (mean range 30-270 days). Based on 24 studies (2733 patients), the overall successful SDD rate was 88% (95% confidence interval [CI] 80-92). These rates were 91% (95% CI 84-95) across studies with selected patients and 76% (95% CI 55-89) across studies with unselected patients. Overall readmission, complication and reoperation rates were 3% (95% CI 1.9-4.4), 4% (95% CI 2.8-5.2) and 1% (95% CI 0.8-1.3), respectively. Inability to mobilize, nausea and uncontrolled pain were frequently reported reasons for failed SDD. The overall patient satisfaction rate was 94%. CONCLUSION: This systematic review with meta-analysis found an overall successful SDD rate of 88% after UKA in a heterogeneous cohort of selected and unselected patients. Readmission, complication and reoperation rates suggest UKA can be performed safely and effectively as a same-day discharge procedure. LEVEL OF EVIDENCE: Level IV, systematic review of level III and IV studies.

Survival analyses and their applications in orthopaedics.

Survival analyses are a powerful statistical tool used to analyse data when the outcome of interest involves the time until an event. There is an array of models fit for this goal; however, there are subtle differences in assumptions, as well as a number of pitfalls, that can lead to biased results if researchers are unaware of the subtleties. As larger amounts of data become available, and more survival analyses are published every year, it is important that healthcare professionals understand how to evaluate these models and apply them into their practice. Therefore, the purpose of this study was to present an overview of survival analyses, including required assumptions and important pitfalls, as well as examples of their use within orthopaedic surgery.

Restoration or relative overcorrection of pre-arthritic coronal alignment leads to improved results following medial unicompartmental knee arthroplasty.

PURPOSE: A pre-arthritic alignment strategy for medial unicompartmental knee arthroplasty (UKA) aims to restore a patient's native lower limb alignment which may translate into improved outcomes. This study aimed to assess whether patients with pre-arthritically aligned knees versus patients with non-pre-arthritically aligned knees demonstrated improved mid-term outcomes and survivorship following medial UKA. The hypothesis was that pre-arthritic alignment in medial UKA would lead to better postoperative outcomes. METHODS: A retrospective study of 537 robotic-assisted fixed-bearing medial UKA was conducted. During this procedure, the surgical goal was to restore pre-arthritic alignment guided by re-tensioning of the medial collateral ligament (MCL). For study purposes, coronal alignment was retrospectively evaluated using the mechanical hip-knee-ankle angle (mHKA). Pre-arthritic alignment was estimated through the arithmetic hip-knee-ankle (aHKA) algorithm. Knees were grouped according to the difference between postoperative mHKA and estimated pre-arthritic alignment (i.e., mHKA - aHKA) as Group 1 (pre-arthritically aligned: mHKA restored within 2.0° of the aHKA), Group 2 (mHKA > 2.0° overcorrected relative to the aHKA), or Group 3 (mHKA > 2.0° undercorrected relative to the aHKA). Outcomes included the Knee Injury and Osteoarthritic Outcome Score for Joint Replacement (KOOS, JR), Kujala, proportions of knees achieving the patient acceptable symptom state (PASS) for these scores, and survivorship. PASS thresholds for KOOS, JR and Kujala were determined using a receiver operating characteristic curve method. RESULTS: A total of 369 knees were categorized as Group 1, 107 as Group 2, and 61 as Group 3. At 4.4 ± 1.6 years follow-up, mean KOOS, JR was comparable among groups, while Kujala was significantly worse in Group 3. The proportion of knees achieving the PASS for Kujala (76.5 points) was lower in Group 3 (n = 32; 59%) compared to Group 1 (n = 260; 74%) (p = 0.02). 5-year survivorship was higher in Group 1 and Group 2 (99% and 100%, respectively) compared to Group 3 (91%) (p = 0.04). CONCLUSION: Pre-arthritically aligned knees and knees with relative overcorrection from their pre-arthritic alignment following medial UKA demonstrated improved mid-term outcomes and survivorship compared to knees with relative under correction from their pre-arthritic alignment. These results encourage restoring or relatively overcorrecting pre-arthritic alignment to optimize outcomes following medial UKA, and caution against under correction from the pre-arthritic alignment. LEVEL OF EVIDENCE: IV, case series.

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.

Natural language processing: using artificial intelligence to understand human language in orthopedics.

Natural language processing (NLP) describes the broad field of artificial intelligence by which computers are trained to understand and generate human language. Within healthcare research, NLP is commonly used for variable extraction and classification/cohort identification tasks. While these tools are becoming increasingly popular and available as both open-source and commercial products, there is a paucity of the literature within the orthopedic space describing the key tasks within these powerful pipelines. Curation and navigation of the electronic medical record are becoming increasingly onerous, and it is important for physicians and other healthcare professionals to understand potential methods of harnessing this large data resource. The purpose of this study is to provide an overview of the tasks required to develop an NLP pipeline for orthopedic research and present recent examples of successful implementations.

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.

Artificial Intelligence for Automated Implant Identification in Knee Arthroplasty: A Multicenter External Validation Study Exceeding 3.5 Million Plain Radiographs.

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.

Arthroplasty Surgeons Differ in Their Intraoperative Soft Tissue Assessments: A Study in Human Cadavers to Quantify Surgical Decision-making in TKA.

BACKGROUND: In TKA, soft tissue balancing is assessed through manual intraoperative trialing. This assessment is a physical examination via manually applied forces at the ankle, generating varus and valgus moments at the knee while the surgeon visualizes the lateral and medial gaps at the joint line. Based on this examination, important surgical decisions are made that influence knee stability, such as choosing the polyethylene insert thickness. Yet, the applied forces and the assessed gaps in this examination represent a qualitative art that relies on each surgeon's intuition, experience, and training. Therefore, the extent of variation among surgeons in conducting this exam, in terms of applied loads and assessed gaps, is unknown. Moreover, whether variability in the applied loads yields different surgical decisions, such as choice of insert thickness, is also unclear. Thus, surgeons and developers have no basis for deciding to what extent the applied loads need to be standardized and controlled during a knee balance exam in TKA. QUESTIONS/PURPOSES: (1) Do the applied moments in soft tissue assessment differ among surgeons? (2) Do the assessed gaps in soft tissue assessment differ among surgeons? (3) Is the choice of insert thickness associated with the applied moments? METHODS: Seven independent human cadaveric nonarthritic lower extremities from pelvis to toe were acquired (including five females and two males with a mean age of 73 ± 7 years and a mean BMI of 25.8 ± 3.8 kg/m 2 ). Posterior cruciate ligament substituting (posterior stabilized) TKA was performed only on the right knees. Five fellowship-trained knee surgeons (with 24, 15, 15, 7, and 6 years of clinical experience) and one chief orthopaedic resident independently examined soft tissue balance in each knee in extension (0° of flexion), midflexion (30° of flexion), and flexion (90° of flexion) and selected a polyethylene insert based on their assessment. Pliable force sensors were wrapped around the leg to measure the loads applied by each surgeon. A three-dimensional (3D) motion capture system was used to measure knee kinematics and a dynamic analysis software was used to estimate the medial and lateral gaps. We assessed (1) whether surgeons applied different moments by comparing the mean applied moment by surgeons in extension, midflexion, and flexion using repeated measures (RM)-ANOVA (p < 0.05 was assumed significantly different); (2) whether surgeons assessed different gaps by comparing the mean medial and lateral gaps in extension, midflexion, and flexion using RM-ANOVA (p < 0.05 was assumed significantly different); and (3) whether the applied moments in extension, midflexion, and flexion were associated with the insert thickness choice using a generalized estimating equation (p < 0.05 was assumed a significant association). RESULTS: The applied moments differed among surgeons, with the largest mean differences occurring in varus in midflexion (16.5 Nm; p = 0.02) and flexion (7.9 Nm; p < 0.001). The measured gaps differed among surgeons at all flexion angles, with the largest mean difference occurring in flexion (1.1 ± 0.4 mm; p < 0.001). In all knees except one, the choice of insert thickness varied by l mm among surgeons. The choice of insert thickness was weakly associated with the applied moments in varus (ß = -0.06 ± 0.02 [95% confidence interval -0.11 to -0.01]; p = 0.03) and valgus (ß = -0.09 ± 0.03 [95% CI -0.18 to -0.01]; p= 0.03) in extension and in varus in flexion (ß = -0.11 ± 0.04 [95% CI -0.22 to 0.00]; p = 0.04). To put our findings in context, the greatest regression coefficient (ß = -0.11) indicates that for every 9-Nm increase in the applied varus moment (that is, 22 N of force applied to the foot assuming a shank length of 0.4 m), the choice of insert thickness decreased by 1 mm. CONCLUSION: In TKA soft tissue assessment in a human cadaver model, five surgeons and one chief resident applied different moments in midflexion and flexion and targeted different gaps in extension, midflexion, and flexion. A weak association between the applied moments in extension and flexion and the insert choice was observed. Our results indicate that in the manual assessment of soft tissue, changes in the applied moments of 9 and 11 Nm (22 to 27 N on the surgeons' hands) in flexion and extension, respectively, yielded at least a 1-mm change in choice of insert thickness. The choice of insert thickness may be more sensitive to the applied moments in in vivo surgery because the surgeon is allowed a greater array of choices beyond insert thickness. CLINICAL RELEVANCE: Among five arthroplasty surgeons with different levels of experience and a chief resident, subjective soft tissue assessment yielded 1 to 2 mm of variation in their choice of insert thickness. Therefore, developers of tools to standardize soft tissue assessment in TKA should consider controlling the force applied by the surgeon to better control for variations in insert selection.

Comparable incidence of periprosthetic tibial fractures in cementless and cemented unicompartmental knee arthroplasty: a systematic review and meta-analysis.

PURPOSE: (I) To determine the incidence of periprosthetic tibial fractures in cemented and cementless unicompartmental knee arthroplasty (UKA) and (II) to summarize the existing evidence on characteristics and risk factors of periprosthetic fractures in UKA. METHODS: Pubmed, Cochrane and Embase databases were comprehensively searched. Any clinical, laboratory or case report study describing information on proportion, characteristics or risk factors of periprosthetic tibial fractures in UKA was included. Proportion meta-analysis was performed to estimate the incidence of fractures only using data from clinical studies. Information on characteristics and risk factors was evaluated and summarized. RESULTS: A total of 81 studies were considered to be eligible for inclusion. Based on 41 clinical studies, incidences of fractures were 1.24% (95%CI 0.64-2.41) for cementless and 1.58% (95%CI 1.06-2.36) for cemented UKAs (9451 UKAs). The majority of fractures in the current literature occurred during surgery or presented within 3 months postoperatively (91 of 127; 72%) and were non-traumatic (95 of 113; 84%). Six different fracture types were observed in 21 available radiographs. Laboratory studies revealed that an excessive interference fit (press fit), excessive tibial bone resection, a sagittal cut too deep posteriorly and low bone mineral density (BMD) reduce the force required for a periprosthetic tibial fracture to occur. Clinical studies showed that periprosthetic tibial fractures were associated with increased body mass index and postoperative alignment angles, advanced age, decreased BMD, female gender, and a very overhanging medial tibial condyle. CONCLUSION: Comparable low incidences of periprosthetic tibial fractures in cementless and cemented UKA can be achieved. However, surgeons should be aware that an excessive interference fit in cementless UKAs in combination with an impaction technique may introduce an additional risk, and could therefore be less forgiving to surgical errors and patients who are at higher risk of periprosthetic tibial fractures. LEVEL OF EVIDENCE: V.

Patient and Surgeon Risk-Taking Regarding Total Joint Arthroplasty.

BACKGROUND: Decisions regarding care for osteoarthritis involve physicians helping patients understand likely benefits and harms of treatment. Little work has directly compared patient and surgeon risk-taking attitudes, which may help inform strategies for shared decision-making and improve patient satisfaction. METHODS: We surveyed patients contemplating total joint arthroplasty visiting a high-volume specialty hospital regarding general questions about risk-taking, as well as willingness to undergo surgery under hypothetical likelihoods of moderate improvement and complications. We compared responses from surgeons answering similar questions about willingness to recommend surgery. RESULTS: Altogether 82% (162/197) of patients responded, as did 65% (30/46) of joint replacement surgeons. Mean age among patients was 66.4 years; 58% were female. Surgeons averaged 399 surgeries in 2019. Responses were similar between groups for general, health, career, financial, and sports/leisure risk-taking (P > .20); surgeons were marginally more risk-taking in driving (P = .05). For willingness to have or recommend surgery, as the chance of benefit decreased, or the chance of harm increased, the percentage willing to have or recommend surgery decreased. Between a 70% and 95% chance of moderate improvement (for a 2% complication risk), as well as between a 90% and 95% chance of moderate improvement (for 4% and 6% complication risks), the percentage willing to have or recommend surgery was indistinguishable between patients and surgeons. However, for lower likelihoods of improvement, a higher percentage of patients were willing to undergo surgery than surgeons recommended. Patients were also more often indifferent between complication risks. CONCLUSION: Although patients and surgeons were often willing to have or recommend joint replacement surgery at similar rates, they diverged for lower-benefit higher-harm scenarios.

Editorial Commentary: The Pivot Shift and Lachman Examinations: Teammates With Distinct Roles.

The pivot shift and Lachman examinations are "teammates" with complementary but distinct roles in the successful diagnosis and treatment of anterior cruciate ligament rupture and injury to the surrounding soft-tissue envelope of the knee. The Lachman test measures anterior tibial translation in response to an applied anterior tibial load. This test assesses the integrity of the native or reconstructed anterior cruciate ligament and the secondary medial restraints including the medial meniscus and medial collateral ligament. In contrast, the pivot shift exam creates coupled tibiofemoral motions in response to a complex combination of multiplanar loads. This test assesses the stabilizing role of the native or reconstructed anterior cruciate ligament and the secondary lateral restraints including the lateral meniscus and anterolateral complex. The pivot shift grade depends not only on the soft the tissue stabilizers of the knee but also on the shape of the proximal tibia and the distal femur including lateral tibial slope and femoral condylar offset. Both examinations have unique strengths and weaknesses, but when combined as diagnostic tools, they achieve far more collectively than what each can achieve alone.

Complications Following Biologic Therapeutic Injections: A Multicenter Case Series.

PURPOSE: To describe the complications that occur following biologic therapeutic injections. METHODS: We queried physician members of the Biologic Association, a multidisciplinary organization dedicated to providing a unified voice for all matters related to musculoskeletal biologics and regenerative medicine. Patients included in this study must have (1) received a biologic injection, (2) sustained an adverse reaction, and (3) had a minimum of 1-year follow-up after the injection. Patient demographic information, medical comorbidities, diagnoses, and previous treatments were recorded. The type of injection, injection setting, injection manufacturers, and specific details about the complication and outcome were collected. RESULTS: In total, 14 patients were identified across 6 institutions in the United States (mean age 63 years, range: 36-83 years). The most common injections in this series were intra-articular knee injections (50%), followed intra-articular shoulder injections (21.4%). The most common underlying diagnosis was osteoarthritis (78.5%). Types of injections included umbilical cord blood, platelet-rich plasma, bone marrow aspirate concentrate, placental tissue, and unspecified "stem cell" injections. Complications included infection (50%), suspected sterile inflammatory response (42.9%), and a combination of both (7.1%). The most common pathogen identified from infection cases was Escherichia coli (n = 4). All patients who had isolated infections underwent treatment with at least one subsequent surgical intervention (mean: 3.6, range: 1-12) and intravenous antibiotic therapy. CONCLUSIONS: This study demonstrates that serious complications can occur following treatment with biologic injections, including infections requiring multiple surgical procedures and inflammatory reactions. LEVEL OF EVIDENCE: Level IV, case series.