Impact of Deltoid Computer Tomography Image Data on the Accuracy of Machine Learning Predictions of Clinical Outcomes after Anatomic and Reverse Total Shoulder Arthroplasty.

Background: Despite the importance of the deltoid to shoulder biomechanics, very few studies have quantified the three-dimensional shape, size, or quality of the deltoid muscle, and no studies have correlated these measurements to clinical outcomes after anatomic (aTSA) and/or reverse (rTSA) total shoulder arthroplasty in any statistically/scientifically relevant manner. Methods: Preoperative computer tomography (CT) images from 1057 patients (585 female, 469 male; 799 primary rTSA and 258 primary aTSA) of a single platform shoulder arthroplasty prosthesis (Equinoxe; Exactech, Inc., Gainesville, FL) were analyzed in this study. A machine learning (ML) framework was used to segment the deltoid muscle for 1057 patients and quantify 15 different muscle characteristics, including volumetric (size, shape, etc.) and intensity-based Hounsfield (HU) measurements. These deltoid measurements were correlated to postoperative clinical outcomes and utilized as inputs to train/test ML algorithms used to predict postoperative outcomes at multiple postoperative timepoints (1 year, 2-3 years, and 3-5 years) for aTSA and rTSA. Results: Numerous deltoid muscle measurements were demonstrated to significantly vary with age, gender, prosthesis type, and CT image kernel; notably, normalized deltoid volume and deltoid fatty infiltration were demonstrated to be relevant to preoperative and postoperative clinical outcomes after aTSA and rTSA. Incorporating deltoid image data into the ML models improved clinical outcome prediction accuracy relative to ML algorithms without image data, particularly for the prediction of abduction and forward elevation after aTSA and rTSA. Analyzing ML feature importance facilitated rank-ordering of the deltoid image measurements relevant to aTSA and rTSA clinical outcomes. Specifically, we identified that deltoid shape flatness, normalized deltoid volume, deltoid voxel skewness, and deltoid shape sphericity were the most predictive image-based features used to predict clinical outcomes after aTSA and rTSA. Many of these deltoid measurements were found to be more predictive of aTSA and rTSA postoperative outcomes than patient demographic data, comorbidity data, and diagnosis data. Conclusions: While future work is required to further refine the ML models, which include additional shoulder muscles, like the rotator cuff, our results show promise that the developed ML framework can be used to evolve traditional CT-based preoperative planning software into an evidence-based ML clinical decision support tool.

Initial clinical experience with a predictive clinical decision support tool for anatomic and reverse total shoulder arthroplasty.

PURPOSE: Clinical decision support tools (CDSTs) are software that generate patient-specific assessments that can be used to better inform healthcare provider decision making. Machine learning (ML)-based CDSTs have recently been developed for anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty to facilitate more data-driven, evidence-based decision making. Using this shoulder CDST as an example, this external validation study provides an overview of how ML-based algorithms are developed and discusses the limitations of these tools. METHODS: An external validation for a novel CDST was conducted on 243 patients (120F/123M) who received a personalized prediction prior to surgery and had short-term clinical follow-up from 3 months to 2 years after primary aTSA (n = 43) or rTSA (n = 200). The outcome score and active range of motion predictions were compared to each patient's actual result at each timepoint, with the accuracy quantified by the mean absolute error (MAE). RESULTS: The results of this external validation demonstrate the CDST accuracy to be similar (within 10%) or better than the MAEs from the published internal validation. A few predictive models were observed to have substantially lower MAEs than the internal validation, specifically, Constant (31.6% better), active abduction (22.5% better), global shoulder function (20.0% better), active external rotation (19.0% better), and active forward elevation (16.2% better), which is encouraging; however, the sample size was small. CONCLUSION: A greater understanding of the limitations of ML-based CDSTs will facilitate more responsible use and build trust and confidence, potentially leading to greater adoption. As CDSTs evolve, we anticipate greater shared decision making between the patient and surgeon with the aim of achieving even better outcomes and greater levels of patient satisfaction.

Evaluating the fairness and accuracy of machine learning-based predictions of clinical outcomes after anatomic and reverse total shoulder arthroplasty.

BACKGROUND: Machine learning (ML)-based clinical decision support tools (CDSTs) make personalized predictions for different treatments; by comparing predictions of multiple treatments, these tools can be used to optimize decision making for a particular patient. However, CDST prediction accuracy varies for different patients and also for different treatment options. If these differences are sufficiently large and consistent for a particular subcohort of patients, then that bias may result in those patients not receiving a particular treatment. Such level of bias would deem the CDST "unfair." The purpose of this study is to evaluate the "fairness" of ML CDST-based clinical outcomes predictions after anatomic (aTSA) and reverse total shoulder arthroplasty (rTSA) for patients of different demographic attributes. METHODS: Clinical data from 8280 shoulder arthroplasty patients with 19,249 postoperative visits was used to evaluate the prediction fairness and accuracy associated with the following patient demographic attributes: ethnicity, sex, and age at the time of surgery. Performance of clinical outcome and range of motion regression predictions were quantified by the mean absolute error (MAE) and performance of minimal clinically important difference (MCID) and substantial clinical benefit classification predictions were quantified by accuracy, sensitivity, and the F1 score. Fairness of classification predictions leveraged the "four-fifths" legal guideline from the US Equal Employment Opportunity Commission and fairness of regression predictions leveraged established MCID thresholds associated with each outcome measure. RESULTS: For both aTSA and rTSA clinical outcome predictions, only minor differences in MAE were observed between patients of different ethnicity, sex, and age. Evaluation of prediction fairness demonstrated that 0 of 486 MCID (0%) and only 3 of 486 substantial clinical benefit (0.6%) classification predictions were outside the 20% fairness boundary and only 14 of 972 (1.4%) regression predictions were outside of the MCID fairness boundary. Hispanic and Black patients were more likely to have ML predictions out of fairness tolerance for aTSA and rTSA. Additionally, patients <60 years old were more likely to have ML predictions out of fairness tolerance for rTSA. No disparate predictions were identified for sex and no disparate regression predictions were observed for forward elevation, internal rotation score, American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form score, or global shoulder function. CONCLUSION: The ML algorithms analyzed in this study accurately predict clinical outcomes after aTSA and rTSA for patients of different ethnicity, sex, and age, where only 1.4% of regression predictions and only 0.3% of classification predictions were out of fairness tolerance using the proposed fairness evaluation method and acceptance criteria. Future work is required to externally validate these ML algorithms to ensure they are equally accurate for all legally protected patient groups.

Two-year clinical outcomes and complication rates in anatomic and reverse shoulder arthroplasty implanted with Exactech GPS intraoperative navigation.

INTRODUCTION: We compared the 2-year clinical outcomes of both anatomic and reverse total shoulder arthroplasty (ATSA and RTSA) using intraoperative navigation compared to traditional positioning techniques. We also examined the effect of glenoid implant retroversion on clinical outcomes. HYPOTHESIS: In both ATSA and RTSA, computer navigation would be associated with equal or better outcomes with fewer complications. Final glenoid version and degree of correction would not show outcome differences. MATERIAL AND METHODS: A total of 216 ATSAs and 533 RTSAs were performed using preoperative planning and intraoperative navigation with a minimum of 2-year follow-up. Matched cohorts (2:1) for age, gender, and follow-up for cases without intraoperative navigation were compared using all standard shoulder arthroplasty clinical outcome metrics. Two subanalyses were performed on navigated cases comparing glenoids positioned greater or less than 10° of retroversion and glenoids corrected more or less than 15°. RESULTS: For ASTA, no statistical differences were found between the navigated and non-navigated cohorts for postoperative complications, glenoid implant loosening, or revision rate. No significant differences were seen in any of the ATSA outcome metrics besides higher internal and external rotation in the navigated cohort. For RTSA, the navigated cohort showed an ARR of 1.7% (95% CI 0%, 3.4%) for postoperative complications and 0.7% (95% CI 0.1%, 1.2%) for dislocations. No difference was found in the revision rate, glenoid implant loosening, acromial stress fracture rates, or scapular notching. Navigated RTSA patients demonstrated significant improvements over non-navigated patients in internal rotation, external rotation, maximum lifting weight, the Simple Shoulder Test (SST), Constant, and Shoulder Arthroplasty Smart (SAS) scores. For the navigated subcohorts, ATSA cases with a higher degree of final retroversion showed significant improvement in pain, Constant, American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES), SST, University of California-Los Angeles shoulder score (UCLA), and Shoulder Pain and Disability Index (SPADI) scores. No significant differences were found in the RTSA subcohort. Higher degrees of version correction showed improvement in external rotation, SST, and Constant scores for ATSA and forward elevation, internal rotation, pain, SST, Constant, ASES, UCLA, SPADI, and SAS scores for RTSA. CONCLUSION: The use of intraoperative navigation shoulder arthroplasty is safe, produces at least equally good outcomes at 2 years as standard instrumentation does without any increased risk of complications. The effect of final implant position above or below 10° of glenoid retroversion and correction more or less than 15° does not negatively impact outcomes.

Using machine learning to predict internal rotation after anatomic and reverse total shoulder arthroplasty.

BACKGROUND: Improvement in internal rotation (IR) after anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty is difficult to predict, with rTSA patients experiencing greater variability and more limited IR improvements than aTSA patients. The purpose of this study is to quantify and compare the IR score for aTSA and rTSA patients and create supervised machine learning that predicts IR after aTSA and rTSA at multiple postoperative time points. METHODS: Clinical data from 2270 aTSA and 4198 rTSA patients were analyzed using 3 supervised machine learning techniques to create predictive models for internal rotation as measured by the IR score at 6 postoperative time points. Predictions were performed using the full input feature set and 2 minimal input feature sets. The mean absolute error (MAE) quantified the difference between actual and predicted IR scores for each model at each time point. The predictive accuracy of the XGBoost algorithm was also quantified by its ability to distinguish which patients would achieve clinical improvement greater than the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) patient satisfaction thresholds for IR score at 2-3 years after surgery. RESULTS: rTSA patients had significantly lower mean IR scores and significantly less mean IR score improvement than aTSA patients at each postoperative time point. Both aTSA and rTSA patients experienced significant improvements in their ability to perform activities of daily living (ADLs); however, aTSA patients were significantly more likely to perform these ADLs. Using a minimal feature set of preoperative inputs, our machine learning algorithms had equivalent accuracy when predicting IR score for both aTSA (0.92-1.18 MAE) and rTSA (1.03-1.25 MAE) from 3 months to >5 years after surgery. Furthermore, these predictive algorithms identified with 90% accuracy for aTSA and 85% accuracy for rTSA which patients will achieve MCID IR score improvement and predicted with 85% accuracy for aTSA patients and 77% accuracy for rTSA which patients will achieve SCB IR score improvement at 2-3 years after surgery. DISCUSSION: Our machine learning study demonstrates that active internal rotation can be accurately predicted after aTSA and rTSA at multiple postoperative time points using a minimal feature set of preoperative inputs. These predictive algorithms accurately identified which patients will, and will not, achieve clinical improvement in IR score that exceeds the MCID and SCB patient satisfaction thresholds.

Guía práctica de planificación e impresión 3D en Ortopedia y Traumatología / Practical guide to 3D printing and planning in orthopedics and traumatology

El acceso a la tecnología de planificación e impresión 3D está destinado a tener un impacto disruptivo en la práctica quirúrgica de la Ortopedia y Traumatología. Sus ventajas incluyen una mejor comprensión de las lesiones por tratar, mayor precisión técnica, acortamiento de los tiempos quirúrgicos, disminución de la pérdida sanguínea y menor exposición a los rayos. El objetivo de esta publicación es ofrecer una guía práctica paso a paso tomando como ejemplo el tratamiento de una fractura desplazada del tercio medio de la clavícula. Nivel de Evidencia: V

Access to 3D planning and printing technology is destined to have a disruptive impact on the surgical practice of Orthopedics. Its advantages include a better understanding of the injuries, greater technical precision, shortened surgical times, decreased blood loss, and less exposure to X-rays. The aim of this publication is to provide a practical step-by-step guide using the treatment of a displaced middle-third clavicle fracture as an example. Level of Evidence: V

Current concepts in anterior glenohumeral instability: diagnosis and treatment.

The glenohumeral joint is the most dislocated articulation, accounting for more than 50% of all joint dislocations. The reason behind shoulder instability should be investigated in detail for successful management, and the treatment plan should be individualized for all patients. Several classification systems have been proposed for glenohumeral instability. A physical exam is mandatory no matter what classification system is used. When treating patients with anterior shoulder instability, surgeons need to be aware of the critical size of the bone loss, which is commonly seen. The glenoid track concept was clinically adopted, and the measurement of the glenoid track for surgical decision-making is recommended. Detailed assessment of existing soft tissue injury to the labrum, capsule, glenohumeral ligaments, and rotator cuff is also mandatory as their presence influences the surgical outcome. Rehabilitation, arthroscopic repair techniques, open Bankart procedure, capsular plication, remplissage, Latarjet technique, iliac crest, and other bone grafts offer the surgeon different treatment options according to the type of patient and the lesions to be treated. Three-dimensional (3D) technologies can help to evaluate glenoid and humeral defects. Patient-specific guides are low-cost surgical instruments and can be used in shoulder instability surgery. 3D printing will undoubtedly become an essential tool to achieve the best results in glenohumeral instability surgery.

Risk factors for complications and revision surgery after anatomic and reverse total shoulder arthroplasty.

BACKGROUND: Complications and revisions following anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty have deleterious effects on patient function and satisfaction. The purpose of this study is to evaluate patient-specific, implant-specific and technique-specific risk factors for intraoperative complications, postoperative complications, and the occurrence of revisions after aTSA and rTSA. METHODS: A total of 2964 aTSA and 5616 rTSA patients were enrolled in an international database of primary shoulder arthroplasty. Intra- and postoperative complications, as well as revisions, were reported and evaluated. Multivariate analyses were performed to quantify the risk factors associated with complications and revisions. RESULTS: aTSA patients had a significantly higher complication rate (P = .0026) and a significantly higher revision rate (P < .0001) than rTSA patients, but aTSA patients also had a significantly longer average follow-up (P < .0001) than rTSA patients. No difference (P = .2712) in the intraoperative complication rate was observed between aTSA and rTSA patients. Regarding intraoperative complications, female sex (odds ratio [OR] 2.0, 95% confidence interval [CI] 1.17-3.68) and previous shoulder surgery (OR 2.9, 95% CI 1.73-4.90) were identified as significant risk factors. In regard to postoperative complications, younger age (OR 0.987, 95% CI 0.977-0.996), diagnosis of rheumatoid arthritis (OR 1.76, 95% 1.12-2.65), and previous shoulder surgery (OR 1.42, 95% CI 1.16-1.72) were noted to be risks factors. Finally, in regard to revision surgery, younger age (OR 0.964, 95% CI 0.933-0.998), more glenoid retroversion (OR 1.03, 95% CI 1.001-1.058), larger humeral stem size (OR 1.09, 95% CI 1.01-1.19), larger humeral liner thickness or offset (OR 1.50, 95% CI 1.18-1.96), larger glenosphere diameter (OR 1.16, 95% CI 1.07-1.26), and more intraoperative blood loss (OR 1.002, 95% CI 1.001-1.004) were noted to be risk factors. CONCLUSIONS: Studying the impact of numerous patient- and implant-specific risk factors and determining their impact on complications and revision shoulder arthroplasty can assist surgeons in counseling patients and guide patient expectations following aTSA or rTSA. Care should be taken in patients with a history of previous shoulder surgery, who are at increased risk of both intra- and postoperative complications.

Three-Dimensional Printing in Orthopedics: from the Basics to Surgical Applications.

PURPOSE OF REVIEW: Additive manufacturing (AM) is a rapidly evolving field traditionally utilized in non-medical industries. Recently, the medical use of AM is expanding, especially in orthopedics. The goal of this article is presenting the principles of AM and its main applications in orthopedics. RECENT FINDINGS: The main indications for AM in orthopedics are education, orthotics, surgical planning, surgical guides, and custom-made implants. Three-dimensional (3D) digital models can be obtained from tomographic scans using available free software. Then, it can be used to create a physical model, plan surgeries, or develop surgical guides which can aid the orthopedic surgeon during complex cases. Recent studies demonstrated the benefits of using printed models in educating patients and medical residents. Custom-made implants also have been evaluated with promising clinical outcomes. Using 3D technology has become a reality in orthopedics. Surgeons should expect exponential growth of its applications in the upcoming years. It is paramount that orthopedists get familiar with this disruptive technology.