Influence of subscapularis stiffness with glenosphere lateralization on physiological external rotation limits after reverse shoulder arthroplasty.

BACKGROUND: Repair of the subscapularis following reverse shoulder arthroplasty (RSA) remains a controversial topic among surgeons. Poor rotator cuff muscle quality is associated with increased musculotendinous stiffness, and the subsequent effect of compromised tissue repair on RSA functional outcomes remains unclear. The objective was to investigate the influence of subscapularis stiffness together with glenoid component lateralization on pre- and postimpingement joint mechanics during external rotation after RSA. METHODS: A validated finite element model incorporating the Zimmer Trabecular Metal reverse system was used. The deltoid and subscapularis tendon were tensioned and wrapped around the joint prior to controlled shoulder external rotation. Baseline subscapularis stiffness, determined from cadaveric testing, was varied to 80%, 120% and 140% of baseline, to simulate a range of pliability associated with fatty infiltration and fibrosis. We evaluated the effects of varying subscapularis stiffness and the corresponding variation in joint tension with varying glenosphere lateralization (2, 4, and 10 mm) on the torque required to externally rotate the shoulder and the impingement/subluxation risk. RESULTS: Prior to any impingement, the torques required to externally rotate the shoulder ranged from 22-47 Nm across the range of parameters studied, with the greatest torques required for the 10-mm glenosphere lateralization. The impact of increasing subscapularis stiffness on torque requirements was most pronounced at the 10-mm lateralization, as well. A 20% increase in subscapularis stiffness necessitated a 7%-14% increase in preimpingement torque, whereas a 40% stiffness increase was associated with a 12%-27% increase in torque. Torque was proportional to lateralization. When lateralization was increased from 2 to 4 mm, the preimpingement torque increased by 10%-13%, whereas a 10-mm lateralization necessitated a 35%-62% torque increase relative to 2 mm of lateralization. Increased subscapularis stiffness did not limit impingement-free range of motion or substantially decrease postimpingement subluxation in this model. DISCUSSION: Mechanical gains achieved through lateralization may be hindered by increased torque demands, especially when a stiffer subscapularis is repaired. As lateralization increases subscapularis tension, greater torque is required to externally rotate the shoulder. The torque required for external rotation has been reported between 15-50 Nm. Subscapularis repair with the simulated increases in stiffness requires relative increases in torque that the reconstructed shoulder may not be able to physically produce to rotate the glenohumeral joint, particularly at 10-mm lateralization. These results suggest that subscapularis repair may not be indicated in cases where a lateralized glenoid component is used and the subscapularis is compromised.

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Knee.

BACKGROUND: Revisions and reoperations for patients who have undergone total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), and distal femoral replacement (DFR) necessitates accurate identification of implant manufacturer and model. Failure risks delays in care, increased morbidity, and further financial burden. Deep learning permits automated image processing to mitigate the challenges behind expeditious, cost-effective preoperative planning. Our aim was to investigate whether a deep-learning algorithm could accurately identify the manufacturer and model of arthroplasty implants about the knee from plain radiographs. METHODS: We trained, validated, and externally tested a deep-learning algorithm to classify knee arthroplasty implants from one of 9 different implant models from retrospectively collected anterior-posterior (AP) plain radiographs from four sites in one quaternary referral health system. The performance was evaluated by calculating the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, and accuracy when compared with a reference standard of implant model from operative reports. RESULTS: The training and validation data sets were comprised of 682 radiographs across 424 patients and included a wide range of TKAs from the four leading implant manufacturers. After 1000 training epochs by the deep-learning algorithm, the model discriminated nine implant models with an AUC of 0.99, accuracy 99%, sensitivity of 95%, and specificity of 99% in the external-testing data set of 74 radiographs. CONCLUSIONS: A deep learning algorithm using plain radiographs differentiated between 9 unique knee arthroplasty implants from four manufacturers with near-perfect accuracy. The iterative capability of the algorithm allows for scalable expansion of implant discriminations and represents an opportunity in delivering cost-effective care for revision arthroplasty.

Are We Treating Similar Patients? Hospital Volume and the Difference in Patient Populations for Total Knee Arthroplasty.

BACKGROUND: Early findings of superior total knee arthroplasty (TKA) outcomes at high volume centers have been thought to have led to distinct referral patterns. However, the effect of these selective referral processes has not been well assessed. Therefore, this study compared the characteristics of primary TKA patients at high, intermediate, and low volume hospitals. METHODS: A total of 12,541 primary TKA patients were stratified into risk groups based on age (>65 years), body mass index (>40), and Charlson Comorbidity Index (≥4). Hospitals were classified as low, intermediate, or high volume based on mean annual TKA volumes (<250, 250-499, and >500). Multivariate logistic regression models evaluated the relationship between baseline patient characteristics and hospital volume. RESULTS: There was a greater percentage of high risk patients at high volume (19%, n = 853) compared to those at intermediate (16%, n = 899) or low volume (17%, n = 444) hospitals (P < .001). Patients with a body mass index >40 were more likely to be treated at high compared to intermediate (odds ratio [OR] 1.4, 95% confidence interval [CI] 1.2-1.6, P < .001) and low volume centers (OR 1.4, 95% CI 1.2-1.7, P < .001). Patients with Charlson Comorbidity Index scores ≥4 were also more likely be treated at high compared to intermediate (OR 1.5, 95% CI 1.3-1.6, P < .001) or low (OR 1.2, 95% CI 1.0-1.4, P = .002) volume centers. CONCLUSION: This study found that TKA patients at high volume centers have significantly different baseline characteristics compared to those at lower volume centers. This study highlights the importance of considering hospital volume status and the associated disparity in the preoperative risk of patients when comparing primary TKA outcomes between centers.

Correction to: Skin closure with 2-octyl cyanoacrylate and polyester mesh after primary total knee arthroplasty offers superior cosmetic outcomes and patient satisfaction compared to staples: a prospective trial.

The original version of this article unfortunately contained a mistake.

Skin closure with 2-octyl cyanoacrylate and polyester mesh after primary total knee arthroplasty offers superior cosmetic outcomes and patient satisfaction compared to staples: a prospective trial.

INTRODUCTION: The goals of this study were to compare patient satisfaction and wound-related complications in patients receiving 2-octyl cyanoacrylate (glue) and polyester mesh for skin closure after primary total knee arthroplasty (TKA) versus staples. METHODS: A total of 60 knees in 54 patients undergoing TKA were enrolled in a prospective trial and randomized to receive either skin closure with glue and polyester mesh (n = 30) or a control group closed with staples (n = 30). Hollander wound cosmesis score (measured on a scale of 0-5, with 0 being the best) and visual analog scale (VAS) scores (range 0-100 mm) for patient satisfaction with wound cosmesis, as well as wound-related readmission, reoperation, and complications, including superficial surgical site infection, wound dehiscence, wound hematoma, stitch abscess, and wound discharge, were assessed at 6 weeks and 90 days after TKA. Baseline characteristics were not statistically significantly different between the groups (p > 0.05). RESULTS: At 6 weeks and 90 days, the Hollander wound cosmesis score was significantly lower (p < 0.01) in the glue and polyester mesh groups. Similarly, at 6 weeks and 90 days, the VAS for patient satisfaction with wound cosmesis was significantly higher (p < 0.01) in the glue and polyester mesh groups. The rate of superficial surgical site infection was 1/30 (3%) in glue and polyester mesh groups versus zero in the control group (p = 1.00). The rate of wound dehiscence was 1/30 (3%) in glue and polyester mesh groups versus zero in the control group (p = 1.00). CONCLUSION: These results suggest that glue and polyester mesh closure may offer superior cosmetic outcomes to staples for skin closure in TKA.

Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring.

BACKGROUND: Driven by the recent ubiquity of big data and computing power, we established the Machine Learning Arthroplasty Laboratory (MLAL) to examine and apply artificial intelligence (AI) to musculoskeletal medicine. METHODS: In this review, we discuss the 2 core objectives of the MLAL as they relate to the practice and progress of orthopedic surgery: (1) patient-specific, value-based care and (2) human movement. RESULTS: We developed and validated several machine learning-based models for primary lower extremity arthroplasty that preoperatively predict patient-specific, risk-adjusted value metrics, including cost, length of stay, and discharge disposition, to provide improved expectation management, preoperative planning, and potential financial arbitration. Additionally, we leveraged passive, ubiquitous mobile technologies to build a small data registry of human movement surrounding TKA that permits remote patient monitoring to evaluate therapy compliance, outcomes, opioid intake, mobility, and joint range of motion. CONCLUSION: The rapid rate with which we in arthroplasty are acquiring and storing continuous data, whether passively or actively, demands an advanced processing approach: AI. By carefully studying AI techniques with the MLAL, we have applied this evolving technique as a first step that may directly improve patient outcomes and practice of orthopedics.

Remote Patient Monitoring Using Mobile Health for Total Knee Arthroplasty: Validation of a Wearable and Machine Learning-Based Surveillance Platform.

BACKGROUND: Recent technologic advances capable of measuring outcomes after total knee arthroplasty (TKA) are critical in quantifying value-based care. Traditionally accomplished through office assessments and surveys with variable follow-up, this strategy lacks continuous and complete data. The primary objective of this study was to validate the feasibility of a remote patient monitoring (RPM) system in terms of the frequency of data interruptions and patient acceptance. Second, we report pilot data for (1) mobility; (2) knee range of motion, (3) patient-reported outcome measures (PROMs); (4) opioid use; and (5) home exercise program (HEP) compliance. METHODS: A pilot cohort of 25 patients undergoing primary TKA for osteoarthritis was enrolled. Patients downloaded the RPM mobile application preoperatively to collect baseline activity and PROMs data, and the wearable knee sleeve was paired to the smartphone during admission. The following was collected up to 3 months postoperatively: mobility (step count), range of motion, PROMs, opioid consumption, and HEP compliance. Validation was determined by acquisition of continuous data and patient tolerance at semistructured interviews 3 months after operation. RESULTS: Of the 25 enrolled patients, 100% had uninterrupted passive data collection. Of the 22 available for follow-up interviews, all found the system motivating and engaging. Mean mobility returned to baseline within 6 weeks and exceeded preoperative baseline by 30% at 3 months. Mean knee flexion achieved was 119°, which did not differ from clinic measurements (P = .31). Mean KOOS improvement was 39.3 after 3 months (range: 3-60). Opioid use typically stopped by postoperative day 5. HEP compliance was 62% (range: 0%-99%). CONCLUSIONS: In this pilot study, we established the ability to remotely acquire continuous data for patients undergoing TKA, who found the application to be engaging. RPM offers the newfound ability to more completely evaluate the patients undergoing TKA in terms of mobility and rehabilitation compliance. Study with more patients is required to establish clinical significance.

Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model.

BACKGROUND: The objective is to develop and validate an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition before primary total knee arthroplasty (TKA). The secondary objective applied the ANN to propose a risk-based, patient-specific payment model (PSPM) commensurate with case complexity. METHODS: Using data from 175,042 primary TKAs from the National Inpatient Sample and an institutional database, an ANN was developed to predict LOS, charges, and disposition using 15 preoperative variables. Outcome metrics included accuracy and area under the curve for a receiver operating characteristic curve. Model uncertainty was stratified by All Patient Refined comorbidity indices in establishing a risk-based PSPM. RESULTS: The dynamic model demonstrated "learning" in the first 30 training rounds with areas under the curve of 74.8%, 82.8%, and 76.1% for LOS, charges, and discharge disposition, respectively. The PSPM demonstrated that as patient comorbidity increased, risk increased by 2.0%, 21.8%, and 82.6% for moderate, major, and severe comorbidities, respectively. CONCLUSION: Our deep learning model demonstrated "learning" with acceptable validity, reliability, and responsiveness in predicting value metrics, offering the ability to preoperatively plan for TKA episodes of care. This model may be applied to a PSPM proposing tiered reimbursements reflecting case complexity.

Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model.

BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create a patient-specific payment model (PSPM) accounting for patient complexity. METHODS: Using 15 preoperative variables from 78,335 primary total hip arthroplasty cases for osteoarthritis from the National Inpatient Sample and our institutional database, an ANN was developed to predict LOS, charges, and disposition. Validity metrics included accuracy and area under the curve of the receiver operating characteristic curve. Predictive uncertainty was stratified by All Patient Refined comorbidity cohort to establish the PSPM. RESULTS: The dynamic model demonstrated "learning" in the first 30 training rounds with areas under the curve of 82.0%, 83.4%, and 79.4% for LOS, charges, and disposition, respectively. The proposed PSPM established a risk increase of 2.5%, 8.9%, and 17.3% for moderate, major, and severe comorbidities, respectively. CONCLUSION: The deep learning ANN demonstrated "learning" with good reliability, responsiveness, and validity in its prediction of value-centered outcomes. This model can be applied to implement a PSPM for tiered payments based on the complexity of the case.

Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models.

BACKGROUND: Value-based payment programs in orthopedics, specifically primary total hip arthroplasty (THA), present opportunities to apply forecasting machine learning techniques to adjust payment models to a specific patient or population. The objective of this study is to (1) develop and validate a machine learning algorithm using preoperative big data to predict length of stay (LOS) and patient-specific inpatient payments after primary THA and (2) propose a risk-adjusted patient-specific payment model (PSPM) that considers patient comorbidity. METHODS: Using an administrative database, we applied 122,334 patients undergoing primary THA for osteoarthritis between 2012 and 16 to a naïve Bayesian model trained to forecast LOS and payments. Performance was determined using area under the receiver operating characteristic curve and percent accuracy. Inpatient payments were grouped as <$12,000, $12,000-$24,000, and >$24,000. LOS was grouped as 1-2, 3-5, and 6+ days. Payment model uncertainty was applied to a proposed risk-based PSPM. RESULTS: The machine learning algorithm required age, race, gender, and comorbidity scores ("risk of illness" and "risk of morbidity") to demonstrate excellent validity, reliability, and responsiveness with an area under the receiver operating characteristic curve of 0.87 and 0.71 for LOS and payment. As patient complexity increased, error for predicting payment increased in tiers of 3%, 12%, and 32% for moderate, major, and extreme comorbidities, respectively. CONCLUSION: Our preliminary machine learning algorithm demonstrated excellent construct validity, reliability, and responsiveness predicting LOS and payment prior to primary THA. This has the potential to allow for a risk-based PSPM prior to elective THA that offers tiered reimbursement commensurate with case complexity. LEVEL OF EVIDENCE: III.

Optimizing reverse shoulder arthroplasty component position in the setting of advanced arthritis with posterior glenoid erosion: a computer-enhanced range of motion analysis.

BACKGROUND: Our study purpose was to determine the optimal glenoid and humeral reverse shoulder arthroplasty (RSA) component design and position in osteoarthritic shoulders with severe glenoid retroversion deformities. METHODS: Computed tomography scans from 10 subjects were analyzed with advanced software including RSA range of motion (ROM) analysis. Variables included glenoid component retroversion of 0°, 5°, 10°, 15°, and 20° and baseplate lateralization of 0, 5, and 10 mm. Humeral variables included 135°, 145°, and 155° angle of inclination (AOI) combined with variable humeral offset. RESULTS: Glenoid component lateralization had the greatest influence on ROM. In comparing each ROM direction among all lateralization options independently, there were significantly greater adduction, abduction, external rotation, extension, and flexion motions with progressively greater lateralization. Internal rotation motion was greater at 10 mm only. In analyzing the effects of glenoid version independently, no differences in adduction or abduction ROM were seen. With greater retroversion, decreased external rotation and extension motion was noted; however, greater internal rotation and flexion motion was seen with the exception of flexion at 10 mm of lateralization. For adduction, external rotation, and extension, a more valgus AOI resulted in less ROM at each progressively greater AOI independent of humeral lateralization. Internal rotation and flexion motions were greater with a more varus AOI but not significant between each inclination angle. Abduction ROM was maximized with a more valgus AOI. Humeral lateralization had no effect on ROM. CONCLUSIONS: In the setting of RSA for advanced glenoid osteoarthritic deformities, optimal ROM is achieved with 10-mm baseplate lateralization and neutral to 5° of retroversion mated to a humeral implant with a varus (135°) inclination angle.

Total shoulder arthroplasty in patients with a B2 glenoid addressed with corrective reaming.

BACKGROUND: This study describes the short-term functional and radiographic outcomes after total shoulder arthroplasty (TSA) in shoulders with a B2 glenoid deformity addressed with corrective reaming. METHODS: We conducted a retrospective series of consecutive patients who underwent TSA with a Walch B2 glenoid quantified by computed tomography scan. All glenoid deformities were addressed using partially corrective glenoid reaming. Radiographic and functional outcome measures, including scores on the visual analog scale for pain, American Shoulder and Elbow Standardized Shoulder Assessment, and Simple Shoulder Test were collected. RESULTS: Functional outcome scores were available for 59 of 92 eligible subjects (64%) at a mean of 50 months. The mean preoperative retroversion measured 18° (range, -1° to 36°), superior inclination was 8° (range, -11° to 27°), and posterior subluxation was 67% (range, 39%-91%). Mean visual analog scale improved from 7.4 to 1.4, the American Shoulder and Elbow Shoulder Standardized Assessment improved from 35.4 to 84.3, and the SST improved from 4.5 to 9.1. Radiographs were evaluated at a mean of 31 months: 38 had no glenoid radiolucent lines, 13 glenoids had grade 1, 2 had grade 2, and 5 had grade 3 lucencies. There was no difference in the rate of progression of glenoid radiolucencies between shoulders with a preoperative glenoid version of ≤20° (27.8%) compared with glenoids with >20° of retroversion (22.7%, P = .670). No shoulders were revised due to glenoid loosening or instability. CONCLUSION: TSA with partial corrective glenoid reaming in selected shoulders with a B2 glenoid deformity resulted in excellent functional and radiographic outcomes at short-term follow-up, with a low risk of revision surgery.

Does prosthetic humeral articular surface positioning associate with outcome after total shoulder arthroplasty?

BACKGROUND: The purpose of this study was to determine the effect of humeral articular component positioning on changes in patient-reported outcomes after anatomic total shoulder arthroplasty. METHODS: This was a retrospective series of consecutive patients at 2 high-volume referral centers. The study included patients with (1) a preoperative and postoperative radiograph demonstrating a perfect or nearly perfect profile of the humerus and implant and (2) Simple Shoulder Test, visual analog scale for pain, and American Society of Shoulder and Elbow Surgeons (ASES) Standardized Shoulder Assessment scores preoperatively and at greater than 2 years postoperatively. Head height, head diameter, tuberosity-to-head height distance, inclination, and medial offset of the center of rotation (COR) were measured preoperatively and postoperatively. Distance and direction from the ideal COR to the reconstructed center of rotation was measured. Measurements were correlated with improvement in functional outcomes. RESULTS: The study included 95 patients, aged 66 ± 9 years, with a mean follow-up of 4.3 ± 1.7 years. An a priori power analysis suggested that a sample size of 95 patients provided 80% power to detect correlations of R2 = 0.07. The COR shift was >2 mm in 62% of patients and >4 mm 15%. Thirty-two percent had a change of ASES of <21 points. On multivariate analysis, there were no significant associations between any change in measured prosthetic radiographic parameters and changes in the visual analog scale, Simple Shoulder Test, or ASES scores (P > .05). CONCLUSION: In this retrospective analysis of total shoulder arthroplasty in which most components were well positioned, humeral component positioning did not associate with change in postoperative outcomes. These findings should be prospectively confirmed.

Correlation of Patient-Reported Outcomes Measurement Information System (PROMIS) scores with legacy patient-reported outcome scores in patients undergoing rotator cuff repair.

BACKGROUND: The Patient-Reported Outcomes Measurement Information System (PROMIS) is being used to assess outcomes in many patient populations despite limited validation. The purpose of this study was to investigate the relationship between American Shoulder and Elbow Surgeons (ASES) and Simple Shoulder Test (SST) scores and PROMIS Physical Function (PF) and Upper Extremity (UE) function scores collected preoperatively in patients undergoing rotator cuff repair. METHODS: This cross-sectional study analyzed 164 consecutive patients undergoing arthroscopic rotator cuff repair. Study inclusion required preoperative completion of the ASES and SST evaluations, as well as the PROMIS PF, UE, and Pain Interference computerized adaptive tests. Descriptive statistics were produced, and Pearson correlation coefficients were calculated between each of the outcome measures. RESULTS: Average PROMIS UE scores indicated greater impairment than PROMIS PF scores (34 vs 44). Three percent of patients reached the PROMIS UE ceiling score of 56. PROMIS PF scores demonstrated a weak correlation with ASES scores (r = 0.43, P < .001) and a moderate correlation with SST scores (r = 0.51, P < .001). PROMIS UE scores demonstrated a moderate correlation with both ASES scores (r = 0.59, P < .001) and SST scores (r = 0.62, P < .001). PROMIS Pain Interference scores demonstrated weak negative correlations with both ASES scores (r = -0.43, P < .001) and SST scores (r = -0.41, P < .001). Patients answered fewer questions on average using the PROMIS PF and UE instruments as compared with the ASES and SST instruments. CONCLUSION: PROMIS UE scores indicate greater impairment and demonstrate a stronger correlation with the legacy shoulder scores than PROMIS PF scores in patients with symptomatic rotator cuff tears. PROMIS computerized adaptive tests allow for more efficient patient-reported outcome data collection compared with traditional outcome scores.

Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model.

BACKGROUND: Value-based and patient-specific care represent 2 critical areas of focus that have yet to be fully reconciled by today's bundled care model. Using a predictive naïve Bayesian model, the objectives of this study were (1) to develop a machine-learning algorithm using preoperative big data to predict length of stay (LOS) and inpatient costs after primary total knee arthroplasty (TKA) and (2) to propose a tiered patient-specific payment model that reflects patient complexity for reimbursement. METHODS: Using 141,446 patients undergoing primary TKA from an administrative database from 2009 to 2016, a Bayesian model was created and trained to forecast LOS and cost. Algorithm performance was determined using the area under the receiver operating characteristic curve and the percent accuracy. A proposed risk-based patient-specific payment model was derived based on outputs. RESULTS: The machine-learning algorithm required age, race, gender, and comorbidity scores ("risk of illness" and "risk of morbidity") to demonstrate a high degree of validity with an area under the receiver operating characteristic curve of 0.7822 and 0.7382 for LOS and cost. As patient complexity increased, cost add-ons increased in tiers of 3%, 10%, and 15% for moderate, major, and extreme mortality risks, respectively. CONCLUSION: Our machine-learning algorithm derived from an administrative database demonstrated excellent validity in predicting LOS and costs before primary TKA and has broad value-based applications, including a risk-based patient-specific payment model.

Initial medical management of rotator cuff tears: a demographic analysis of surgical and nonsurgical treatment in the United States Medicare population.

BACKGROUND: Rotator cuff tears have a lifetime incidence between 25% and 40% in the United States, but optimum treatment strategies and protocol have not yet been widely accepted. This study evaluated the proportions of patients treated with nonoperative and operative modalities and how this proportion has changed during an 8-year period (2005-2012) among patients with Medicare. METHODS: Using the PearlDiver patient record database, we identified Medicare patients having been diagnosed with a rotator cuff tear. These patients were then stratified on the basis of treatment with physical therapy, subacromial/glenohumeral injection, or rotator cuff repair. We analyzed the data in regard to standard demographic information, comorbidities, and the Charlson Comorbidity Index. RESULTS: During the study period, 878,049 patients were identified and 397,116 patients had rotator cuff repair. The proportion of patients treated initially with physical therapy dropped from 30.0% in 2005 to 13.2% in 2012, and the subacromial/glenohumeral injection proportion decreased from 6.00% to 4.19% (P < .001). The proportion of patients who had rotator cuff repair increased from 33.8% to 40.4% from 2005 to 2012 (P < .001). Charlson Comorbidity Indexes were significantly lower in operative patients compared with each nonoperative treatment examined. DISCUSSION: This analysis demonstrates a significant decrease in the initial trial of nonoperative treatment and an increase in the rate of surgery. Patients undergoing rotator cuff repair had fewer comorbidities than those undergoing nonoperative treatments. It also demonstrates that patients who had a trial of injection had a higher incidence of eventual rotator cuff repair compared with the patients with an initial trial of physical therapy.

The influence of patient insurance status on access to outpatient orthopedic care for flexor tendon lacerations.

PURPOSE: To determine the effect of patient insurance status on access to outpatient orthopedic care for acute flexor tendon lacerations. METHODS: The research team contacted 100 randomly chosen orthopedic surgery practices in North Carolina by phone on 2 different occasions separated by 3 weeks. The research team attempted to obtain an appointment for a fictitious 28-year-old man with an acute flexor tendon laceration. Insurance status was presented as Medicaid in 1 call and private insurance in the other call. Ability of an office to schedule an appointment was recorded. RESULTS: Of the 100 practices, 13 were excluded because they did not perform hand surgery, which left 87 practices. The patient in the scenario with Medicaid was offered an appointment significantly less often (67%) than the patient in the scenario with private insurance (82%). The odds of the patient with private insurance obtaining an appointment were 2.2 times greater than the odds of the Medicaid patient obtaining an appointment. The Medicaid patient was more likely not to be offered an appointment owing to the lack of a hand surgeon at a practice (28% of appointment denials) than privately insured patients (13% of appointment denials). CONCLUSIONS: For patients with acute flexor tendon lacerations, insurance status has an important role in the ability to obtain an orthopedic clinic appointment. We found that patients with Medicaid have more barriers to accessing care for a flexor tendon laceration than patients with private insurance. TYPE OF STUDY/LEVEL OF EVIDENCE: Prognostic II.

Rate of incidental findings on routine preoperative computed tomography for shoulder arthroplasty.

BACKGROUND: Incidental findings are commonly noted in advanced imaging studies. Few data exist regarding the rate of incidental findings on computed tomography (CT) for preoperative shoulder arthroplasty planning. This study aims to identify the incidence of these findings and the rate at which they warrant further work-up to help guide orthopedic surgeons in counseling patients. METHODS: A retrospective review was performed to identify patients with available preoperative shoulder CT who subsequently underwent shoulder arthroplasty procedures at a single institution between 2015 and 2021. Data including age, sex, and smoking status were obtained. Radiology reports for CTs were reviewed for incidental findings and categorized based on location, tissue type, and/or body system. The rate of incidental findings and the rate at which further follow-up was recommended by the radiologist were determined. RESULTS: A total of 617 patients was identified. There were 173 incidental findings noted in 146 of these patients (23.7%). Findings ranged from pulmonary (59%), skin/soft tissue (16%), thyroid (13%), vascular (9%), spinal (2%), and abdominal (1%) areas. Of the pulmonary findings, 50% were pulmonary nodules and 47% were granulomatous disease. Overall, the final radiology report recommended further follow-up for 50% of the patients with incidental findings. CONCLUSIONS: Incidental findings are relatively common in preoperative CTs obtained for shoulder arthroplasty, occurring in nearly one-quarter of patients. Most of these findings are pulmonary in nature. Overall, half of the patients with incidental findings were recommended for further follow-up. These results establish population data to guide orthopedic surgeons in patient counseling. Level of evidence: III.

Utility of Recovery Room vs Post-Operative Day 1 Radiographs Following Shoulder Arthroplasty.

Background: Postoperative radiographs may be performed on different timelines after shoulder arthroplasty. Radiographs obtained in the post-operative recovery unit (PACU) are often of poorer quality. The purpose of the current study was to explore and compare the quality of PACU radiographs and radiographs performed in the radiology suite on post-operative Day 1 (POD1), as well as determine their impact on changes in post-operative management. Methods: Our series included 50 consecutive anatomic total shoulder arthroplasties (TSA) for which post-operative radiographs were obtained in the PACU and 50 consecutive TSA for which post-operative radiographs were obtained in the radiology suite on POD 1. TSA radiographs were blinded and reviewed by 3 authors and graded on their quality using criteria described using previously published methods. The weighted kappa was used to describe the intra-rater agreement and inter-rater agreement between two raters. Results: There was no difference in age, sex, BMI, and comorbidities between cohorts. Intra-observer reliability was moderate to substantial with weighted kappa values of 0.65±0.07 (p<0.001), 0.58±0.09 (p<0.001), and 0.67±0.07 (p<0.001). Inter-observer reliability was moderate to substantial with weighted kappa values of 0.605±0.07 (p<0.001), 0.66±0.07 (p<0.001), and 0.65±0.08 (p<0.001). When assessing quality of radiographs, 30% of radiographs obtained in PACU were deemed quality while 57% of radiographs obtained in the radiology suite were deemed quality (p<0.001). Conclusion: Post-operative radiographs in the PACU do not alter patient management and are often inadequate to serve as baseline radiographs. Conversely, radiographs obtained in the radiology suite are of higher quality and can serve as a superior baseline radiograph. Level of Evidence: IV.