Faculty Bibliography

Hypothesis: The use of intraoperative glenohumeral load sensors has the potential to facilitate an objective, quantitative assessment of the soft tissue tension during reverse total shoulder arthroplasty. Material and Methods: A reverse total shoulder arthroplasty was performed on eight fresh frozen cadaveric shoulders, creating three different tightness conditions for each shoulder by using various shim thicknesses attached to an instrumented, load-sensing humeral trial component. The glenohumeral loads were recorded during four dynamic maneuvers, consisting of maximum internal/external rotation at 0-, 45- and 90-degree abduction and forward elevation. The joint kinematics were synchronously recorded using an optical tracking system. Results: For normal tightness conditions, 98.3% of the observed loads were below 40 lbf. These loads varied through the range of motion with an increase in glenohumeral loads generally observed towards the limits of the range of motion. With increasing shoulder tightness, the range of motion of the joint was not significantly affected, though the average and maximum glenohumeral load significantly increased (p < 0.01 for all). Conclusion: In a cadaveric setting, higher glenohumeral loads were observed at higher tightness conditions, demonstrating the potential of a load-sensing humeral trial component to quantify intraoperative joint load with reverse total shoulder arthroplasty. The glenohumeral loads are increasing towards the limits of the range of motion, indicating the importance of performing dynamic assessment of stability at the extents of the range of motion during implant trialing. Level of Evidence: Basic science study

BACKGROUND:We sought to identify the influence of shoulder arthroplasty (SA) type (primary anatomic, primary reverse, revision) and indication (fracture, arthritis) on the risk of postoperative packed red blood cell transfusion (pRBCT) and intravenous fluid bolus (IVFB). METHODS:Patients who underwent SA from 2013 through 2016 at our institution were categorized into four groups: primary anatomic (pTSA; N = 298), primary reverse (pRTSA; N = 292), revision (RevSA; N = 133), and shoulder arthroplasty for fracture (SAF; N = 58). Basic demographics, intraoperative metrics, preoperative lab values, and postoperative interventions (pRBCT and IVFB) were retrieved from electronic records. RESULTS:There were 781 SAs, of which 176 cases involved at least one postoperative intervention: pRBCT (6.3%) or IVFB (18.8%). Compared to pTSA, the odds of pRBCT in pRTSA, revSA, and SAF were: 3.0 (95% CI 0.9-10.0), 3.4 (95% CI 0.9-2.7), and 8.6 (95% CI 2.2-32.9), respectively. Independent risk factors for pRBCT included increasing age (p = 0.003), underweight body mass index (p = 0.019), Charlson Comorbidity Index ≥ 3 (p = 0.002), inpatient discharge to higher level of care (p = 0.011), estimated blood loss (p = 0.003), and preoperative hemoglobin (p ≤ 0.001). Also, compared to pTSA, the odds for IVFB in pRTSA, revSA, and SAF were: 2.8 (95% CI 1.7-4.6), 2.2 (95% CI 1.2-4.0), and 4.7 (95% CI 2.2-9.9), respectively. Other independent risk factors for IVFB included female sex (0.002), CCI ≥ 3 (p = 0.017), and operative time (p = 0.047). CONCLUSION/CONCLUSIONS:Shoulder arthroplasty for fracture is an independent risk factor for higher risk of postoperative pRBCT and IVFB.

OBJECTIVES/OBJECTIVE:To examine one health system's response to the essential care of its hip fracture population during the COVID-19 pandemic and report on its effect on patient outcomes. DESIGN/METHODS:Prospective cohort study SETTING:: Seven musculoskeletal care centers with New York City and Long Island. PATIENTS/PARTICIPANTS/METHODS:138 recent and 115 historical hip fracture patients. INTERVENTION/METHODS:Patients with hip fractures occurring between February 1, 2020 and April 15, 2020 or between February 1, 2019 and April 15, 2019 were prospectively enrolled in an orthopedic trauma registry and chart reviewed for demographic and hospital quality measures. Patients with recent hip fractures were identified as COVID positive (C+), COVID suspected (Cs) or COVID negative (C-). MAIN OUTCOME MEASUREMENTS/METHODS:Hospital quality measures, inpatient complications and mortality rates. RESULTS:Seventeen (12.2%) patients were confirmed C+ by testing and another 14 (10.1%) were suspected (Cs) of having had the virus but were never tested. The C+ cohort, when compared to Cs and C- cohorts, had: an increased mortality rate (35.3% vs 7.1% vs 0.9%), increased length of hospital stay, a greater major complication rate and a greater incidence of ventilator need postoperatively. CONCLUSIONS:COVID-19 had a devastating effect on the care of hip fracture patients during the pandemic. Although practice patterns generally remained unchanged, treating physicians need to understand the increased morbidity and mortality in hip fracture patients complicated by COVID-19. LEVEL OF EVIDENCE/METHODS:Prognostic Level III. See Instructions for Authors for a complete description of Levels of Evidence.

BACKGROUND:Preoperative planning software is gaining utility in reverse total shoulder arthroplasty (RTSA), particularly when addressing pathologic glenoid wear. The purpose of this study was to quantify inter- and intrasurgeon variability in preoperative planning a series of RTSA cases to identify differences in how surgeons consider optimal implant placement. This may help identify opportunities to establish consensus when correlating plan differences with clinical data. METHODS:A total of 49 computed tomography scans from actual RTSA cases were planned for RTSA by 9 fellowship-trained shoulder surgeons using the same platform (Exactech GPS, Exactech Inc., Gainesville, FL, USA). Each case was planned a second time 6-12 weeks later. Variability within and between surgeons was measured for implant selection, version correction, inclination correction, and implant face position. Interclass correlation coefficients, and Pearson and Light's kappa coefficient were used for statistical analysis. RESULTS:There was considerable variation in the frequency of augmented baseplate selection between surgeons and between rounds for the same surgeon. Thresholds for augment use also varied between surgeons. Interclass correlation coefficients for intersurgeon variability ranged from 0.43 for version, 0.42 for inclination, and 0.25 for baseplate type. Pearson coefficients for intrasurgeon variability were 0.34 for version and 0.30 for inclination. Light's kappa coefficient for baseplate type was 0.61. CONCLUSIONS:This study demonstrates substantial variability both between surgeons and between rounds for individual surgeons when planning RTSA. Although average differences between plans were relatively small, there were large differences in specific cases suggesting little consensus on optimal planning parameters and opportunities to establish guidelines based on glenoid pathoanatomy. The correlation of preoperative planning with clinical outcomes will help to establish such guidelines.

As the world grapples with the COVID-19 pandemic, we as health care professionals thrive to continue to help our patients, and as orthopedic surgeons, this goal is ever more challenging. As part of a major academic tertiary medical center in New York City, the orthopedic department at New York University (NYU) Langone Health has evolved and adapted to meet the challenges of the COVID pandemic. In our report, we will detail the different aspects and actions taken by NYU Langone Health as well as NYU Langone Orthopedic Hospital and the orthopedic department in particular. Among the steps taken, the department has reconfigured its staff's assignments to help both with the institution's efforts and our patients' needs from reassigning operating room nurses to medical COVID floors to having attending surgeons cover urgent care locations. We have reorganized our residency and fellowship rotations and assignments as well as adapting our educational programs to online learning. While constantly evolving to meet the institution's and our patient demands, our leadership starts planning for the return to a new "normal".

BACKGROUND:Machine learning techniques can identify complex relationships in large healthcare datasets and build prediction models that better inform physicians in ways that can assist in patient treatment decision-making. In the domain of shoulder arthroplasty, machine learning appears to have the potential to anticipate patients' results after surgery, but this has not been well explored. QUESTIONS/PURPOSES/OBJECTIVE:(1) What is the accuracy of machine learning to predict the American Shoulder and Elbow Surgery (ASES), University of California Los Angeles (UCLA), Constant, global shoulder function, and VAS pain scores, as well as active abduction, forward flexion, and external rotation at 1 year, 2 to 3 years, 3 to 5 years, and more than 5 years after anatomic total shoulder arthroplasty (aTSA) or reverse total shoulder arthroplasty (rTSA)? (2) What is the accuracy of machine learning to identify whether a patient will achieve clinical improvement that exceeds the minimal clinically important difference (MCID) threshold for each outcome measure? (3) What is the accuracy of machine learning to identify whether a patient will achieve clinical improvement that exceeds the substantial clinical benefit threshold for each outcome measure? METHODS:A machine learning analysis was conducted on a database of 7811 patients undergoing shoulder arthroplasty of one prosthesis design to create predictive models for multiple clinical outcome measures. Excluding patients with revisions, fracture indications, and hemiarthroplasty resulted in 6210 eligible primary aTSA and rTSA patients, of whom 4782 patients with 11,198 postoperative follow-up visits had sufficient preoperative, intraoperative, and postoperative data to train and test the predictive models. Preoperative clinical data from 1895 primary aTSA patients and 2887 primary rTSA patients were analyzed using three commercially available supervised machine learning techniques: linear regression, XGBoost, and Wide and Deep, to train and test predictive models for the ASES, UCLA, Constant, global shoulder function, and VAS pain scores, as well as active abduction, forward flexion, and external rotation. Our primary study goal was to quantify the accuracy of three machine learning techniques to predict each outcome measure at multiple postoperative timepoints after aTSA and rTSA using the mean absolute error between the actual and predicted values. Our secondary study goals were to identify whether a patient would experience clinical improvement greater than the MCID and substantial clinical benefit anchor-based thresholds of patient satisfaction for each outcome measure as quantified by the model classification parameters of precision, recall, accuracy, and area under the receiver operating curve. RESULTS:Each machine learning technique demonstrated similar accuracy to predict each outcome measure at each postoperative point for both aTSA and rTSA, though small differences in prediction accuracy were observed between techniques. Across all postsurgical timepoints, the Wide and Deep technique was associated with the smallest mean absolute error and predicted the postoperative ASES score to ± 10.1 to 11.3 points, the UCLA score to ± 2.5 to 3.4, the Constant score to ± 7.3 to 7.9, the global shoulder function score to ± 1.0 to 1.4, the VAS pain score to ± 1.2 to 1.4, active abduction to ± 18 to 21°, forward elevation to ± 15 to 17°, and external rotation to ± 10 to 12°. These models also accurately identified the patients who did and did not achieve clinical improvement that exceeded the MCID (93% to 99% accuracy for patient-reported outcome measures (PROMs) and 85% to 94% for pain, function, and ROM measures) and substantial clinical benefit (82% to 93% accuracy for PROMs and 78% to 90% for pain, function, and ROM measures) thresholds. CONCLUSIONS:Machine learning techniques can use preoperative data to accurately predict clinical outcomes at multiple postoperative points after shoulder arthroplasty and accurately risk-stratify patients by preoperatively identifying who may and who may not achieve MCID and substantial clinical benefit improvement thresholds for each outcome measure. CLINICAL RELEVANCE/CONCLUSIONS:Three different commercially available machine learning techniques were used to train and test models that predicted clinical outcomes after aTSA and rTSA; this device-type comparison was performed to demonstrate how predictive modeling techniques can be used in the near future to help answer unsolved clinical questions and augment decision-making to improve outcomes after shoulder arthroplasty.

Introduction: The prevalence of reverse total shoulder arthroplasty (RTSA) has surpassed anatomic shoulder arthroplasty and is often used in complex cases of severe glenoid erosion including cases with an intact rotator cuff where there may be concerns about addressing glenoid wear with anatomic implants. CT-based preoperative planning software is now available for most commonly-used implants allowing surgeons to virtually determine optimal implant choice and placement in advance of surgery. While there are generally agreed upon ranges of normal glenoid anatomy for version and inclination, standards for an optimal RTSA have yet to be established. This study seeks to quantify surgeon differences in baseplate selection and placement for a series of cases with a range of pathologic glenoids.
Method(s): CT scans from 50 cases submitted for actual preoperative planning were collected and planned for RTSA on 2 separate occasions by 9 fellowship trained shoulder arthroplasty surgeons. One cases was excluded due to extreme glenoid wear leaving 49 cases for a total of 882 preoperative plans. All cases were planned using the same implant system (Equinoxe, Exactech Inc., Gainesville, FL) which offers 4 different baseplates: standard, an 8degree posterior augment, a 10degree superior augment, and a combined posterior/superior augment. Cases ranged from 0.9degree anteversion to -25degree retroversion (average -11.7degree) and from 14.6degree inferior to 15.6degree superior inclination (average 2.4degree superior) as measured by the software relative to the Friedman axis. Surgeons were compared for differences in baseplate selection, version and inclination correction and thresholds for augment use.
Result(s): Standard baseplates were selected in 21% of cases on average (Range: 5-68%). Average retroversion and inclination for use of a standard baseplate were -5.7degree (Range: -2.9 to -8.9degree) and 2.0degree superior (Range: 0.1degree inferior to 4.6degree superior). 8degree posterior augments were used in an average of 32% (Range: 22-67%). Average retroversion and inclination for this baseplate were -14.3degree (Range: -11.3 to -15.5degree) and 1.9degree inferior (Range: 5degree inferior to 2degree superior). 10degree superior augments were used in 16% of cases on average (Range: 3-32%). Average retroversion and inclination for this baseplate were -9.1degree (Range: -5 to -13.6degree) and 7.6degree superior (Range: 0.6 to 8.9degree superior). Posterior superior augments were used in 27% of cases on average (Range: 2-67%). Average retroversion and inclination for this baseplate were -15.9degree (Range: -11 to -21.6degree) and 3.4degree superior (Range: 1degree inferior to 4.8degree superior). Overall surgeons corrected cases to an average retroversion of -1.9degree (Range: 0 to -4.1degree). Maximum residual retroversion averaged -8.7degree (Range: 0 to -16degree). Overall inclination correction was to 0.4degree superior (Range: 0.2degree inferior to 1.8degree superior). Surgeons also differed on baseplate depth up to 1.8mm. [Formula presented] Discussion: This study indicates that surgeons vary substantially in their choice of baseplate and their threshold for augment use for given degrees of version and inclination. Overall use of augmented baseplates ranged from 32-95%. Interestingly, the average version and inclination correction differences for the two surgeons that differed the most in augment use was only 0.7degree and 0.1degree respectively. This may indicate that surgeons can achieve relatively similar goals for implant placement using different strategies of implant choice and position. Future studies should focus on strategies to minimize bone loss when correcting pathologic glenoid morphology as well as determine if certain baseplate position and orientation parameters are biomechanically favorable in terms of fixation especially in complex cases with severe wear patterns.
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Background: Treatment of proximal humerus fractures (PHFs) via reverse total shoulder arthroplasty (RTSA) has shown early promise when compared to historical treatment modalities. Ideal surgical timing remains unclear. The purpose of this study was to compare the outcomes of early versus delayed RTSA for PHF. We hypothesized that acute RTSA would display superior outcomes compared to those receiving delayed surgical intervention.
Method(s): This multicenter study retrospectively analyzed 142 patients who underwent RTSA for fracture. Patients treated within 4 weeks of injury were placed in the acute group (n=102), and patients treated longer than 4 weeks after injury were placed in the chronic group (n=38). A comprehensive panel of patient reported outcome measures, VAS pain scores, range of motion, and patient satisfaction were evaluated.
Result(s): The acute group had significantly better final follow-up SPADI scores (20.8 +/- 23.9 vs. 30.7 +/- 31.7) (p<0.05). The acute group demonstrated higher passive external rotation compared to the chronic group (47.8 +/- 16.5 vs. 40.4 +/- 16.1) (p<0.05). No further differences were detected in other postoperative range of motion measurements, subjective outcomes, or VAS scores. The acute group displayed significantly greater overall improvements (pre vs post) in all range of motion measurements as well as patient-reported outcome and VAS scores. Average follow-up was 51.4 months.
Conclusion(s): Our results suggest that patients treated acutely display similar mid-term outcomes to those who receive delayed treatment. Surgeons may first give consideration to a period of nonoperative treatment.
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The glenoid is considered a weak link in total shoulder arthroplasty because failure on the glenoid side is one of the most common reasons for revision of total shoulder arthroplasty. Glenoid wear is commonly seen in glenohumeral arthritis and compromises glenoid bone stock and also alters the native version and inclination of the glenoid. It is critical to recognize glenoid wear and correct it intraoperatively to avoid component malposition, which can negatively affect the survivorship of the glenoid implant. The end point of correction for the glenoid wear in shoulder arthroplasty is controversial, but anatomic glenoid component positioning is likely to improve long-term survivorship of the total shoulder arthroplasty. Preoperative three-dimensional (3-D) computer planning software, based on CT, is commercially available. It allows the surgeon to plan implant type (anatomic versus reverse), size, and position on the glenoid, and also allows for templating deformity correction using bone graft and/or augments. Guidance technology in the form of computer-assisted surgery (CAS) and patient-specific instrumentation (PSI) allows the surgeon to execute the preoperative plan during surgery with a greater degree of accuracy and precision and has shown superiority to standard instrumentation. However, the proposed benefits of this technology including improved glenoid survivorship, reduced revision arthroplasty rate and cost-effectiveness have not yet been demonstrated clinically. In this review, we present the current evidence regarding PSI and CAS in managing glenoid deformity in total shoulder arthroplasty.