Faculty Bibliography

BACKGROUND:Preoperative 3D planning and intraoperative navigation for shoulder arthroplasty has recently gained interest because of the potential to enhance the surgeon's understanding of glenoid anatomy and improve the accuracy of glenoid component positioning. The purpose of our study was to assess the impact of preoperative 3D planning on the surgeon's selection of the glenoid component (standard vs. augmented) and compare duration of surgery with and without intraoperative navigation. METHODS:We retrospectively analyzed 200 consecutive patients who underwent shoulder arthroplasty. The first group of 100 patients underwent shoulder arthroplasty using standard 2D preoperative planning based on standard radiographs and computed tomographic scans. The second group of 100 patients underwent shoulder arthroplasty using 3D preoperative planning and intraoperative navigation. Type of glenoid component and operative time were recorded in each case. RESULTS:For the group of patients with standard preoperative planning, only 15 augmented glenoid components were used, whereas in the group of patients with 3D preoperative planning and navigation, 54 augments were used (P < .001). The operative time was 11 minutes longer for the procedures that used intraoperative navigation, compared with those that did not (P < .001). This difference diminished as the surgeon became more proficient with the navigation technique. CONCLUSION/CONCLUSIONS:Use of preoperative 3D planning changes the surgeon's understanding of the patient's glenoid anatomy. In our study, using 3D planning increased the likelihood that the surgeon selected an augmented glenoid component compared with 2D planning. Intraoperative navigation slightly lengthened the duration of surgery, but this became insignificant as part of a learning curve within 6 months.

BACKGROUND:Preoperative planning software is widely available for most anatomic total shoulder arthroplasty (ATSA) systems. It can be most useful in determining implant selection and placement with advanced glenoid wear. The purpose of this study was to quantify inter- and intrasurgeon variability in preoperative planning of a series of ATSA cases. METHODS:Forty-nine computed tomography scans were planned for ATSA by 9 fellowship-trained shoulder surgeons using the ExactechGPS platform (Exactech Inc., Gainesville, FL, USA). Each case was planned a second time between 4 and 12 weeks later. Variability within and between surgeons was measured for implant type, size, version and inclination correction, and implant face position. Interclass correlation coefficients, Pearson, and Light's kappa coefficients were used for statistical analysis. RESULTS:There was considerable variation in the frequency of augment use between surgeons and between rounds for the same surgeon. Thresholds for augment use also varied between surgeons. Interclass correlation coefficients for intersurgeon variability were 0.37 for version, 0.80 for inclination, 0.36 for implant type, and 0.36 for implant size. Pearson coefficients for intrasurgeon variability were 0.17 for version and 0.53 for inclination. Light's kappa coefficient for implant type was 0.64. CONCLUSIONS:This study demonstrates substantial inter- and intrasurgeon variability in preoperative planning of ATSA. Although the magnitude of differences in correction was small, surgeons differed significantly in the use of augments to achieve the resultant plan. Surgeons differed from each other on thresholds for augment use and maximum allowable residual retroversion. This suggests that there may a range of acceptable corrections for each shoulder rather than a single optimal plan.

BACKGROUND:Uncorrected superior glenoid wear in patients managed with reverse total shoulder arthroplasty (rTSA) can result in increased complications, including baseplate failure. The present study quantifies the clinical and radiographic outcomes of patients with Favard type-E1, E2, and E3 glenoid deformity who were managed with rTSA with use of a superior or superior/posterior augmented glenoid baseplate. METHODS:We retrospectively reviewed the records for 68 patients with shoulder arthritis and Favard type-E1, E2, or E3 glenoid deformity who were managed with primary rTSA and a 10° superior augmented or 10° superior/8° posterior augmented baseplate. The mean duration of follow-up was 40 months (range, 24 to 85 months). Outcomes were assessed preoperatively and at the latest follow-up with shoulder range of motion and use of outcome scores including the Simple Shoulder Test (SST), University of California Los Angeles (UCLA) score, American Shoulder and Elbow Surgeons (ASES) score, Constant score, and Shoulder Pain and Disability Index (SPADI) score. Radiographs were evaluated preoperatively and at the time of the latest follow-up. Differences in preoperative and postoperative range of motion and outcome metrics were assessed with use of a 2-tailed Student t test. RESULTS:The majority of patients experienced clinically meaningful improvements in terms of pain and function following rTSA with a superior or superior/posterior augment, with 94% of patients rating themselves as "much better" (73.5%) or "better" (20.5%) at the time of the latest follow-up. At least 88% of the patients exceeded the minimum clinically important difference (MCID) threshold, and 75% of patients exceeded the substantial clinical benefit (SCB) threshold, for each of the clinical outcome metrics and range of motion. Five complications were reported (prevalence, 7.4%), including acromial stress fracture (2 patients), posttraumatic scapular neck fracture (1 patient), chronic shoulder pain (1 patient), and aseptic glenoid loosening (1 patient). CONCLUSIONS:The present short-term clinical and radiographic study demonstrated that shoulder arthropathy with superior glenoid wear patterns (Favard types E1, E2, and E3) can be successfully treated with rTSA with a superior or superior/posterior augmented baseplate. Longer-term clinical and radiographic follow-up is necessary to confirm that these promising short-term results are durable. LEVEL OF EVIDENCE/METHODS:Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.

This article was updated on TK because of a previous error, which was discovered after the preliminary version of the article was posted online. In Table VII, the fracture rate in the study by Walch et al. that had read "4.6% (21 of 457)" now reads "0.9% (4 of 457)."

Background/UNASSIGNED:No studies compare outcomes of anatomic total shoulder arthroplasty to reverse total shoulder arthroplasty with more than five-year follow-up. Methods/UNASSIGNED:A multicenter prospectively collected shoulder registry was utilized to review all patients undergoing primary anatomic total shoulder arthroplasty or primary reverse total shoulder arthroplasty with a minimum five-year follow-up utilizing a single platform stem implant system. One-hundred-ninety-one patients received an anatomic total shoulder arthroplasty and 139 patients received a reverse total shoulder arthroplasty. Patients were scored preoperatively and at latest follow-up using the simple shoulder test (SST), University of California Los Angeles (UCLA), American shoulder and elbow surgeons (ASES), Constant, and shoulder pain and disability index (SADI) scores as well as range of motion. Radiographs were evaluated for implant loosening or notching. Complications were reviewed. A Student's two-tailed, unpaired t-test identified differences in preoperative, postoperative, and pre-to-postoperative improvements. Results/UNASSIGNED:Reverse total shoulder arthroplasty patients were significantly older than anatomic total shoulder arthroplasty patients. All patients demonstrated significant improvement in functional metric scores and range of motion following anatomic total shoulder arthroplasty or reverse total shoulder arthroplasty. There was no difference in final outcome scores between anatomic total shoulder arthroplasty and reverse total shoulder arthroplasty patients at midterm follow-up; however, reverse total shoulder arthroplasty patients demonstrated significantly less motion. Discussion/UNASSIGNED:We demonstrate equivalent outcomes with five scoring metrics at mean follow-up of 71.3 ± 14.1 months. Although postoperative scores were significantly greater than preoperative scores for both anatomic total shoulder arthroplasty and reverse total shoulder arthroplasty patients, significant differences in outcome scores between cohorts were not observed.

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.

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.

STUDY DESIGN/METHODS:Retrospective analysis of outcomes in single-level spine and primary hip and knee arthroplasty patients. OBJECTIVE:Compare baseline and post-operative outcomes in patients undergoing spine surgery procedures with total hip arthroplasty (THA) and total knee arthroplasty (TKA) to further define outcomes in orthopedic surgery. SUMMARY OF BACKGROUND DATA/BACKGROUND:Computer-adaptive Patient Reported Outcome Information System (PROMIS) allows for standardized assessment of the Health Related Quality of Life across different disease states. METHODS:Patients who underwent spine surgery (anterior cervical discectomy and fusion, cervical disc replacement, lumbar laminectomy, microscopic lumbar discectomy, transforaminal lumbar interbody fusion or adult reconstruction surgery (THA, TKA) were grouped. Mean Charlson Comorbidity Index (CCI), Baseline (BL) and 6-month (6 M) PROMIS scores of Physical Function, Pain Interference, and Pain Intensity were determined. Paired t-tests compared differences in CCI, BL, 6 M, and change in PROMIS scores for spine and adult reconstruction procedures. RESULTS:304 spine surgery patients (Age=58.1 ± 15.6; 42.9% Female) and 347 adult reconstruction patients (Age=62.9 ± 11.8; 54.1% Female) were compared. Spine surgery groups had more disability and pain at baseline than adult reconstruction patients according to Physical Function [(21.0, 22.2, 9.07, 12.6, 10.4) vs (35.8, 35.0), respectively, p < .01], Pain Interference [(80.1, 74.1, 89.6, 92.5, 90.6) vs (64.0, 63.9), respectively, p < .01] and Pain Intensity [(53.0, 53.1, 58.3, 58.5, 56.1) vs (53.4, 53.8), respectively, p < .01]. At 6 M, spine surgery patients remained more disabled and had more pain compared to adult reconstruction patients. Over the 6-month timespan, spine patients experienced greater improvements than adult reconstruction patients in terms of Physical Function [(+8.7, +22.2, +9.7, +12.9, +12.1) vs (+5.3, +3.9), respectively, p < .01] and Pain Interference scores [(-15.4, -28.1, -14.7, -13.1, -12.3) vs (-8.3, -6.0), respectively, p < .01]. CONCLUSIONS:Spinal surgery patients had lower BL and 6 M PROMIS scores, but greater relative improvement in PROMIS scores compared to adult reconstruction patients. LEVEL OF EVIDENCE/METHODS:3.

OBJECTIVES:(1) To demonstrate how a risk assessment tool modified to account for the COVID-19 virus during the current global pandemic is able to provide risk assessment for low-energy geriatric hip fracture patients. (2) To provide a treatment algorithm for care of COVID-19 positive/suspected hip fractures patients that accounts for their increased risk of morbidity and mortality. SETTING:One academic medical center including 4 Level 1 trauma centers, 1 university-based tertiary care referral hospital, and 1 orthopaedic specialty hospital. PATIENTS/PARTICIPANTS:One thousand two hundred seventy-eight patients treated for hip fractures between October 2014 and April 2020, including 136 patients treated during the COVID-19 pandemic between February 1, 2020 and April 15, 2020. INTERVENTION:The Score for Trauma Triage in the Geriatric and Middle-Aged ORIGINAL (STTGMAORIGINAL) score was modified by adding COVID-19 virus as a risk factor for mortality to create the STTGMACOVID score. Patients were stratified into quartiles to demonstrate differences in risk distribution between the scores. MAIN OUTCOME MEASUREMENTS:Inpatient and 30-day mortality, major, and minor complications. RESULTS:Both STTGMA score and COVID-19 positive/suspected status are independent predictors of inpatient mortality, confirming their use in risk assessment models for geriatric hip fracture patients. Compared with STTGMAORIGINAL, where COVID-19 patients are haphazardly distributed among the risk groups and COVID-19 inpatient and 30 days mortalities comprise 50% deaths in the minimal-risk and low-risk cohorts, the STTGMACOVID tool is able to triage 100% of COVID-19 patients and 100% of COVID-19 inpatient and 30 days mortalities into the highest risk quartile, where it was demonstrated that these patients have a 55% rate of pneumonia, a 35% rate of acute respiratory distress syndrome, a 22% rate of inpatient mortality, and a 35% rate of 30 days mortality. COVID-19 patients who are symptomatic on presentation to the emergency department and undergo surgical fixation have a 30% inpatient mortality rate compared with 12.5% for patients who are initially asymptomatic but later develop symptoms. CONCLUSION:The STTGMA tool can be modified for specific disease processes, in this case to account for the COVID-19 virus and provide a robust risk stratification tool that accounts for a heretofore unknown risk factor. COVID-19 positive/suspected status portends a poor outcome in this susceptible trauma population and should be included in risk assessment models. These patients should be considered a high risk for perioperative morbidity and mortality. Patients with COVID-19 symptoms on presentation should have surgery deferred until symptoms improve or resolve and should be reassessed for surgical treatment versus definitive nonoperative treatment with palliative care and/or hospice care. LEVEL OF EVIDENCE:Prognostic Level III. See Instructions for Authors for a complete description of Levels of Evidence.