Acromial and scapular fractures are a rare but difficult complication with reverse total shoulder arthroplasty (rTSA), with an incidence rate reported from 1–10%. The risk factors associated with these fractures types is largely unknown. The goal of this study is to analyze the clinical outcomes, demographic and comorbidity data, and implant sizing and surgical technique information from 4125 patients who received a primary rTSA with one specific prosthesis (Equinoxe, Exactech, Inc) and were sorted based on the radiographic documentation of an acromial and/or scapula fracture (ASF) to identify factors associated with this complication. 4125 patients (2652F/1441M/32 unspecified; mean age: 72.5yrs) were treated with primary rTSA by 23 orthopaedic surgeons. Revision and fracture reverse arthroplasty cases were excluded. The radiographic presence of each fracture was documented and classified using the Levy classification method. 61 patients were identified as having ASF, 10 patients had fractures of the Type 1, 32 patients had Type 2, and 18 patients had Type 3 fractures according to Levy's classification. One fracture was not classifiable. Pre-op and post-op outcome scoring, ROM as well as demographic, comorbidity, implant, and surgical technique information were evaluated for these 61 patients and compared to the larger cohort of patients to identify any associations. A two-tailed, unpaired t-test identified differences (p<0.05).Introduction
Methods
Machine learning is a relatively novel method to orthopaedics which can be used to evaluate complex associations and patterns in outcomes and healthcare data. The purpose of this study is to utilize 3 different supervised machine learning algorithms to evaluate outcomes from a multi-center international database of a single shoulder prosthesis to evaluate the accuracy of each model to predict post-operative outcomes of both aTSA and rTSA. Data from a multi-center international database consisting of 6485 patients who received primary total shoulder arthroplasty using a single shoulder prosthesis (Equinoxe, Exactech, Inc) were analyzed from 19,796 patient visits in this study. Specifically, demographic, comorbidity, implant type and implant size, surgical technique, pre-operative PROMs and ROM measures, post-operative PROMs and ROM measures, pre-operative and post-operative radiographic data, and also adverse event and complication data were obtained for 2367 primary aTSA patients from 8042 visits at an average follow-up of 22 months and 4118 primary rTSA from 11,754 visits at an average follow-up of 16 months were analyzed to create a predictive model using 3 different supervised machine learning techniques: 1) linear regression, 2) random forest, and 3) XGBoost. Each of these 3 different machine learning techniques evaluated the pre-operative parameters and created a predictive model which targeted the post-operative composite score, which was a 100 point score consisting of 50% post-operative composite outcome score (calculated from 33.3% ASES + 33.3% UCLA + 33.3% Constant) and 50% post-operative composite ROM score (calculated from S curves weighted by 70% active forward flexion + 15% internal rotation score + 15% active external rotation). 3 additional predictive models were created to control for the time required for patient improvement after surgery, to do this, each primary aTSA and primary rTSA cohort was subdivided to only include patient data follow-up visits >20 months after surgery, this yielded 1317 primary aTSA patients from 2962 visits at an average follow-up of 50 months and 1593 primary rTSA from 3144 visits at an average follow-up of 42 months. Each of these 6 predictive models were trained using a random selection of 80% of each cohort, then each model predicted the outcomes of the remaining 20% of the data based upon the demographic, comorbidity, implant type and implant size, surgical technique, pre-operative PROMs and ROM measures inputs of each 20% cohort. The error of all 6 predictive models was calculated from the root mean square error (RMSE) between the actual and predicted post-op composite score. The accuracy of each model was determined by subtracting the percent difference of each RMSE value from the average composite score associated with each cohort.Introduction
Methods
A better understanding of the rate of improvement associated with aTSA and rTSA is critical to establish accurate patient expectations for treatment to reduce pain and restore function; more realistic patient expectations pre-operatively may lead to greater patient satisfaction post-operatively. To this end, this study quantifies the rate of improvement in outcomes of aTSA and rTSA using 5 different scoring metrics for 1641 patients with one platform shoulder arthroplasty system. 1641 patients (mean age: 69.3yrs) were treated by 14 orthopaedic surgeons using one platform shoulder system (Exactech, Inc). 729 patients received aTSA (65.3yrs; 384F/345M) for treatment of degenerative arthritis and 912 patients received rTSA (72.5yrs; 593F/319M) for treatment of CTA/RCT/OA. Each patient was scored pre-operatively and at various follow-up intervals (3 months, 6months, annually, etc) using the SST, UCLA, ASES, Constant, and SPADI metrics; active abduction, active forward flexion, and active/passive external rotation were also measured. 4439 total follow-up reports were analyzed (1851 and 2588 rTSA). Improvements in outcome using each metric score were calculated and normalized on a 100 point scale. The rate of improvement was analyzed using a 40 point moving filter treadline and with a 3rd order polynomial treadline over the entire range of follow-up.Introduction
Methods
