The increased prevalence of osteoporosis in the patient population undergoing reverse shoulder arthroplasty (RSA) results in significantly increased complication rates. Mainly demographic and clinical predictors are currently taken into the preoperative assessment for risk stratification without quantification of preoperative computed tomography (CT) data (e.g. bone density). It was hypothesized that preoperative CT bone density measures would provide objective quantification with subsequent classification of the patients’ humeral bone quality. Thirteen bone density parameters from 345 preoperative CT scans of a clinical RSA cohort represented the data set in this study. The data set was divided into testing (30%) and training data (70%), latter included an 8-fold cross validation. Variable selection was performed by choosing the variables with the highest descriptive value for each correlation clustered variables. Machine learning models were used to improve the clustering (Hierarchical Ward) and classification (Support Vector Machine (SVM)) of bone densities at risk for complications and were compared to a conventional statistical model (Logistic Regression (LR)).Introduction
Methods
The inherently high stiffness of locked plate constructs is increasingly recognized as a potential cause of deficient healing observed in patients with periarticular locked plating systems. The objective of this study is to perform a biomechanical comparison of distal femur locked plating systems. Biomechanical study using bone substitutes in a distal femur fracture model (OTA/AO 33-A3). Four different locked plate fixation systems were compared (AxSOS, LCP, PERI-LOC, POLYAX). Eight bone implant constructs of each plating system were evaluated in a multiple testing model to examine static failure, stiffness under static and cyclic loading and cyclic fatigue.Background
Methods
