Targeted Muscle Reinnervation for Transradial Amputation: Description of Operative Technique.

Targeted muscle reinnervation (TMR) is a revolutionary surgical technique that, together with advances in upper extremity prostheses and advanced neuromuscular pattern recognition, allows intuitive and coordinated control in multiple planes of motion for shoulder disarticulation and transhumeral amputees. TMR also may provide improvement in neuroma-related pain and may represent an opportunity for sensory reinnervation as advances in prostheses and haptic feedback progress. Although most commonly utilized following shoulder disarticulation and transhumeral amputations, TMR techniques also represent an exciting opportunity for improvement in integrated prosthesis control and neuroma-related pain improvement in patients with transradial amputations. As there are no detailed descriptions of this technique in the literature to date, we provide our surgical technique for TMR in transradial amputations.

Development of a prognostic naive bayesian classifier for successful treatment of nonunions.

BACKGROUND: predictive models permitting individualized prognostication for patients with fracture nonunion are lacking. The objective of this study was to train, test, and cross-validate a Bayesian classifier for predicting fracture-nonunion healing in a population treated with extracorporeal shock wave therapy. METHODS: prospectively collected data from 349 patients with delayed fracture union or a nonunion were utilized to develop a naïve Bayesian belief network model to estimate site-specific fracture-nonunion healing in patients treated with extracorporeal shock wave therapy. Receiver operating characteristic curve analysis and tenfold cross-validation of the model were used to determine the clinical utility of the approach. RESULTS: predictors of fracture-healing at six months following shock wave treatment were the time between the fracture and the first shock wave treatment, the time between the fracture and the surgery, intramedullary stabilization, the number of bone-grafting procedures, the number of extracorporeal shock wave therapy treatments, work-related injury, and the bone involved (p < 0.05 for all comparisons). These variables were all included in the naïve Bayesian belief network model. CONCLUSIONS: a clinically relevant Bayesian classifier was developed to predict the outcome after extracorporeal shock wave therapy for fracture nonunions. The time to treatment and the anatomic site of the fracture nonunion significantly impacted healing outcomes. Although this study population was restricted to patients treated with shock wave therapy, Bayesian-derived predictive models may be developed for application to other fracture populations at risk for nonunion. LEVEL OF EVIDENCE: prognostic Level II. See Instructions to Authors for a complete description of levels of evidence.