Examining the Potential Use of a Novel Radiographic Scoring System for Determining Surgical Intervention in Diabetic Charcot Arthropathy.

INTRODUCTION: Although Eichenholtz and the Schon systems are commonly used to evaluate foot Charcot arthropathy on radiographs, a novel system with expanded characterization may have added benefit. METHODS: Patients with Charcot arthropathy and foot radiographs were grouped in nonsurgical group 1 (imaging sets at minimum 2-year interval) and surgical group 2 (imaging preceding fusion and/or amputation). Radiographs were scored with Eichenholtz and Schon systems, and a novel scoring system (summation of 0-3 rank for bone density, distention/swelling, debris, disorganization, and dislocation/subluxation). Summative scores of the 2 groups were compared. Differences in scores of each system from serial images of group 1 were compared and average scores from each of the systems for preoperative imaging sets of group 2 were compared. RESULTS: A total of 111 patients were included (group 1, 19 patients; group 2, 92 patients). The novel system provided a broad numerical characterization of the radiographs (range 1-15). Summative scores of the novel system for groups 1 and 2 were statistically different with lower median score in the nonsurgical group (nonsurgical median score 6 vs surgical median score 9). Individual characteristic scores from 4 (distention, debris, disorganization, and dislocation) of 5 categories for the novel system were statistically different, with lower scores for the nonoperative group. The narrower numerical scores from the Eichenholtz and Schon systems did not yield statistically significant results. CONCLUSION: The novel scoring system provides a broad numerical description of radiographic findings in Charcot arthropathy of the foot and has potential advantage for surgical predictive value. LEVELS OF EVIDENCE: Level IV: Retrospective.

Prediction of biologically significant components from microarray data: Independently Consistent Expression Discriminator (ICED).

MOTIVATION: Class distinction is a supervised learning approach that has been successfully employed in the analysis of high-throughput gene expression data. Identification of a set of genes that predicts differential biological states allows for the development of basic and clinical scientific approaches to the diagnosis of disease. The Independent Consistent Expression Discriminator (ICED) was designed to provide a more biologically relevant search criterion during predictor selection by embracing the inherent variability of gene expression in any biological state. The four components of ICED include (i) normalization of raw data; (ii) assignment of weights to genes from both classes; (iii) counting of votes to determine optimal number of predictor genes for class distinction; (iv) calculation of prediction strengths for classification results. The search criteria employed by ICED is designed to identify not only genes that are consistently expressed at one level in one class and at a consistently different level in another class but identify genes that are variable in one class and consistent in another. The result is a novel approach to accurately select biologically relevant predictors of differential disease states from a small number of microarray samples. RESULTS: The data described herein utilized ICED to analyze the large AML/ALL training and test data set (Golub et al., 1999, Science, 286, 531-537) in addition to a smaller data set consisting of an animal model of the childhood neurodegenerative disorder, Batten disease, generated for this study. Both of the analyses presented herein have correctly predicted biologically relevant perturbations that can be used for disease classification, irrespective of sample size. Furthermore, the results have provided candidate proteins for future study in understanding the disease process and the identification of potential targets for therapeutic intervention.