The Role of Trabecular Bone Score and Hip Geometry in Thalassemia Major: A Neural Network Analysis

Osteopathy in thalassemia is a very heterogeneous condition; severity depends on multiple factors, interacting through nonlinear mechanisms. Classic statistics have limitations when applied to the study of such highly complex relationships. Currently, an alternative approach of analysis is represented by the artificial neural networks (ANNs), powerful mathematical tools, increasingly applied to analyze multifactorial databases, as considered more appropriate than classic statistics. We adopted this specialized mathematical method to 76 thalassemia major (TM) patients. In all of them dual energy X-ray absorptiometry (DXA) was performed to measure bone mineral density, and two recent developments were included: trabecular bone score, evaluating bone microarchitecture, and hip structural analysis, evaluating hip geometry. The relationships between bone status and endocrine, hematologic, and clinical parameters were investigated. Using a particular ANN (Auto Contractive Map algorithm), the strength of inter-variable association was defined and a connectivity map generated, visually representing the main connections among the entered variables. Iron status indices (ferritin, liver iron concentration) emerged as the most important variables, dividing the map into two sectors, with parameters indicating satisfactory bone condition in the upper, those indicating poor condition in the lower, near the variable “fractures”. The Auto Contractive Map highlighted the key role of bone quantity, bone geometry, and microarchitecture in defining thalassemic bone condition. Among numerous available indices, high femoral bone mineral density and low cross-sectional moment of inertia emerged as the gold standard to classify thalassemic patients for prognostic and therapeutic purposes.