In silico prediction of hip fractures: improved fall modeling and expanded validation across cohorts with diverse risk profiles.

Osteoporosis constitutes a significant global health concern, however the development of novel treatments is challenging due to the limited cost-effectiveness and ethical concerns inherent to placebo-controlled clinical trials. Computational approaches are emerging as alternatives for the development and assessment of biomedical interventions. The aim of this study was to evaluate the ability of an In Silico trial technology (BoneStrength) to predict hip fracture incidence by implementing a novel approach designed to reproduce the phenomenology of falls as reported in clinical data, and by testing its accuracy in three virtual cohorts characterised by different risk profiles. Three cohorts of 1270, 1249 and 1262 virtual patients (Finite Element models of proximal femur) were generated based on a statistical anatomy atlas. Fall events were modelled using a negative binomial distribution, which replicated the over-dispersed nature of falls among the elderly population. A multiscale stochastic model was employed to estimate the impact force for each fall event, and subject-specific FE models were used to determine fall-specific femur strength. Patients were classified as fractured if the impact force exceeded femur strength. Fracture incidence over a two- or three-years follow-up was predicted with a Markov chain approach. The model predicted 12 ± 4, 16 ± 3 and 37 ± 7 fractures for the three cohorts, in alignment with clinical data (8, 14 and 41 fractures reported respectively). In conclusion, BoneStrength could reproduce fall phenomenology and fracture incidence in diverse populations. These results highlight its potential for future applications in the development of hip fracture prevention strategies.