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

IF 3.5 2区医学 Q2 ENGINEERING, BIOMEDICAL

Journal of the Mechanical Behavior of Biomedical Materials Pub Date : 2025-08-30 DOI:10.1016/j.jmbbm.2025.107182

Giacomo Savelli , Sara Oliviero , Marco Viceconti , Antonino Amedeo La Mattina

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引用次数: 0

Abstract

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.

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髋部骨折的计算机预测：改进跌倒模型，并在具有不同风险概况的队列中扩大验证

骨质疏松症是一个重要的全球健康问题，然而，由于有限的成本效益和安慰剂对照临床试验固有的伦理问题，开发新的治疗方法具有挑战性。计算方法正在成为开发和评估生物医学干预措施的替代方法。本研究的目的是通过实施一种旨在重现临床数据中报告的跌倒现象的新方法，并通过在三个具有不同风险特征的虚拟队列中测试其准确性，来评估一种计算机试验技术（bonstrength）预测髋部骨折发生率的能力。基于统计解剖图谱生成虚拟患者1270、1249和1262三个队列（股骨近端有限元模型）。跌倒事件采用负二项分布建模，这复制了老年人跌倒的过度分散性质。采用多尺度随机模型来估计每次跌倒事件的冲击力，并使用受试者特定的有限元模型来确定跌倒特定的股骨强度。如果撞击力超过股骨强度，则将患者归类为骨折。用马尔可夫链方法预测2 - 3年随访期间的骨折发生率。该模型预测3组患者发生骨折分别为12±4、16±3和37±7处，与临床数据相符（分别为8、14和41处）。总之，骨强度可以再现不同人群的跌倒现象和骨折发生率。这些结果突出了其在未来髋骨骨折预防策略开发中的应用潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the Mechanical Behavior of Biomedical Materials 工程技术-材料科学：生物材料

CiteScore

7.20

自引率

7.70%

发文量

505

审稿时长

46 days

期刊介绍： The Journal of the Mechanical Behavior of Biomedical Materials is concerned with the mechanical deformation, damage and failure under applied forces, of biological material (at the tissue, cellular and molecular levels) and of biomaterials, i.e. those materials which are designed to mimic or replace biological materials. The primary focus of the journal is the synthesis of materials science, biology, and medical and dental science. Reports of fundamental scientific investigations are welcome, as are articles concerned with the practical application of materials in medical devices. Both experimental and theoretical work is of interest; theoretical papers will normally include comparison of predictions with experimental data, though we recognize that this may not always be appropriate. The journal also publishes technical notes concerned with emerging experimental or theoretical techniques, letters to the editor and, by invitation, review articles and papers describing existing techniques for the benefit of an interdisciplinary readership.