In Silico clinical trial to predict the efficacy of hip protectors for preventing hip fractures.

Hip fracture Hip protector In Silico medicine In Silico trial Osteoporosis

Journal

Journal of biomechanics
ISSN: 1873-2380
Titre abrégé: J Biomech
Pays: United States
ID NLM: 0157375

Informations de publication

Date de publication:
18 Sep 2024
Historique:
received: 10 10 2023
revised: 13 09 2024
accepted: 17 09 2024
medline: 22 9 2024
pubmed: 22 9 2024
entrez: 21 9 2024
Statut: aheadofprint

Résumé

Osteoporosis is characterized by loss of bone mineral density and increased fracture risk. Reduction of hip fracture incidence is of major clinical importance. Hip protectors aim to attenuate the impact force transmitted to the femur upon falling, however different conclusions on their efficacy have been reported; some authors suggest this may be due to differences in compliance. The aim of this study was to apply an In Silico trial methodology to predict the effectiveness of hip protectors and its dependence on compliance. A cohort of 1044 virtual patients (Finite Element models of proximal femur) were generated. A Markov chain process was implemented to predict fracture incidence with and without hip protectors, by simulating different levels of compliance. At each simulated follow-up year, a Poisson distribution was randomly sampled to determine the number of falls sustained by each patient. Impact direction and force were stochastically sampled from a range of possible scenarios. The effect of wearing a hip protector was simulated by applying attenuation coefficients to the impact force (12.9 %, 19 % and 33.8 %, as reported for available devices). A patient was considered fractured when impact force exceeded the femur strength. Without hip protector, virtual patients experienced 66 ± 5 fractures in 10 years. Wearing the three devices, fracture incidence was reduced to 43 ± 4, 35 ± 4 and 17 ± 2 respectively, at full compliance. As expected, effectiveness was dependent on compliance. This In Silico trial technology can be applied in the future to test multiple interventions, optimise intervention strategies, improve clinical trial design and drug development.

Identifiants

pubmed: 39305859
pii: S0021-9290(24)00413-5
doi: 10.1016/j.jbiomech.2024.112335
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

112335

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Sara Oliviero (S)

Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Italy. Electronic address: sara.oliviero@unibo.it.

Antonino A La Mattina (AA)

Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.

Giacomo Savelli (G)

Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Italy.

Marco Viceconti (M)

Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Italy; Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.

Classifications MeSH