A predictive mechanical model for evaluating vertebral fracture probability in lumbar spine under different osteoporotic drug therapies

López,E. (Universidad de Zaragoza) ; Ibarz,E. (Universidad de Zaragoza) ; Herrera,A. (Universidad de Zaragoza) ; Puértolas,S. (Universidad de Zaragoza) ; Gabarre,S. ; Más,Y. ; Mateo,J. (Universidad de Zaragoza) ; Gil-Albarova,J. (Universidad de Zaragoza) ; Gracia,L. (Universidad de Zaragoza)
A predictive mechanical model for evaluating vertebral fracture probability in lumbar spine under different osteoporotic drug therapies
Resumen: Osteoporotic vertebral fractures represent a major cause of disability, loss of quality of life and even mortality among the elderly population. Decisions on drug therapy are based on the assessment of risk factors for fracture from bone mineral density (BMD) measurements.A previously developed model, based on the Damage and Fracture Mechanics, was applied for the evaluation of the mechanical magnitudes involved in the fracture process from clinical BMD measurements. BMD evolution in untreated patients and in patients with seven different treatments was analyzed from clinical studies in order to compare the variation in the risk of fracture. The predictive model was applied in a finite element simulation of the whole lumbar spine, obtaining detailed maps of damage and fracture probability, identifying high-risk local zones at vertebral body.For every vertebra, strontium ranelate exhibits the highest decrease, whereas minimum decrease is achieved with oral ibandronate. All the treatments manifest similar trends for every vertebra. Conversely, for the natural BMD evolution, as bone stiffness decreases, the mechanical damage and fracture probability show a significant increase (as it occurs in the natural history of BMD). Vertebral walls and external areas of vertebral end plates are the zones at greatest risk, in coincidence with the typical locations of osteoporotic fractures, characterized by a vertebral crushing due to the collapse of vertebral walls.This methodology could be applied for an individual patient, in order to obtain the trends corresponding to different treatments, in identifying at-risk individuals in early stages of osteoporosis and might be helpful for treatment decisions.
Idioma: Inglés
DOI: 10.1016/j.cmpb.2016.04.006
Año: 2016
Publicado en: Computer Methods and Programs in Biomedicine 131 (2016), 37-50
ISSN: 0169-2607

Factor impacto JCR: 2.503 (2016)
Categ. JCR: COMPUTER SCIENCE, THEORY & METHODS rank: 21 / 104 = 0.202 (2016) - Q1 - T1
Categ. JCR: MEDICAL INFORMATICS rank: 8 / 23 = 0.348 (2016) - Q2 - T2
Categ. JCR: ENGINEERING, BIOMEDICAL rank: 26 / 77 = 0.338 (2016) - Q2 - T2
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 32 / 105 = 0.305 (2016) - Q2 - T1

Factor impacto SCIMAGO: 0.639 - Computer Science Applications (Q2) - Software (Q2) - Health Informatics (Q2)

Tipo y forma: Article (PostPrint)
Área (Departamento): Área Traumatología y Ortopedia (Dpto. Cirugía,Ginecol.Obstetr.)
Área (Departamento): Área Expresión Gráfica en Ing. (Dpto. Ingeniería Diseño Fabri.)
Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)

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 Record created 2017-04-19, last modified 2020-02-21

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