Big Data, Big Knowledge: Big Data for Personalized Healthcare.

The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the "physiological envelope" during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority

Probability of osteoporotic vertebral fractures assessment based on DXA measurements and finite element simulation

Osteoporotic vertebral fractures represent 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 measurements. The combination of biomechanical models with clinical studies could better estimate bone strength and support the specialists in their decision. A model to assess the probability of fracture, based on the Damage and Fracture Mechanics has been developed, evaluating the mechanical magnitudes involved in the fracture process from clinical bone mineral density measurements. The model is intended for simulating the degenerative process in the skeleton, with the consequent lost of bone mass and hence the decrease of its mechanical resistance which enables the fracture due to different traumatisms. Clinical studies were chosen, both in non-treatment conditions and receiving drug therapy, and fitted to specific patients according their actual bone mineral density measures. The predictive model is applied in a finite element simulation of the lumbar spine. The fracture zone would be determined according loading scenario (fall, impact, accidental loads, etc.), using the mechanical properties of bone obtained from the evolutionary model corresponding to the considered time. Bone mineral density evolution in untreated patients and in those under different treatments was analyzed. Evolutionary curves of fracture probability were obtained from the evolution of mechanical damage. The evolutionary curve of the untreated group of patients presented a marked increase of the fracture probability, while the curves of patients under drug treatment showed variable decreased risks, depending on the therapy type. The finite element model allowed obtaining detailed maps of damage and fracture probability, identifying high-risk local zones at vertebral body, which are the usual localization of osteoporotic vertebral fractures. The developed model is suitable for being used in individualized cases. The model might better identify at-risk individuals in early stages of osteoporosis and might be helpful for treatment decisions

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

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