Patients (2.7M in EU) with positive cancer prognosis frequently develop metastases (≈1M) in their remaining lifetime. In 30-70% cases, metastases affect the spine, reducing the strength of the affected vertebrae. Fractures occur in ≈30% patients. Clinicians must choose between leaving the patient exposed to a high fracture risk (with dramatic consequences) and operating to stabilise the spine (exposing patients to unnecessary surgeries). Currently, surgeons rely on their sole experience. This often results in to under- or over-treatment. The standard-of-care are scoring systems (e.g. Spine Instability Neoplastic Score) based on medical images, with little consideration of the spine biomechanics, and of the structure of the vertebrae involved. Such scoring systems fail to provide clear indications in ≈60% patients. The HEU-funded METASTRA project is implemented by biomechanicians, modellers, clinicians, experts in verification, validation, uncertainty quantification and certification from 15 partners across Europe. METASTRA aims to improve the stratification of patients with vertebral metastases evaluating their risk of fracture by developing dedicated reliable computational models based on Explainable Artificial Intelligence (AI) and on personalised Physiology-based biomechanical (VPH) models.Introduction
Method
Spinal implant-associated infections (SIAI) require combined surgical and antimicrobial treatment and prolonged hospital stay. We evaluated the clinical, laboratory, microbiological and radiological characteristics and treatment approaches in patients with SIAI. Consecutive adult patients with SIAI treated between 2015 and 2017 were prosepctively included. SIAI was defined by: (i) significant microbial growth from intraoperative tissue or sonication fluid, (ii) intraoperative purulence, secondary wound dehiscence or implant on view, (iii) radiographic evidence of infection and fever (>38°C) without other recognized cause, increasing back pain or neurologic impairment, (iv) peri-implant tissue inflammation in histopathology.Aim
Method
