For people with lower back pain, a lumbar spondylodesis is often preferred as a last resort if other treatments do not provide a solution. Undergoing lumbar spondylodesis comes with major health risks. Despite recent advances in surgery and anaesthesiological techniques, not every patient benefits from a lumbar spondylodesis; on average, 56% of patients experience a clinically relevant reduction in pain. The aim of this thesis was therefore to improve the health of people who choose a lumbar spondylodesis by adopting an increasingly predictive, preventive, personalised and participatory (P4) perioperative care path. The research conducted for this thesis provides insights into effective preoperative training methods. In addition, a predictive tool has been developed that can improve joint decision-making for spondylodesis. Initial steps have been taken towards integration of a modern data infrastructure by introducing a new 'omics' family called functionomics.
|Award date||8 Apr 2022|
|Place of Publication||Maastricht|
|Publication status||Published - 2022|
- Data Science
- Preoperative Care