Abstract
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.
Original language | English |
---|---|
Awarding Institution |
|
Supervisors/Advisors |
|
Award date | 8 Apr 2022 |
Place of Publication | Maastricht |
Publisher | |
Print ISBNs | 9789463616317 |
DOIs | |
Publication status | Published - 2022 |
Keywords
- Spine
- Physiotherapy
- Data Science
- Preoperative Care