Key words: Spine, Physiotherapy, Data Science, Preoperative Care

"The Optimal Pahtway To Lumbar Spinal Fusion; Improving perioperative health and care with patients opting for lumbar spinal fusion surgery"

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. And initial steps have been taken towards integration of a modern data infrastructure by introducing a new 'omics' family called functionomics.

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