Nerve decompression surgery in the head and neck: From novel screening modalities to patient reported outcomes

This thesis focuses on improving patient selection and outcomes in nerve decompression surgery for neuralgia in the head and neck. It is structured into four parts. Part 1 evaluates the effectiveness of conservative treatments through a systematic review and meta-analysis. Results showed that anesthetic injections (with or without corticosteroids) and Botox offered the best symptom relief, although none provided lasting effects.
Part 2 describes new screening tools to improve identification of surgical candidates. These include ultrasound, MR Neurography (MRN), and a machine learning (ML) algorithm. Ultrasound proved feasible for measuring extracranial nerves and establishing reference values. MRN demonstrated improved visualization of nerve injuries compared to conventional MRI. A ML algorithm, integrated into a web-based 3D head model, showed high performance in detecting neuralgia patterns on patient pain drawings.
Part 3 assesses the clinical and economic impact of delayed surgical intervention. Many patients experienced prolonged delays—often 19 to 20 years—before surgical referral. Cost analysis revealed conservative treatment costs were significantly higher over time than surgery. Outcomes were also less favorable when surgery was delayed beyond 2.9 years after symptom onset, identifying an optimal window for intervention.
Part 4 focuses on patient-reported outcomes and surgical optimization. A content mapping study found current PROMs lacking in coverage and readability. To address this, the HEADACHE-Q was developed based on patient interviews, resulting in a comprehensive framework covering health-related quality of life and experience of care.
Together, these studies provide a foundation for earlier diagnosis, improved patient selection, and optimized outcomes in the treatment of head and neck neuralgia.