Chronic postsurgical pain (CPSP) following caesarean delivery affects 15% of patients and is associated with impaired function and reduced maternal quality of life. While acute pain severity, mood, and sleep disruption are implicated, mechanistic understanding remains limited, and no validated risk stratification tools exist. This study will investigate biopsychosocial predictors of CPSP using multimodal data from the UK Biobank, including clinical, psychological, behavioural, imaging, biomarker, and genetic domains.
Women undergoing caesarean delivery will be identified via OPCS-4 codes in Hospital Episode Statistics. The caesarean date will serve as the index event. Predictors will be extracted from the first 12 months postpartum; pain outcomes will be derived from questionnaire data collected after delivery.
Research Question:
Which biopsychosocial risk factors are associated with the development of chronic pain after caesarean delivery?
Objectives:
1. Define a caesarean delivery cohort using hospital records.
2. Extract multimodal data:
* Sociodemographics, age, co-morbidities, BMI, obstetric data
* Acute and chronic pain symptoms, affective symptoms, adverse childhood experiences, cognitive function, energy/fatigue
* Subjective and actigraphy-derived sleep metrics
* Brain imaging markers (e.g., grey matter volumes, connectivity) collected at any timepoint
* Inflammatory and metabolic biomarkers
* Polygenic scores and pain-relevant SNPs
3. Model associations with CPSP.
4. Explore mediation via sleep and pain interference (functional pain metrics).
5. Assess the feasibility of predictive modelling using multimodal data.
Findings will provide mechanistic insights and inform future risk stratification strategies for chronic pain in postpartum populations.