One in three people with sciatica still has pain a year later, but doctors cannot predict who will recover and who will not. This fellowship aims to change that by tracking 180 people with acute sciatica over 12 months, combining monthly pain diaries with detailed biological and psychological measurements. Current care treats sciatica as a simple recovered-versus-persistent binary, which ignores the reality that patients follow at least four distinct recovery patterns. Without knowing which trajectory a patient is on, clinicians cannot offer personalised treatment or early intervention. This project will identify those trajectories, then interview patients from each group to understand what influenced their path. If successful, the research will give GPs and physiotherapists a practical way to identify patients at risk of long-term pain within weeks of onset. That would reduce the uncertainty that compounds suffering, and move sciatica care from one-size-fits-all towards targeted treatment based on individual recovery patterns. The findings will be published for clinicians and patients, with accessible summaries to support shared decision-making in primary care.
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Background Spine-related leg pain ('sciatica') is very common (43% lifetime prevalence), and highly heterogeneous in presentation, treatment-response, and outcome. At least one third of patients develop symptoms lasting one year or longer. Different patho-mechanisms likely underlie this heterogeneity. There are no robust prognostic factors for symptom persistence, preventing early identification of those at risk of persistent pain. Prognostic uncertainty compounds patient suffering and hinders personalised care. Importantly, sciatica symptom behaviour over time (recovery trajectory) is highly variable, and a dichotomy of 'recovered' versus 'persistent pain' is a stark oversimplification. Four distinct recovery trajectories have been identified in sciatica, and promisingly, baseline factors prognostic for trajectory have been identified. This suggests potential for understanding and predicting recovery in sciatica as a temporal pattern rather than a snapshot in time. However, this finding came from a cohort combining acute and chronic sciatica, obscuring features distinct to early sciatica, and lessening the clinical utility of these prognostic factors in identifying which patients might go on to develop persistent pain. Dataset I have coordinated and can access a longitudinal study (FORECAST) which uses a deep-phenotyping approach to seek mechanism-related prognostic factors for pain persistence in n=180 people with strictly acute/subacute sciatica. I have collected multidimensional baseline measures representing psychosocial, somatosensory (e.g. quantitative sensory testing), and blood inflammatory profiles. I have strategically added monthly pain ratings and repetition of phenotypic key measures at 12 months to enable additional trajectory analysis. In this fellowship, I will combine these additional measures, qualitative interviews, and the existing dataset, to provide granular detail on the prognosis of acute/subacute sciatica. Combining an in-depth qualitative exploration alongside highly detailed quantitative measures I will establish the nature and prediction of distinct recovery trajectories. Aims and Methods Identify common recovery trajectories I will use latent class trajectory modelling on sciatica leg pain scores collected monthly over 12-months to identify distinct recovery trajectories. Explore participant perceptions I will conduct semi-structured interviews with 8-10 participants from each trajectory class, exploring patient experiences and perceptions of factors that influenced their individual trajectory. Thematic analysis will establish common themes between individuals, giving voice to patient perspectives . This complementary information will contextualise my quantitative data. Describe phenotypic profiles for participants clustered according to recovery trajectory To inform the dominant mechanisms within each emerging trajectory class and their temporal development, I will explore phenotypic profiles of participants at baseline using discrimination analysis. Explore the temporal development of phenotypic measures in the trajectory subgroups, using repeated measures analysis of variance. Exploit the deeply-phenotyped FORECAST dataset seeking mechanism-related prognostic factors for trajectory class Discriminant analysis and multivariate adaptive regression splines will be used to identify baseline features that associated with trajectory classes. Anticipated impact: I will disseminate findings via publications and conferences, producing accessible summaries for patients and clinicians. My findings will contribute to the understanding of possible mechanisms underlying heterogeneity in sciatica recovery patterns. This will enhance prognostic information for patients and clinicians, lessening uncertainty and associated suffering while facilitating a pathway towards personalised care.
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