Utilising group-based trajectory modelling to explore patterns of long-term outcome trajectories in patients with haemorrhagic stroke, their associated predictors, and their differences from those in patients with ischaemic stroke

Lead Supervisor
Yanzhong Wang
Reader in Medical Statistics
School of Population Health and Environmental Sciences, King’s College London
yanzhong.wang@kcl.ac.uk

Co-supervisor
Professor Charles Wolfe
Professor of Public Health
Head of School, School of Population Health & Environmental Sciences, King’s College London

Project Details

Stroke is a long-term condition that leads to negative consequences for individuals, their families and health and social care services. Although haemorrhagic stroke comprises only 15% of all strokes, evidence showed that patients with haemorrhagic stroke have poorer survival and worse long-term outcomes compared with patients with ischaemic stroke. Moreover, their long-term healthcare costs are substantial. Therefore, exploring trajectory patterns of the long-term outcomes (both physical and mental outcomes) over time and evaluating their associated predictors would be helpful to alleviate the impact of haemorrhagic stroke on patients at a higher risk of a worse prognosis. Most existing longitudinal studies report only changes in the mean value or proportion of various outcomes after stroke over time, often suggesting increasing proportion in disability and inactivity, and/or dynamic in depression and anxiety. However, this may be inaccurate when there is theoretical or empirical evidence that one trajectory shape is not assumed to fit all. Thereby, group-based trajectory modelling (GBTM) will likely to be an alternate approach to provide a better understanding of its longitudinal pattern. GBTM allowed us to simultaneously identify and characterise distinct trajectory patterns of the variable of interest within a population and can be used to identify predictor and

outcome variables associated with different trajectory patterns as well. GBTM has previously been applied to describe the developmental trajectory of depression symptoms, disability status and apathy in various populations and patient groups. However, relatively few studies have used this statistical method in exploring long-term outcomes after haemorrhagic stroke. 

Aim:

This project aims to use GBTM to explore developmental trajectory patterns of long-term outcomes and identify their associated predictors in patients with haemorrhagic stroke and their differences from patients with ischaemic stroke utilising a population-based database:

  1. To delineate the trajectories of long-term functional and mental outcomes of patients with haemorrhagic stroke and identify their associated predictors.
  2. To delineate the trajectories of long-term functional and mental outcomes and identify their associated predictors of patients with ischaemic stroke in comparison with those with haemorrhage.
  3. Compare GBTM and other group-based analysis approaches (e.g. Growth Curve Modelling) for charting long-term outcomes of patients with haemorrhagic stroke.
  4. To predict the long-term outcomes based on the trajectory patterns in patients with haemorrhagic stroke.
  5. To conduct sub-group analysis such as young strokes as they are on the rise in recent years.

Datasets

The South London Stroke Register (SLSR); The Lambeth DataNet (LDN); The European Register of Stroke (EROS). Ethical approvals obtained.

Keywords

haemorrhagic stroke, long-term outcome, trajectory, predictor