Abstract
117: The natural history of depression and differential patterns of development among subgroups of stroke patients: The South London Stroke Register
Ayis S1, Rudd A1,2, Ayerbe L3, Crichton S1, and Wolfe C1,4
1 Department of primary care and public health sciences; Division of Health and Social Care Research King's College London, London, UK
2 Stroke Unit, Guy's and St. Thomas' NS Foundation Trust, St. Thomas' Hospital. London, UK
3 Blizard Institute, Centre for Primary Care and Public Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
4 National Institute for Health Research (NIHR) Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust, London UK, and the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) South London at King's College Hospital NHS Foundation Trust, London, UK
Introduction: The natural history of depression is complex and the mechanism of change over time is not fully understood. The study aims to detect heterogeneous patterns of depression development after stroke using group based trajectory modelling (GBTM) technique.
Method: The primary analysis comprised 761 patients who completed 5 years of follow up and survived at least 5 years after stroke, using data from the prospective South London Stroke Register (1998–2013). The Hospital Anxiety and Depression scale (HADs) was used to screen patients for depression 3 months after stroke, then annually. The Censored Tobit model was used to identify patterns of depression development.
Results: 4 distinct patterns were identified: 6.31% of patients had severe symptoms, improved slightly in early years then worsened (predicted mean HAD score, 15.74 (se = 1.06); 28.65% had moderate symptoms, a tendency to get worse over time, predicted mean score 7.36 (se = 0.35); 49.54% had mild symptoms, predicted mean 3.89 (se = 0.30), and 15.51% of the cohort, had no symptoms and remained so over time. Observed scores agreed well with predicted ones. Sensitivity analyses used to assess the robustness of the findings using samples sizes 350 to 1061 patients, based on up to 15 years follow up data, and different inclusion criteria, agreed with the primary analysis.
Discussion: The study identified subgroups with different patterns of depression development and provides first time estimates of the prevalence of each. These provide a further insight into the natural history of depression that would be useful for the applications of cost effective personalised interventions.