Abstract
Background and Aims: Depression and anxiety are risk factors for developing Coronary heart Disease (CHD), and are associated with poor disease outcomes and mortality. However, there is little information describing repeated measures and longitudinal data that may study the trajectories of depression and anxiety over time,how these are manifested in the context of CHD, and their relationship to sociodemographic measures, cardiac risk factors, and measures of disability.
Methods: Using a primary care cohort of 803 patients with a diagnosis of CHD, a latent class growth curve model was developed to study the distinct trajectories of depression and anxiety symptoms over a 3 year period, with 7 distinct 6-month follow-up points.Logistic regression analysis was then conducted to study the association between latent classes and baseline risk factors.
Results: The 5-class model yielded the best combination of statistical best-fit analysis and clinical correlation. These classes were:‘stable asymptomatic’ (n = 558), ‘increasing symptoms’ (n = 64),‘decreasing symptoms’ (n = 15), ‘chronic highly symptomatic’(n = 55), and ‘fluctuating symptoms’ (n = 111).The comparison group was the ‘stable asymptomatic’ class. Female sex was associated with the ‘fluctuating class’. Non-white ethnicity was associated with ‘chronic high’ and ‘worsening’ class. Current smoking was associated with all classes, particularly the ‘chronic high’ class.Chest pain was associated strongly with ‘chronic high’ class. Multi-variate models will analyse these associations further.
Conclusions: The distinct trajectories of depression and anxiety in CHD will provide important information on the specific ways in which these symptoms affect patients, and provide unique insight into the monitoring and management of this comorbidity.
Methods: Using a primary care cohort of 803 patients with a diagnosis of CHD, a latent class growth curve model was developed to study the distinct trajectories of depression and anxiety symptoms over a 3 year period, with 7 distinct 6-month follow-up points.Logistic regression analysis was then conducted to study the association between latent classes and baseline risk factors.
Results: The 5-class model yielded the best combination of statistical best-fit analysis and clinical correlation. These classes were:‘stable asymptomatic’ (n = 558), ‘increasing symptoms’ (n = 64),‘decreasing symptoms’ (n = 15), ‘chronic highly symptomatic’(n = 55), and ‘fluctuating symptoms’ (n = 111).The comparison group was the ‘stable asymptomatic’ class. Female sex was associated with the ‘fluctuating class’. Non-white ethnicity was associated with ‘chronic high’ and ‘worsening’ class. Current smoking was associated with all classes, particularly the ‘chronic high’ class.Chest pain was associated strongly with ‘chronic high’ class. Multi-variate models will analyse these associations further.
Conclusions: The distinct trajectories of depression and anxiety in CHD will provide important information on the specific ways in which these symptoms affect patients, and provide unique insight into the monitoring and management of this comorbidity.
| Original language | English |
|---|---|
| Pages (from-to) | 146-147 |
| Number of pages | 2 |
| Journal | Bipolar Disorders |
| Volume | 18 |
| Issue number | Suppl 1 |
| DOIs | |
| Publication status | Published - 3 Jul 2016 |