| dc.description.abstract | Background: Acute Coronary Syndrome (ACS) is an umbrella term that includes myocardial infarction and unstable angina. Depression is a common psychological response in patients with ACS and has been shown to be independently associated with increased mortality, cardiac events, and poor quality of life. Although the prevalence of depression after ACS tends to be stable over time, its trajectories are not, and little is known about the variation in depressive trajectories after an ACS event. Evidence suggests that trajectories of depressive symptoms are heterogenous; some patients experience minimal or no depressive symptoms while others experience transient, worsening, or persistent symptoms. Studies that have addressed this, identified two to five trajectories of depressive symptoms that vary in their stability and intensity. Therefore, it is important to understand the patterns of these trajectories in ACS patients and to identify the characteristics of different trajectory groups. To date, few studies have addressed trajectories of depressive symptoms and their predictors in patients with ACS. None of these studies were carried out in Jordan.
Aim: The aim of this study was to employ group-based trajectory modelling to identify the heterogeneous trajectories of depressive symptoms, and their predictors, following an ACS event.
Methods: This study was a prospective cohort study conducted across four hospitals in Jordan. Data were collected from ACS patients using structured interview and self-reporting questionnaires. At baseline, the following self-reported questionnaires were administered to all patients: (1) Patients Health Questionnaire (PHQ-9), (2) Multidimensional Perceived Social Support (MPSS), (4) Type D Personality (DS14), and (5) Brief COPE. Changes in depressive symptoms were evaluated by telephone at 1, 3 and 6 months using the PHQ-9. All questionnaires were in Arabic and required less than 25 minutes to complete. Nested logistic regression was used to identify predictors of in-hospital depressive symptoms. Trajectories of depressive symptoms were identified using growth curve and growth mixture modelling (GMM). Multinomial logistic regression was used to identify predictors of trajectories of depressive symptoms over six months of ACS.
Results: A total of 434 patients participated in the study. The prevalence of depressive symptoms in patients with ACS was 23.5% (n=102), based on the PHQ-9 score ? 10. In the first nested model, monthly income (?500 JOD), smoking, Type D personality, dysfunctional coping, low perceived social support, and history of depression were significant predictors of depressive symptoms. In the second nested model, Left Ventricular Ejection Fraction (LVEF<40), length of hospital stays, and the four aforementioned psychosocial variables were significant predictors of depressive symptoms. The Nagelkerke Pseudo R squared value for the demographics, and health-related behaviours model was 13.5%. Including psychosocial variables increased the R Squared to 33.7%. Likewise, adding the psychosocial variables into the clinical variables model increased the Nagelkerke Pseudo R squared value from 13.5% to 32.8%.
The prevalence of depressive symptoms at one, three and six months after ACS was 18% (n=75), 16.7% (n=67) and 15% (n=61), respectively. The cumulative incidence of depression over six months of ACS was 8.3%. Using GMM, four distinct depressive symptom trajectory groups were identified: minimal and decreasing (n=327, 75.4%), decreasing (n=39, 9.0%), increasing (n=31, 7.1%) and stable high (n=37, 8.5%). The final multivariate model explained 34.9% (Negelkerke R2) of the total variance in depressive symptom trajectory groups. The model showed that low (<500 JOD) monthly income (OR= 6.618, 95%CI, 1.630-26.876), having a history of depression (OR = 3.547, 95% CI, 1.379-9.123), Type D personality (OR= 2.544, 95%CI, 1.016-6.370), using dysfunctional coping (OR =1.073, 95% CI, 1.011-1.138), and having low perceived social support (OR =.910, 95% CI, .875-.946) were significant predictors of persistent depression compared to no depression. Further, current smoking status (OR= 4.635, 95%CI, 1.765-12.174) and problem-focused coping (OR =.874, 95%CI, .767-.996) were significant predictors of increased depression compared to no depression.
Conclusion: This is the first study in Jordan to address trajectories of depressive symptoms and their predictors after ACS. This is also the first study in Jordan to examine the incidence of depressive symptoms following ACS. As depressive symptoms were found to be prevalent in hospitalised patients with ACS, screening for depression is highly recommended. This study also found that trajectories of depressive symptoms in patients with ACS are heterogenous and that a single trajectory does not represent the change over time. Therefore, screening of depressive symptoms in patients with ACS should not be limited to hospitalisation but should continue after discharge from the hospital.
Finally, the results of this study greatly inform the body of knowledge in this area of ACS care. They are particularly important for Jordanian healthcare professionals, policy makers as well as for patients themselves. Timely assessment and treatment of depression in the ACS cohort has the potential to optimise patient recovery, reduce mortality & morbidity whilst improving quality of life. | en |
