Depressive vulnerabilities predict depression status and trajectories of depression over one year in persons with acute coronary syndrome
Any type of content formally published in an academic journal, usually following a peer-review process.
Depression is prevalent in patients with coronary heart disease, with the prevalence estimated at approximately 20% in patients with myocardial infarction . This is significantly higher than that seen in general population samples . The importance of depression is highlighted not only in its prevalence, and its impact on quality of life, but also on the ability of depression to predict cardiovascular prognosis [3-5].
However, while a large literature concerns the prediction of prognosis in depressed cardiac patients, relatively little research is concerned with what happens to depression after the acute hospitalisation phase. Depression is a chronic, episodic condition, and therefore research on what happens to depressive symptoms in the post-acute phase potentially provides vital information for intervention design. While the prevalence of depression is comparatively steady over time, this masks the different trajectories symptoms of depression take [6-8]. Indeed, sophisticated studies have shown different patterns of resolving and persistent depression in patients with heart disease [7, 8]. For example, Martens et al.  surveyed 287 patients post-hospitalisation for myocardial infarction at 2 and 12 months. They categorised four groups of patients in relation to depressive symptom status: non-depressed, mildly depressed, moderately depressed and severely depressed. Similarly, Kaptein et al.  followed 475 patients with myocardial infarction every 3 months over one year, and their results showed that five distinct groups regarding depression:no depressive symptoms, mild depressive symptoms, moderate and increasing depressive symptoms, significant but decreasing depressive symptoms and significant and increasing depressive symptoms. Thus, the evolution of depression is complex, and in order to design optimal interventions, more knowledge on the predictors of depressive symptoms and such depressive trajectories is needed .
While some research has established predictors of depression in patients with coronary heart disease from easily available variables recorded as part of standard hospital care, the results are often contradictory [7, 8, 10-12]. For example, age, sex, medications and left ventricular function have been shown to predict depression in cardiac patients in some of these findings, but not in others. Furthermore, such findings are atheoretical, and thus provide little clue as to how to intervene in such populations [9, 13]. A paucity of evidence exists assessing the relative importance of theoretical vulnerabilities, and their associated interventions, regarding risk of depression and trajectories of depression after acute coronary syndrome (ACS) . While a small number of studies have assessed theoretical vulnerabilities to depression – for example, stressful life events, personality and cognitions have all been associated with depression in cardiac patients [7, 15, 16] – such studies have not measured these vulnerabilities simultaneously, or have not assessed their association with trajectories of depression post-ACS.
These vulnerabilities are especially important, given recent findings which suggest that, in patients with ACS, such vulnerabilities predict depression better than do demographic or disease variables [13, 17]. However, both these studies were limited, as they were cross-sectional, and did not allow for the direction of causality to be determined [13, 17]. Also, it was possible that recall bias in depressed patients contributed to a higher self-reported level of such vulnerabilities – thus to inflated correlations between the variables. We therefore report on longitudinal data from our cohort. We aimed to determine a) whether depressive vulnerabilities predicted depression over time, when controlling for baseline depression, and b) whether these vulnerabilities better predicted different types of depression (e.g. persistent depression).