Depression and anxiety symptoms are common in Coronary Heart Disease (CHD) [1] [2]. It is both a causal factor [3,4] and poor prognostic indicator [5], being associated with a range of adverse outcomes, including mortality [6,7], but mechanisms for such associations are incompletely understood [8]. Management strategies in patients with comorbid mood disorders and CHD are problematic [9], partly because of the overlap of symptoms of depression and anxiety with the symptomatology of long-term heart conditions [10]. Randomised controlled trials indicate that antidepressants are effective in improving mood in individuals with depression and CHD [11,12], but have not reduced adverse cardiac outcomes and mortality [13]. There is as yet inconclusive information regarding the dynamic interaction between symptoms of depression and anxiety and how they correlate to CHD and its progression.

Screening for depression in CHD has been controversial since its implementation. Citing the high prevalence of depression in CHD, routine screening for depression in this patient population was recommended by the American Heart Association (AHA) in 2008 [14], but this was challenged shortly after, pointing to a lack of evidence that screening improved outcomes [15]. In the UK, screening for depression was adopted on the Quality and Outcomes Framework (QOF) from 2006 [16], but has since been dropped. NICE guidelines do not recommend screening, although GPs are advised to be alert to depression in at-risk patients (with previous history of mental illness or chronic physical conditions) [17]. There indeed may be insufficient evidence from RCTs to support the recommendation of screening in CHD [18], however there is evidence that screening in conjunction with active management of depression in CHD and diabetes by means of collaborative care may be associated with improved physical and mental health outcomes [19].

One disadvantage of screening is the generation of multiple false positives–i.e. individuals who have transient distress. There is a shortage of longitudinal analysis than can accurately assess the persistence symptoms of depression and anxiety in the course of CHD, and whether an initial screen for these symptoms can predict persistence. Screening might be informative as the comorbidity between CHD and mental disorders increases readmission rates, and overall and outpatient healthcare costs [20], and screening could help to identify and diminish these increased costs. Furthermore, health-related quality of life (QOL) is reduced in CHD patients who have comorbid mental disorders [21], and it is not known whether a single screening tool could help identify those at risk for lower quality of life.

With this in mind, we explore the extent to which a one-off, baseline measure for depression and anxiety symptomatology is predictive of future outcome in a cohort of patients with CHD. Our aims were threefold: Firstly, we aimed to determine the stability of a single positive screen for depression and anxiety. Secondly, we aimed to determine the difference in healthcare costs between those positive and those negative at baseline. Thirdly, we aimed to analyse the differences in quality of life among those measuring positive and those measuring negative. By meeting these objectives we answer how much predictive information a one-off measure of depression and anxiety in CHD patients can provide, an important matter when considering the potential of this measure as a future screening tool in these patients.

The population used for this study is derived from the cohort conducted by the UPBEAT UK research programme [22]. This cohort consists of participants identified and recruited from CHD registers in 17 general practices in South London. The sampling frame consisted of 2,938 total number of CHD register patients amongst these 16 practices. These patients were invited to participate by their local GP, of which 917 agreed to be contacted. Of those contacted, 803 (87.6%) agreed to participate and were recruited into the study, and comprises the population we used for these analyses.

Each participant had an initial baseline interview which included the Clinical Interview Schedule Revised (CIS-R) [23], Hospital Anxiety and Depression Scale (HADS) [24], and Patient Health Questionnaire (PHQ-9) [25] to identify depression and anxiety symptoms. They then had follow-up assessments using the HADS and PHQ-9 at 6, 12, 18, 24, 30, and 36 months, done by phone interviews. Each participant therefore had up to 7 time points recorded for symptoms of depression and anxiety. Costs were measured using service use data from the Client Service Receipt Inventory (CSRI) [26], and quality of life data was gathered using the 12-item Short-Form Health Survey (SF-12) [27], both also applied at 6 month intervals.

For the purpose of this analysis, we tested whether a positive result on the HADS at initial assessment was a useful predictor of future reporting of symptoms, costs, and quality of life, and thus a potentially reliable tool for screening patients in this population. The HADS has previously been reported to be a valid screening measure in cardiac patients [28]. Here, we used the HADS total score—comprised of the sum of the depression (HADS-D) and anxiety (HADS-A) subscales—as a measure of general distress. Recently HADS has been argued to be better seen as a general measure of distress in cardiac patients, due in part to the overlapping symptoms of depression and anxiety that make a separation into subscales unreliable [29].

To determine the best scale and cut-off to be used for screening purposes, we first ran a sensitivity analysis comparing the results of the HADS at baseline to those of the CIS-R, to test whether the HADS was an accurate measure of depression/anxiety in our patient population. We calculated the specificity and sensitivity of the HADS with reference to the CIS-R using a Receiver Operating Characteristic (ROC) curve analysis, and thereafter determined the optimal threshold using the ‘point of curve closest to the (0,1)’ criteria [30]. By calculating the distance of each cut-off point on the HADS to the optimal (0,1) point on the graph, we could determine the cut-off point which gave the best combination of sensitivity and specificity. The formula used for calculating the distance was d² = [(1–Sn)² + (1 –Sp)²], where Sn = sensitivity and Sp = specificity [30].

Having determined the cut-off for the HADS as our measure for general distress, we proceeded with the analysis in accordance with our study aims. We tested the initial baseline measurement of the HADS to assess its predictive accuracy across three domains: 1) as a predictor of ongoing symptoms of depression and anxiety; 2) as a predictor of healthcare costs; and 3) as a predictor of quality of life. All analyses were conducted using Stata version 11.2.

We have demonstrated that a 12-item questionnaire on depression and anxiety symptoms, which is simple and quick to complete in a primary care setting, is a valid measure with an optimal cut-off of 12 in a CHD population. This measure is predictive of future symptoms of distress, healthcare costs, and quality of life.

This study has several strengths. It is the first to report on the effect of a baseline measure for continuing reporting of symptoms of depression and anxiety over a multi-wave follow-up. It also shows how the reporting of these symptoms negatively affects quality of life and increases primary and secondary healthcare costs, using repeated measures across time We achieved satisfactory response rates considering this was done in a primary care setting, constrained with an opt-in recruitment method, and follow-up rates were high throughout the course of the study. Whilst the HADS showed good reliability as compared to the CIS-R at baseline, and we selected the cut-off point with the best overall balance of sensitivity and specificity, we did not have a gold-standard measure to compare HADS to across time. This notwithstanding is counteracted by the fact that we aimed primarily to test whether the results of one baseline measure held firm across time and could predict the persistence of these symptoms, which our data suggests was indeed the case. The study population represents a primary care sample, and whilst patients were recruited directly from the surgeries based solely on an existing diagnosis of CHD, it does not necessarily represent the CHD register as a whole. We also did not remove patients with a positive diagnosis for mood disorders, or under current treatment for depression and/or anxiety from the main analysis, as our data regarding these circumstances was unreliable. However, some authors have suggested removing patients with diagnosis of depression from screening instruments [37], as this would lessen the probability of a selection bias affecting the results, and thus we ran the analysis removing those patients with current treatment for depression, using self-report data. This sensitivity analysis showed no effect on our results on future reporting of symptoms, costs, or quality of life.

Most studies in the existing literature regarding depression and anxiety in the context of CHD use a single assessment and follow-up over time to determine results. We used a multi-wave, repeated measures sample with which we can assess the pattern of reporting of symptoms over time, and have been able to track the results of validated measures of distress, costs, and quality of life, able in this way to accurately assess differences across time between those patients with symptoms of distress at baseline and those without these symptoms.

We echo the recommendations made by previous authors regarding screening for depression in primary care [18], and add that in patients with CHD, anxiety symptoms are equally important and screening should incorporate both. We have shown the HADS to be a reliable predictive measure as a screening tool for symptoms of distress, and to have a cut-off with sufficient sensibility and specificity to identify these symptoms.

This study suggests that the screening of symptoms of depression and anxiety in this population may identify those who are at risk for continuing reporting of these symptoms, and additionally, identify those with a lower quality of life, and those accruing a higher cost to the healthcare system. Furthermore, the HADS is a simple and often-used tool to detect symptoms of depression and anxiety, and we found a score of 12 and above to be a reliable cut-off in this population. This combined depression/anxiety score could perhaps serve better as a screening tool, and we recommend exploring its use further amongst GPs in primary care practice, as well as considering its inclusion in future revisions of the QOF.

We suggest a procedure to dictate future policy in these patients. After a baseline screen, our data suggest that after a negative screen for depression and anxiety symptoms, more follow-ups are not immediately necessary. However, patients who screen positive should be reassessed and followed up for further symptomatology. If they continue to report symptoms at next follow-up, the call is to manage their symptoms actively, rather than risk the effect these symptoms have on negative outcomes.

There have been previous studies arguing for the use of the HADS as a screening instrument in cardiac patients of both sexes [38]. However, there are still questions that remain answering. For example, after screening positive, what is the intervention needed to reduce these symptoms of distress and prevent detrimental disease-specific outcomes and higher mortality risk? This question lies at the core of the suggestion to remove screening for depression in patients with CHD, as there are no reliable management strategies in place after these patients are identified. Indeed, screening only as part of an ‘enhanced care’ package was suggested soon after it was initially implemented in the 2006 QoF [39]. Future research should focus on RCTs that provide interventions for patients who screen positive and analyse the effect on the symptoms themselves, and outcomes of the underlying CHD. A recent pilot study of a personalised care intervention for CHD patients with depression (identified by HADS-D) and current chest pain was found to be feasible and cost-effective in a primary care setting [40].

Ongoing research will analyse the data presented further. For example, the question of causality still remains unanswered, and we will explore whether these symptoms of depression and anxiety are an effect of the underlying physical condition and diminishing quality of life, or the symptoms themselves are worsening the physical condition and lowering the quality of life in these patients. This is especially relevant given the interesting finding that the physical component of the SF-12 has such a strong association to the HADS. We also aim to investigate further into the distinct trajectories of depression and anxiety symptoms that these patients present, and how to interpret those patients which have consistent positive or negative symptoms in comparison to those whose HADS score fluctuates across time.

In the meantime, the identification of concurrent mood disorders in patients with chronic conditions, such as CHD, should be prioritised, in an attempt to turn the tide on the long-term detrimental effects this comorbidity has—both on patients’ lives and the healthcare system as a whole.