Introduction

It is widely acknowledged that there is a higher occurrence of depression and anxiety in chronic patients, with a resulting impact on patients’ quality of life [1]. Investigating the determinants of anxiety and depression in chronic cardiac patients may help to tailor interventions aimed at reducing their upsurge in these patients, thereby diminishing the global health burden.

The literature on this topic indicates the existence of higher anxiety and depression rates in cardiac patients [2–6]. Post-myocardial infarction patients exhibit a rate of depression of 24 %, while the rate of depression of the control population is 17.6 % [7]. A depression incidence rate of approximately 25 % has been found in coronary heart disease patients [8]; moreover, up to 26 % chronic heart failure patients suffer from major clinical depression [9]. Large existing evidence supports the prevalence of depression in chronic heart failure patients [10]. Anxiety rates range from 18.5 to 26 % among post-myocardial infarction patients [11] and are at 41.4 % in coronary disease patients [12].

The literature suggests the existence of a bidirectional link between anxiety/depression and cardiac patients health-related quality of life. On the one hand, the experience of chronic illness, accompanied by physical impairment and hospitalization, can be difficult to manage and can cause psychological distress in patients due to the changes and limitations it determines in their lifestyles. Hospitalization may compromise patients’ psychological well-being, affecting their physical recovery and compliance to therapies. On the other hand, cardiac patients suffering from anxiety/depression will face adverse effects on cardiovascular health outcome [13–16].

In the following pages, evidence of both sides of this bidirectional link is presented.

First, the literature showing worse health conditions in cardiac patients caused by anxiety and depression is introduced. This branch of the literature explains the rationale for the interest in a second strand of research investigating the causes of the increase in anxiety and depression among cardiac hospitalized patients, and calls for additional efforts in isolating, and possibly controlling, any further booster of anxiety and depression in such chronic patients.

Knowing the causes of the arousal of anxiety and depression in the cardiac healthcare delivery service is the first step in an intervention aimed at reducing such negative psychological states and consequently the health burden of cardiac patients.

From anxiety/depression to worse health conditions in cardiac patients

Anxiety and depression are known to be potential risk factors for cardiovascular patients: They are related to the experience of recurrent events of hospitalization of coronary artery disease patients [16, 17] and affect the cardiovascular patient’s Health-Related Quality of Life [18, 19].

Moreover, chronic patients who are also depressed record statistically significant lower mean health scores than chronic patient not experiencing depression [1]. The incidence rates of depressive symptoms in cardiac population are higher with respect to the lay population. Depression may fall into the category of both episodic psychological risk factors (lasting from several months to 2 years with a tendency to recur) and chronic psychological risk factor, promoting gradual progression of coronary artery disease for coronary syndromes. This may account for the observed long-term relationship between depression and cardiac episodes [7].

Depression in patients hospitalized for acute myocardial infarction significantly rise the 1-year cardiac mortality rate (8.3 % of the depressed women vs. 2.7 % of the non-depressed, and 7.0 % for depressed men vs. 2.4 % on non-depressed): Its influence is not affected by other post-myocardial infarction risks [13].

Evidence suggests that anxiety is among those acute psychological factors activating pathophysiological mechanisms that trigger myocardial ischemia during daily life activities [20, 21].

From chronic illness to anxiety/depression

Evidence offered by the World Health Surveys data on worldwide prevalence of depression, asthma, angina, arthritis and diabetes shows that chronically ill people are significantly more likely to suffer from depression than those not suffering from any chronic condition [1]. Medical comorbidities and severe medical illness are among the mechanisms leading to depression for chronic cardiac patients [22]; a higher NYHA (New York Heart Association) class is associated with both depression and anxiety, while comorbid physical illness with anxiety [23].

Depression, which may occur as a reaction to acute myocardial infarction, is a major determinant of recovery. Patients showing depressive symptoms before admission may experience an intensification of such symptoms during hospitalization, while other patients, who are not depressed at admission, may experience depression in reaction to the hospitalization experience and the feeling of helplessness linked to the hospital experience [24]. Evidence on low-income heart failure patients showed the correlation between disease burden and major depressive disorder symptoms; disease burden mediates between post-traumatic stress disorder symptoms and major depressive disorder symptoms [25].

Living with coronary heart disease may trigger anxiety: Myocardial infarction patients can experience their sick conditions as a threat for themselves. The fear of death, lack of autonomy, poor sexuality and changes in family and social role can cause the arousal of anxiety in these patients [26].

Uncertainty in illness and coping strategies as possible levers of anxiety and depression in hospitalized cardiac patients

In cardiac patients, the hospitalization episode and the disease experience are significantly characterized by uncertainty in illness [27], which is a well-known lever for anxiety and depression in chronic hospitalized patients [28]. It also augments the negative impact of the state of chronic illness on patients’ quality of life and obstacles patients’ coping strategies [28–32].

Notwithstanding the above considerations, the investigation on the increase in anxiety and depression in cardiac patients and its relation to subjective well-being, uncertainty in illness and coping is still partial and fragmented. There is evidence of a positive relation between the subjective well-being of heart failure patients and task-focused coping strategies, while a negative relation has been established between subjective well-being/maladaptive coping strategies and depressive symptoms [33]. In patients following an acute coronary syndrome event, the occurrence of a left ventricular ejection fraction (an index of prognostic severity) was predicted by emotion-focused coping strategies. Moreover, it has been shown that chronic cardiac patients adopting adaptive coping strategies are less exposed to depression [34]. The indirect effect of uncertainty as cause of depression in patients with atrial fibrillation has also been proven [35]. Finally, uncertainty and coping strategies have been found to explain a large part of variance in emotional distress prior to hospital discharge following myocardial infarction [30].

Some studies, focusing on different chronic patients, have jointly considered the correlations between anxious and depressive symptoms on the one hand, and global disability, psychological variables and uncertainty and coping strategies in hospital on the other hand [36–39]. However, to our knowledge, neither in Italy nor in an international context, have such correlations been investigated in cardiac patients.

The present contribution aims at filling this gap: It estimates the probability of cardiac patients developing anxiety and depression when hospitalized, and at verifying the impact of uncertainty, coping and quality of life on the aforementioned psychological conditions. The hypothesis tested is that, in hospitalized cardiac patients, uncertainty in illness may play a crucial part in the development of anxiety and depression, at both borderline and pathological levels. Coping strategies may help to contrast the negative effects of uncertainty.

Data collected have been employed within an econometric analysis, which applies statistical techniques to economic and social problems and theories. The probability of patients crossing the borderline threshold for both anxiety and depression has been estimated, along with the probability of such patients going beyond their pathological thresholds.

Results suggest that uncertainty in illness is associated with the probability of crossing the borderline threshold for both anxiety and depression and that the emotion-focused/Active Coping style, Positive Reinterpretation and Growth, are inversely related to anxiety and depression, while the problem-focused/passive coping style, Restraint Coping, are positively related to anxiety.

This study enriches the literature related to the burden of cardiac chronic illness. In fact, knowing more about the extent of such correlations may provide useful suggestions on how to better assist chronic cardiac patients and how to support them along their long-term care path.

Methods

Patients and tools employed

A convenience sample of 200 consecutive patients was recruited between January 2014 and March 2014 from the operative units of Cardiology, Cardiosurgery Angiology and Vascular Surgery of the university hospital Azienda Policlinico Universitario “Vittorio Emanuele” in Catania, Italy.

In order to be considered eligible for this study, patients had to satisfy the following criteria: Their cardiac disease had to be clinically defined, and they had to be aware of their diagnosis. Those who were unable to understand the questionnaire were excluded from the analysis. Overall, 97 % of the patients enrolled agreed to answer to the questionnaire.

Patients were asked to complete a test battery: The questions proposed asked for information on their clinical history, how they perceived their state of illness and the quality and quantity of information received from physicians.

Hence, patients answered the Hospital Anxiety and Depression Scale (HADS), the Coping Orientation to Problems Experienced (COPE) questionnaire and the Mishel Uncertainty in Illness Scale (MUIS), together with the EuroQol-5D (EQ-5D).

The HADS is composed of two scales, designed to measure anxiety and depression, each encompassing seven items on a four-point Likert scale, which ranges from 0 to 3. For each scale, scores less than or equal to 7 indicate non-cases, scores between 8 and 10 indicate borderline cases—which could potentially evolve in psychopathology—and scores greater than 11 indicate pathological cases [40]. The HADS is widely applied both in Italy and internationally and is considered a valid and reliable instrument [41–45]: It has been employed in the diagnosis of anxiety and depression in patients experiencing chest pain [46, 47], patients surviving recent myocardial infarction [48], patients in emergency departments suffering non-cardiac chest pain [49] and cardiac patients [50]. The HADS, initially designed to identify and quantify depression and anxiety in an outpatient hospital setting [51], has been validated and used in different inpatient settings too [52–54].

The COPE questionnaire [55] is aimed at analyzing the coping strategies adopted by patients as a result of their state of illness. Overall, it comprises 60 items and encompasses 15 dimensions, namely Active Coping, Planning, Seeking Instrumental Social Support, Seeking Emotional Social Support, Suppression of Competing Activities, Religion, Positive Reinterpretation and Growth, Restraint Coping, Acceptance, Focus on Emotions, Denial, Mental Disengagement, Behavioral Disengagement, Alcohol/Drug Use, and Humor. Each dimension includes 4 items scoring between 1 and 4.

The COPE has been used with cardiac patients to assess their efforts in coping with coronary artery bypass surgery [56], to study the association between coping strategies and post-open-heart surgery levels of distress on the one hand and the recovering of functional capacity on the other [57], and to explain patients’ attendance at cardiac rehabilitation [58]. The COPE has been translated and validated in different national contexts, including Estonia, Croatia, Germany, France, Spain and Italy [59, 60].

Uncertainty in Illness was assessed through a unidimensional uncertainty construct, recently validated in Italy [61], made up of eighteen items on a five-point Likert scale, between 1 and 5. It is the result of a confirmatory factor analysis performed for each of the four MUIS original dimensions, which validated three out of the four original dimensions: ambiguity (vagueness and indistinctness of the state of illness), inconsistency (contradictory information received) and complexity (cues about the treatment) [62, 63]. Nevertheless, as these dimensions were highly correlated, all their items were used to explain a new single factor called Uncertainty. This unidimensional construct exhibited positive and highly significant standardized regression weights, which can be interpreted as the correlations between the observed variables (the item scores) and the corresponding common construct (Uncertainty). It also showed satisfactory fit indexes, generally used to assess how well the model matches the observed data. The Uncertainty construct groups the great majority of the items encompassed by the three (out of the four) separate dimensions of the MUIS, which has been widely employed to measure the perception of uncertainty in illness in cardiac patients [27, 30, 64, 65].

Health-Related Quality of Life was measured by the EuroQoL-5D (EQ-5D), an extensively used multi-country, multi-attribute, non-disease-specific tool [66]. This short generic quality of life measure was chosen in order to make our results comparable with other studies tackling different chronic diseases.

The EQ-5D is based on five health attributes (mobility, self-care, usual activities, pain/discomfort and anxiety/depression), and each of them is rated according to its perceived level of severity (no problems/some or moderate problems/extreme problems). The individual assessment of the health state under consideration is then expressed by means of an index value, calculated attributing a particular set of weights to the scores of the five health dimensions. The EQ-5D worksheet also includes a Visual Analog Scale (VAS), which asks the respondents to assess their health state using a graduate interval scale: Its maximum value, 100, indicates perfect health, while its lowest value, 0, indicates the worst imaginable health [67].

Statistical analysis: the bivariate probit model

The probabilities of experiencing anxiety and depression when hospitalized can be estimated using a bivariate probit model (biprobit). This model allows clarifying the causal relationship among the crucial variables that were considered in the analysis. It includes two latent variables, y n1 and y n2, not directly observable and assumed to be a linear function of a set of explanatory variables, which might be or might not be the same for both of them [68, 69].

The biprobit model has been employed in studies concerning health care for example, in end-stage renal disease patients, to assess their probability of undergoing hemodialysis versus peritoneal dialysis at public or private centres [70], or in maternal care, to estimate the probability of delivering at healthcare facilities and the child probability of survival [71].

The system of equations to be estimated is the following:

$$y_{1i}^{*} = \beta_{1}^{{\prime }} x_{1i} + u_{1i}$$
(1)
$$y_{2i}^{*} = \beta_{2}^{{\prime }} x_{2i} + u_{2i}$$
(2)

where \(y_{1i}^{*}\) and \(y_{2i}^{*}\) are the latent variables, which, in this case, represent the probability of reporting anxiety and the probability of experiencing depression, respectively.

The coefficients represent the effect of changes in the values of the explanatory variables on these probabilities: In particular, a positive (negative) coefficient means that an increase (decrease) in the value of this variable leads to an increase (decrease) in the predicted probability of the dependent variable.

Two biprobit specifications were estimated.

The first model looked at the probability of crossing the borderline threshold of anxiety and depression; the second model estimated the probability of crossing the pathological threshold of anxiety and depression. The explanatory variables were the same for each model and related to the information collected through the COPE, MUIS and EQ-5D questionnaires. Other control variables were age and years of illness.

Estimations were carried out using the software package Stata 10.0 [72, 73].

Results

Descriptive analysis

Descriptive statistics can be seen in Table 1, which reports the mean scores for the HADS, COPE and MUIS questionnaires contained in the worksheet.

Table 1 Descriptive statistics
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Patients were on average 66 year old—their age ranging from 19 to 87; 53 % were male and 47 % female; most of them (79 %) were married or lived with someone and 60 % had completed higher education.

Patients had been in a state of illness for a period ranging from 9 months to 40 years (average of 6 years and 7 months). They were asked about their health state before being diagnosed with a chronic disease and, on a scale from 0 to 100, the mean assessment was 73. The subsequent assessment of health state, made through the VAS, on the other hand, showed an average value of 51. Approximately, one-third of the patients (34 %) declared that their health state had worsened during the past year. The average EQ-5D score was 0.64 in a range from −0.06 to 1 (maximum value); only one patient, in the sample, evaluated his/her health state as worse than death (whose corresponding EQ-5D score is 0). These results offer evidence of how the burden of a cardiac chronic illness impacts a patients’ self assessment of his/her health-related quality of life.

As for the dependent variables of the analysis, i.e., the presence of anxiety and depression, 39 and 53 % of the patients were, respectively, clinically anxious and depressed, while 10 and 24 % of patients were pathologically anxious and depressed. This is consistent with the literature, which found high incidence rates of anxiety and depression in cardiac patients [22, 23, 26].

Correlation analysis

The base hypothesis considered is that coping and uncertainty, controlling for quality of life, may have a relevant role in the development of anxiety and depression in hospitalized cardiac patients. Therefore, in order to check which items are significantly correlated with the dependent variables (i.e., the occurrence of anxiety and depression) and to select those ones that may be included as explanatory variables in the econometric analysis, different pairwise correlations were calculated. Specifically, the correlations between each of the four HADS threshold scores, detecting the presence of anxiety and depression at the borderline and pathological level and, respectively, each score referring to all the COPE dimensions, and to the MUIS were estimated. All these correlations are reported in Table 7 in “Appendix”.

Conversely, Table 2 shows only the correlations among the four HADS threshold scores and the variables employed in the biprobit analysis.

Table 2 Correlations among the variables employed in the estimations
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The COPE dimensions exhibiting statistically significant correlations with pathological or borderline anxiety and depression were Suppression of Competing Activities, Restraint Coping, Focus on Emotions and Positive Reinterpretation and Growth (Table 7). Restraint Coping and Positive Reinterpretation and Growth were chosen as regressors. As suggested by the literature [55], they may be considered “polar”: The former is a problem-focused/passive strategy, the latter an emotion-focused/active strategy.

Restraint Coping is a problem-focused coping strategy as it is intended to impact the source of stress, and, at the same time, is a passive coping strategy as the individual waits for the right moment to do something so that he/she does not act. Positive Reinterpretation and Growth is an active coping strategy according to which the individual tries to manage the emotional consequences of the distress rather than dealing with the very source of stress.

As summarized in Table 2, Restraint Coping was inversely correlated with anxiety, be it borderline or pathological, and Positive Reinterpretation and Growth was inversely and significantly correlated with borderline and pathological anxiety and depression. The MUIS score was significantly and positively correlated with borderline anxiety and depression, and with pathological depression. These results are mainly confirmed by the two models tested by the biprobit analysis.

Moreover, age was significantly and inversely correlated with pathological depression. This reinforces its use as a control variable, in line with the literature suggesting that there might be an intrinsic reduction in susceptibility to anxiety and depression with age [74]. Experience in illness showed a negative though not statistically significant correlation with both levels of anxiety and depression. Nevertheless, the absolute magnitude of the correlation coefficient for anxiety and depression at borderline level suggests controlling for this variable as well. The EQ-5D score was significantly and inversely correlated with both anxiety and depression at borderline and pathological levels; the introduction of this variable in the following biprobit analysis allowed taking into account that the severity of patients’ chronic cardiac conditions may increase the probability of them suffering from anxiety and depression too, as suggested by the relevant literature

Biprobit analysis: first model

Results of the first biprobit model, investigating the probability of crossing the borderline threshold of anxiety and depression, is shown in Table 3.

Table 3 Biprobit model for borderline anxiety and depression
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The EQ-5D, MUIS and the Positive Reinterpretation and Growth scores were the regressors considered in both equations. Restraint Coping was included only in the anxiety equation because of the low and nonsignificant correlation between Restraint Coping and depression, be it over the borderline or the pathological level.

While results of the biprobit analysis mainly confirm the findings of the correlation analysis, before commenting such results, it is worth noting that in the biprobit regressions, the interpretation of the coefficients is not as straightforward as in linear regressions. This is because the increase in probability attributed to a one-unit increase in a given predictor depends both on its starting value and on the values of the other predictors. However, it can be said that a positive (negative) coefficient means that an increase (decrease) in the predictor leads to an increase (decrease) in the predicted probability.

Regarding the control variables, in line with the literature [74], age showed a positive although not significant correlation with anxiety, while it was significantly and inversely correlated with depression. This suggests that older hospitalized patients are generally less depressed than younger ones; this may be related with a higher acceptance of the deteriorated health conditions due to the illness state, as being older entails having as a reference group people with generally worse health conditions [75]. Consistent with this finding, the EQ-5D score was inversely correlated with both anxiety and depression, which may be envisaged as a reaction to a perceived deterioration in quality of life.Footnote 1 This is coherent with the evidence of the casual link offered by the relevant literature which goes from worst health conditions to anxiety and depression in chronic patients [22, 23].

Years of illness (time elapsed since the diagnosis) were inversely correlated with anxiety and, weakly, with depression: These results are consistent with the effect of the age variable. The more the years of illness, the more the patients get used to the chronic illness condition and become able to keep anxiety and depression under control during hospitalization. Moreover, this is also congruent with the extant literature claiming that the illness duration increases the knowledge of illness [76] and reduces uncertainty in illness which, as it will be shown, impacts positively on the reduction in both anxiety and depression. As regards to the crucial variables in our analysis, all the estimated coefficients were significant and confirmed the original hypothesis on uncertainty and coping.

Uncertainty was positively correlated with both the dependent variables, which were equally affected by it. This highlights how uncertainty during hospitalization negatively impacts patients’ well-being, and calls for an adequate psychological assistance in this delicate acute phase of illness. For example, behavior therapy might be helpful for people manifesting anxiety, although this issue has not been frequently investigated in hospital care [77].

The negative sign of Positive Reinterpretation and Growth is in line with the cited literature [55], according to which this coping style should be negatively correlated with both anxiety and depression, as it implies making the best of the situation and growing from it. This suggests that those patients who succeed in recognizing the positive aspects in an adverse situation report lower levels of anxiety and depression. On the other hand, Restraint Coping was positively related to anxiety. Restraint Coping is the attitude of coping passively by holding back one’s coping attempts until they can be of use; hence, the patient elaborates his/her own coping strategies and then waits before using them, although waiting for the “right moment” to put such strategies in practice may increase the probability of reporting anxiety.

The biprobit analysis also allowed to calculate the marginal effects (see Table 4), which quantifies the effects of a unitary variation in each explanatory variable on the probability of experiencing both anxiety and depression; to report neither anxiety nor depression; to report anxiety but not depression; and to report depression but not anxiety.Footnote 2 Marginal effects reinforce the considerations made above on the determinants of anxiety and depression.

Table 4 Marginal effects for borderline anxiety and depression
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Within the four probabilities that were estimated,Footnote 3 the probability of reporting both anxiety and depression was 30.2 %. Each year elapsed from the diagnosis had the effect of lowering such probability by 1 %. Experience allows patients to better control and to understand the illness experience. In line with this result, a unitary positive increase in the uncertainty score increased the probability of reporting both anxiety and depression by 1.3 %.

An increase in the EQ-5D score had the effect of lowering the probability of reporting both anxiety and depression by a marginal value of 76.2 %, while coping strategies had an impact of −2.5 % (Positive Reinterpretation and Growth) and 2.8 % (Restraint Coping).

Considering the marginal effects of all the probabilities estimated, the highest value was obtained for the no anxiety/no depression circumstance, which means that a large number of the patients to whom the questionnaire was administered, about 39 % of the people interviewed, were non-cases.

Results show that being one year older increases the probability of reporting no anxiety and depression in hospital by 0.7 %. The importance of health-related quality of life is confirmed by the evidence of an increase in the EQ-5D score, entailing a rise of 83.2 % in the joint probability of reporting no anxiety and no depression.

Each year of illness raises the joint probability of no anxiety/no depression by 10 %. A unitary reduction of the Uncertainty score lowers the joint probability of experiencing both anxiety/depression by 1.5 %, while coping strategies have an impact of 2.9 % (Positive Reinterpretation and Growth) and −1.4 % (Restraint Coping), respectively. Finally, the probabilities of reporting depression/no anxiety and anxiety/no depression were 24.2 and 6.6 %, respectively.

Biprobit analysis: second model

Table 5 shows the results of the second biprobit model which employed, as dependent variables, the probability of experiencing anxiety and depression at pathological levels.

Table 5 Biprobit model for pathological anxiety and depression
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Some explanatory variables, such as the years of illness, were not significant anymore, while Restraint Coping and Positive Reinterpretation and Growth were less significant than in first equation.

The fact that experience does not matter anymore when pathological specifications are considered suggests that when anxiety and depression are pathological, their upsurge is unlikely to represent a side effect of the hospitalization episode related to the cardiac illness, which can be better dealt with by the more experienced patients. This result is in line with the evidence that uncertainty loses significance when pathological states are considered. It might be concluded that although HADS has been designed to measure anxiety and depression during patients’ hospital stay, when these states reach pathological levels, they have to be considered a major pathology, not explained through the uncertainty related to the contingency of the inpatient episode.

This line of reasoning helps to interpret the circumstance that the statistical significance relative to the EQ-5D coefficients is confirmed for both pathological anxiety and depression: In fact, these pathological states are not reactions to the hospitalization episode, but to a perceived deterioration in quality of life, be it stable or progressive. The negative significant coefficient estimated for age, in this specification, also reinforces the evidence that the younger the patients, the more prone they are to depression.

Marginal effects of the second specification of the analysis are shown in Table 6.

Table 6 Marginal effects for pathological anxiety and depression
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The four aforementioned joint probabilities that were estimated support the interpretation that anxiety and depression at pathological levels are mainly unrelated to hospitalization. The joint probability of reporting both pathological anxiety and pathological depression was only 3.7 %, while the highest joint probability was obtained for the no pathological anxiety/no pathological depression circumstance, approximately 78 %. The probabilities of reporting pathological depression/no pathological anxiety and pathological anxiety/no pathological depression were, respectively, 1.7 and 1.2 %. While the marginal effects were consistent with the findings of the first model, they were generally weaker.

Conclusions

This study, which deals with anxiety and depression in hospitalized patients, offers a contribution to the literature on the health burden of chronic cardiac patients and the psychological consequences of their illness, widely known to impact on health-related quality of life [19].

Through controlling for the patients’ self-assessed quality of life, age and experience of illness, it investigates the impact of uncertainty in illness and coping strategies on the levels of anxiety and depression recorded during hospitalization.

Two bivariate probit models were estimated. Results showed that uncertainty in illness impacted on the probability of crossing the borderline level of both anxiety and depression but not on the probability of developing pathological anxiety and depression. Coping styles matter in the development of anxiety and depression too: A coping strategy based on Positive Reinterpretation and Growth was inversely related to both anxiety and depression, while Restraint Coping was positively related to anxiety.

The added value of the present study lies in the fact that it is the first to consider the concurrent impact of uncertainty and coping styles on the development of anxiety and depression in cardiac patients. A further novelty consists in the distinction between borderline and pathological levels of anxiety and depression. The former psychological states are contingent to the hospitalization episode and, hence, can and must be controlled in the delivery of hospital care; the latter are major pathologies and must be clinically treated as such.

A feasible approach aimed at reducing the patients’ risk of crossing the borderline threshold for anxiety and depression could consist in providing high-quality information to patients. This should be done, not only during the hospitalization period, but within a global patient care management system, encompassing a tight interaction among clinicians of different specialties: Patients should receive not only better clinical integrated treatments, but also clearer and more consistent pieces of information. In line with the extant literature results [78], a better communication strategy, between clinical staff and patients, should be encouraged to let patients manifest their doubts and boost their active role in defining and personalizing their treatment, together with the physicians.

The need of a patient-centered care approach should be also advocated in helping patients to develop a positive coping style. The risk of indulging in Restraint Coping, which negatively affects anxiety and depression, would also be reduced, while, on the other hand, a Positive Reinterpretation and Growth coping strategy, which has an impact on reducing anxiety, would be encouraged.

These considerations are in line with the objectives sketched in the recent Italian National Health Plan (Piano Sanitario Nazionale 2011–2013) which stresses the need for more effective health services planning and calls for the design of specific assistance paths through which the care of the patient is followed over time, and the various stages of patient’s transition between the various healthcare settings (hospital, long-term care structures, rehabilitation structures, etc.) are monitored [79]. The same approach is suggested at a European level too [80, 81].

The most significant limitation of the present contribution lies on the limited number of observations. For this reason, a development of the analysis should envisage the use of a larger sample of cardiac patients. In addition, the application of the same methodology could also be adopted for patients experiencing diverse chronic diseases, and in a broader perspective, it could be used to evaluate the experience of patients’ relatives and caregivers.

Another limitation of this study lies in its cross-sectional nature, which suggests caution in advocating the existence of a casual relation between uncertainty in illness/coping strategies—the main explanatory variables included in the analysis—and anxiety/depression. Rather, this research should be seen as a first step toward a longitudinal analysis that would make its conclusions more robust. The study of a patients cohort allowing the collection of repeated observations not only would give strength to the results, but also would also track the evolution of anxiety and depression during the diverse stages of the chronic disease and would allow to highlight the aspects that need to be taken into account when planning an effective assistance aimed at reducing anxiety and depression both in the short and long run. It would also be interesting to distinguish between patients who are hospitalized for the first time due to their conditions and those who have already experienced a readmission, controlling for the disease stage. Such a comprehensive analysis would allow to form a clear picture of the impact of illness on psychological states and of its indirect costs. In fact, although these costs represent the highest expenses in health care, their measurement is often neglected in economic evaluation studies on patients’ quality of life.