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Depressive Symptoms and Survival of Patients With Coronary Artery Disease

From the Behavioral Medicine Research Center and the Department of Psychiatry and Behavioral Sciences (J.C.B., B.H.B., M.J.H., I.C.S., R.B.W.) and the Department of Medicine (D.B.M.), Duke University Medical Center, Durham, North Carolina.

Address reprint requests to: John C. Barefoot, PhD, Duke University Medical Center, Box 2969, Durham, NC 27710. Email: foot{at}acpub.duke.edu

	ABSTRACT

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OBJECTIVE: Multiple studies have shown that high levels of depressive symptoms increase the mortality risk of patients with established coronary disease. This investigation divided depressive symptoms into groups to assess their relative effectiveness in predicting survival.

METHODS: Questionnaires about the presence of depressive symptoms were administered to 1250 patients with significant coronary disease while they were hospitalized for diagnostic coronary angiography. Follow-up for mortality due to cardiac disease was conducted annually for up to 19.4 years. Factor analysis was used to divide items on the Zung Self-Rating Depression Scale into four groups: Well-Being, Negative Affect, Somatic, and Appetite. In addition, responses to a single item regarding feelings of hopelessness were available for 920 patients.

RESULTS: Well-Being and Somatic symptoms significantly predicted survival (p .01). Negative Affect items were also related to survival (p = .0001) and interacted with age. A 2-SD difference in the Negative Affect term was associated with a relative risk of 1.29 for patients >50 years old and 1.70 for younger ones. Only Negative Affect remained significant in a model with the other symptom groups. Hopelessness also predicted survival with a relative risk of 1.5. Both the Hopelessness and Negative Affect items remained as independent predictors in the same model. All models controlled for severity of disease and treatment. With one exception (income and Hopelessness), results were essentially unchanged by additional controls for age, gender, and income.

CONCLUSIONS: Depressive symptoms differentially predicted survival, with depressive affect and hopelessness being particularly important. These effects were independent of disease severity and somatic symptoms and may be especially important in younger patients.

Key Words: depressive symptoms, • survival • coronary artery disease.

Abbreviations: CAD = coronary artery disease; MMPI = Minnesota Multiphasic Personality Inventory; RR = relative risk; SDS = Zung Self-Rating Depression Scale.

	INTRODUCTION

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A number of studies have demonstrated that the presence of depressive symptoms has an adverse impact on the prognosis of patients with established CAD (1–3). However, depression manifests itself in a variety of symptoms that do not necessarily occur together. The present study is an investigation of the importance of different symptoms for the prediction of survival in patients with coronary disease. There have been several suggestions that some aspects of the syndrome of depression are more important than others for patients with CAD. Appels et al. (4, 5) have argued that feelings of fatigue and demoralization, indicative of the state of vital exhaustion, are potent precursors of myocardial infarction. Others have suggested that feelings of hopelessness are particularly important, predicting coronary events while controlling for other aspects of depression (6). The relative importance of other components of depressive symptomatology has not been systematically addressed.

A related issue has to do with the importance of somatic symptoms in the relationship between depression and survival. Depression and heart disease share a number of physical symptoms, such as loss of energy, inability to perform normal activities, and sleep disturbances. Therefore, it is possible that depression scores predict outcomes in patients with CAD because they reflect manifestations of underlying coronary disease. The importance of separating physical symptoms from other aspects of depression when studying chronically ill patients has been recognized (7). Nevertheless, a number of symptom checklists used in the literature on depression in patients with coronary disease contain somatically related items. The prognostic significance of those items should be examined separately.

This study is based on further analyses of data previously reported from a study in which it was shown that scores on the SDS (8) predicted survival over an extended follow-up period (2). For the new analyses, the SDS was divided into groups of symptoms with the aid of factor analysis, and the ability of each type of symptom to predict survival while controlling for severity of disease and treatment was evaluated. A measure of hopelessness, which was available for a subsample of patients, was also examined in relation to survival.

	METHODS

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Patient Population
Patients entering Duke University Medical Center for diagnostic coronary angiography between October 1974 and February 1980 were administered a battery of psychosocial measures, including the SDS and the MMPI (9). The measures were administered after the angiographic procedure but before the results were known to patients. Patients were recruited if they were admitted for their first cardiac catheterization, were medically stable, and were able to read at least at a sixth grade level. It was not necessary to exclude patients on the basis of comorbidity because, at the time, cardiac catheterization was generally not performed on those with major comorbidity (except for cardiac risk factors such as diabetes mellitus or systemic hypertension). Only 2% of the patients approached who met the above criteria declined to participate. The 1568 patients who were found to have significant CAD (75% narrowing in diameter of at least one coronary artery) were enrolled. An additional 68 patients were excluded because data on key medical variables were missing. The SDS was not completed by 226 patients, leaving a sample of 1031 men and 219 women, who are the focus of the present investigation. The MMPI, the source of the hopelessness measure, is long and burdensome with 566 items. Not surprisingly, only 757 men and 163 women completed the hopelessness measure, which was near the end of the questionnaire. Patient characteristics are described in Table 1.

Correlates of missing data patterns in this sample have been studied extensively (10). Those who completed relatively few items on the questionnaires were characterized by low levels of education and high levels of depressive symptoms. Therefore, those excluded from analyses because of missing data are likely to be among the more depressed patients. This should serve to restrict the range of the independent variable and make it more difficult to detect an effect of depression on survival. Thus, any bias introduced by the missing data should be a conservative one.

Data collection procedures are described in more detail elsewhere (11, 12). All procedures used in this study were approved by the Duke University Medical Center Institutional Review Board.

SDS Symptom Groups
The SDS (8) has 20 items describing depressive symptoms. Respondents describe how frequently they experience each symptom on a four-point scale ranging from "a little of the time" to "most of the time." The SDS is widely used and has been shown to have satisfactory reliability and validity (13). In this sample, 11% of patients had SDS scores in the range indicative of moderate or severe depression, and another 26% had scores indicative of mild depression.

Groups of symptoms were identified with the aid of a principal components analysis with a varimax rotation. This analysis was performed on the 1933 patients who had complete data on all SDS items. This sample included some patients who were not included in the follow-up analyses because they did not meet the criterion for significant disease. A SCREE test revealed the presence of four factors, accounting for 46% of the variance. The items in each group and their loadings on the factor to which they were assigned are presented in Table 2. With one exception, the magnitudes of the loadings were high. The first group contains items describing positive experiences and feelings of Well-Being. The absence of these experiences constitutes a high score on this variable. The second contains symptoms of Negative Affect, and the third contains Somatic symptoms. The final factor contained only two items, both dealing with Appetite. Scores on each symptom group were calculated with unit weightings summed across items.

Follow-Up
Patients were contacted 6 and 12 months after their hospitalization and annually thereafter. March 10, 1994, was the end of follow-up for the present analyses. As of that date, follow-up was 97% complete with only 25 (1.6%) lost and 26 (1.7%) withdrawn from the study. Follow-up times ranged up to 19.4 years, with a median of 15.2 years for patients still living.

Deaths were classified by a mortality committee into cardiovascular and noncardiovascular categories on the basis of information provided by the patient’s physician. These procedures have been described elsewhere (14). Cardiac deaths occurred in 488 patients and there were 116 deaths from other causes. Cardiac death was the outcome for these analyses.

Analysis Strategy
It is necessary to control for CAD severity to determine whether any association between depressive symptoms and survival is due to confounding with clinical characteristics at baseline. As in previous studies (2, 12), disease severity was summarized with a "hazard score" assigned on the basis of a formula devised in analyses based on the entire population of Duke University Medical Center patients with CAD from 1969 to 1984 (15). Baseline clinical and anatomic data obtained during diagnosis were combined into the hazard score using weights derived from those analyses. Primary components of the hazard score include age, left ventricular ejection fraction, electrocardiographic abnormalities, number of vessels with >75% narrowing, and various indicators of myocardial damage (12). Hazard scores have been shown to be accurate predictors of observed survival (16) and useful as summary indices of prognostic information. In addition, treatment status (medical management vs. surgery) was included in all models as a time-dependent covariate.

Cox proportional hazards survival analyses were performed separately for each SDS symptom, each symptom group, and hopelessness. Hazard scores, income, gender, and age were evaluated as potential confounders and moderators. All significant SDS symptom groups were then combined into the same model. Finally, we fit a model that combined hopelessness with the significant effects from the SDS model.

	RESULTS

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Background Characteristics
Table 3 presents the intercorrelations of the various symptom groups and their associations with patient characteristics. With the exception of the Appetite factor, the symptoms groups were moderately interrelated. Women scored higher on the SDS than did men, a commonly observed finding. The negative associations between income and depressive symptoms were also expected. The finding of a negative association between age and affective symptoms was less expected, but it has been observed in other data sets (17; J. C. Barefoot, unpublished data). The small but significant associations of hazard scores with somatic symptoms and hopelessness suggests that disease severity is affecting physical symptomatology and that feelings of hopelessness may partially reflect the patient’s knowledge of the probable extent of their disease.

Table 4 presents the results of survival analyses for each symptom group and the entire SDS. The RRs were calculated as the increase in risk associated with a 2-SD increase in scores on the relevant scale. All variables but the Appetite items of the SDS significantly predicted survival, although there was some variation in effect sizes.

Combined Models
Further analyses fitted models designed to evaluate the independent predictive abilities of the symptom groups. The first simultaneously included the three SDS symptom groups found to be predictive in the initial models. In this model the ß value for the Well-Being variable was reduced by 60% (RR = 1.10) compared with the model in Table 4, and it became nonsignificant. The ß value for the Somatic term was also reduced substantially, by 56% (RR = 1.10), and was not significant. However, the ß value for the Negative Affect symptoms was reduced by only 26% (RR = 1.30), and it retained its statistical significance (p = .01). Thus, both the performance of the Negative Affect measure in the multivariable model and its strength when evaluated alone suggest that it is the most important component of the SDS for predicting survival.

We examined the joint effects of hopelessness and Negative Affect to evaluate their independent contributions to the prediction of survival. Only 867 patients were included in this model because data were missing for the others. Inclusion of both terms resulted in only a 28% decrease in the magnitude of ß (RR = 1.40) for hopelessness and a 19% decrease in the Negative Affect ß (RR = 1.33). Both were significant or nearly significant (p = .06 for hopelessness and p < .02 for Negative Affect). Therefore, there is evidence to suggest that hopelessness and depressive affect were independent predictors of survival.

Role of Demographic Factors
The associations of the Negative Affect symptoms with age, gender, and income (Table 3) led us to evaluate whether its ability to predict survival might be due to confounding with those characteristics and whether it might interact with those characteristics. Controls for age, gender, and income left the magnitude of the Negative Affect ß value essentially unchanged. Negative Affect did not interact with income or gender, but it did interact with age ( Figure 1). For those older than 50 years, the RR associated with a 2-SD difference in Negative Affect scores was 1.29. For younger patients it was 1.70. Thus, younger patients reported more depressive affect and it may have had more impact on them as well.

View larger version (18K): [in this window] [in a new window]   Fig. 1. Survival probability by Negative Affect scores (median split) and age. Upper pair of curves are for younger patients, and lower pair of curves are for older patients.

	DISCUSSION

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Examination of groups of depressive symptoms in patients with CAD revealed that most were predictive of survival, but some were better than others. Depressive affect and hopelessness had somewhat larger effect sizes than other types of symptoms, and they generally retained their strength in multivariable models that controlled for other symptom and demographic factors.

The impact of depressive affect was especially apparent among younger patients. Elsewhere (18) we have observed that the influence of social support on the course of depressive symptoms is more potent in younger patients. This led us to suggest that the nature and severity of depression in patients with CAD may be affected by the age at which they develop the disease. The occurrence of a coronary event may have more significant meaning in those for whom it is more unexpected and likely to result in major lifestyle changes. This is consistent with the observation that stressors may have more psychological impact if they occur at an age that is not "on time" developmentally (19). The higher level of depressive affect in younger patients and its greater prognostic importance in that group support this line of reasoning.

Hopelessness was a potent predictor of survival even though the measure was based on only one question that was endorsed by a relatively small number of patients. As in other studies (6), hopelessness seemed to have an effect over and above that of other depression measures. A question was raised by the finding that hopelessness was not significant when we controlled for income. However, the size of the ß value was not substantially reduced in that analysis. The reduced power of that model due to missing data suggests that the p value of the effect may not be a good guide to its importance. Furthermore, it is not possible to tell whether the effect of hopelessness is due to confounding with some factor associated with income or whether the effect of income is due to its association with hopelessness. Certainly there should be further investigations of the correlates (particularly income) and consequences of hopelessness in patient samples using more sophisticated measures.

In addition to identifying the potential importance of depressive affect and hopelessness, these data show that it is not necessary to include somatic symptoms to demonstrate a relationship between depressive symptoms and survival. This, coupled with controls for clinical indicators of disease by means of the hazard score, further supports the notion that depressive symptoms are not simply surrogates for underlying physical disease. Other mechanisms must be sought to explain their effects.

Many studies have demonstrated that depressed patients with coronary disease are at increased risk for mortality (1–3, 20), other coronary events (21), disability (22), and high levels of medical care utilization (23). It is now appropriate to move beyond demonstrations of these effects to a more detailed understanding of the phenomena. Investigation of potential behavioral and physiological mechanisms is in order (24). A significant part of this strategy could be the identification of those depressive symptoms that are most important and the types of patients whose prognoses are most affected by them. These findings could have treatment implications by identifying the classes of symptoms that need to be ameliorated and the types of patients most likely to benefit from interventions. The data of the present study call attention to depressive affect and hopelessness as potentially central to the phenomena and suggest that depressive affect may be especially significant in the prognosis of younger patients.

	ACKNOWLEDGMENTS

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This research was supported in part by Grants P01 HL36587, R01 HL45702, and R01 HL54780 from the National Heart, Lung, and Blood Institute; Grant R01 AG12458 from the National Institute on Aging; and Grants T32 MH19109 and R05 MH70482 from the National Institute of Mental Health.

Received for publication November 30, 1999.

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