This Article Abstract Figures Only Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Schrader, G. Articles by Guiver, N. Search for Related Content PubMed PubMed Citation Articles by Schrader, G. Articles by Guiver, N.

Predictors of Depression Three Months After Cardiac Hospitalization

From the Department of Psychiatry, University of Adelaide, South Australia (G.S.); and Health Outcomes Unit, Strategic Planning and Population Health, Department of Human Services, Adelaide, South Australia (F.C., A.-L.H., N.G.).

Address correspondence and reprint requests to Dr. Geoff Schrader, University Department of Psychiatry, The Queen Elizabeth Hospital, 28 Woodville Road Woodville, SA 5011. E-mail: geoffrey.schrader{at}adelaide.edu.au

	ABSTRACT

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 
OBJECTIVE: Depression occurs comorbidly in patients hospitalized for a range of cardiac conditions and procedures. This study examines the fluctuations in depressive symptomatology from index hospitalization to 3 months after hospitalization and determines predictors of depression 3 months after hospital admission for a cardiac condition or procedure.

METHODS: Baseline clinical and demographic variables collected from a prospective study of the natural history of depression in 833 hospitalized cardiac patients were entered into a multinomial regression analysis.

RESULTS: Similar proportions of participants were found to have no, mild, or moderate to severe depression at baseline and at 3 months, although 35.8% of participants had moved from one depression level to another during that period. Baseline characteristics predicting depression at 3 months after hospitalization were: a mild or moderate to severe level of depressive symptoms at hospitalization; younger age; smoking; self-reported previous diagnosis of a cardiac condition; and self-reported history of depression, anxiety, or stress.

CONCLUSIONS: The five clinically accessible variables identified as predictors in this study may assist physicians in identification of cardiac patients who are at risk of persistent depression and who may require active intervention. Given that depression in cardiac patients is related to increased mortality and morbidity and that it is currently poorly diagnosed, these findings may have implications for preventing adverse outcomes.

Key Words: depression, • inpatients, • coronary heart disease, • predictors, • natural history, • multinomial regression model.

Abbreviations: CABG = coronary artery bypass graft;; CES-D = Center for Epidemiological Studies – Depression Scale;; CHF = congestive heart failure;; CI = confidence interval;; IDACC = Identifying Depression as a Comorbid Condition;; MI = myocardial infarction;; OR = odds ratio.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 
Depression occurs comorbidly in patients hospitalized for a range of cardiac conditions and procedures. Most commonly, research has focused on the relationship between depression and myocardial infarction (MI) (1); however, similar high prevalence rates of depression have been reported in patients experiencing unstable angina (2) and congestive heart failure (CHF) (3, 4) and in patients undergoing cardiac procedures such as coronary artery bypass graft (CABG) (5,6) and angioplasty (7). Longitudinal studies have reported that symptoms of depression adversely affect severity of cardiac disease (8) and increase the risk of future mortality (9), chest pain, and greater use of hospital and primary care services (10,11). Mortality has been found to be twice as common and rehospitalization three times as likely in depressed CHF patients at 12 months (4), and depressed CABG patients have been reported to experience twice as many cardiac events in the 12 months after surgery (6). Evidence for differing outcomes for cardiac patients experiencing minor vs. major depression has also been reported (12). The pathways to such negative outcomes for depressed cardiac patients are unclear. Both biological (13) and psychological explanations (14,15) have been advanced, including serotonin-mediated platelet disturbances, sympathetic nervous system dysfunction, poor treatment compliance, and low motivation to change smoking habits or lifestyle. Findings from two recent studies designed to determine the effect of identifying and treating depressed patients after MI, the ENRICHD study (16,17) and the SADHART study (18), have not provided clear evidence that either psychological or pharmacological interventions have a clinically meaningful effect on either depressive symptoms or cardiac event rates (19). It has been argued that the disappointing findings from these studies may be partially the result of the high rates of spontaneous remission that occur in patients with post-MI depression (18). Therefore, an important direction for future research is the early identification of patients who have more persistent depression and may, if left without active treatment for depression, be at increased risk of adverse health outcomes (20). To date, few predictors of persistent depression in cardiac patients have been identified, apart from a past history of depression (21).

The Identifying Depression as a Comorbid Condition (IDACC) study (22) prospectively examines depression in patients with a range of cardiac conditions. IDACC comprises a cohort of 1444 cardiac patients aged 18 to 84 years and admitted to the cardiology units of four major public hospitals in Adelaide, South Australia, for MI, unstable angina, arrhythmia, CHF, CABG, or angioplasty over a 16-month period ending in December 2001. The cohort was followed for 12 months, examining the course of depression and associated quality of life and service utilization. The subgroup of patients classified as depressed at baseline were randomly allocated into a controlled trial evaluating the effectiveness of an early intervention involving targeted psychiatric management advice to general practitioners. This paper examines fluctuation in levels of depression in a cohort of IDACC patients from hospitalization to 3 months after discharge. Using multinomial regression, we report the ability of a series of demographic and clinical variables captured during hospitalization to predict mild and moderate to severe depression at 3 months after hospitalization.

	METHODS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 
Of the 2896 patients assessed for eligibility, 981 were excluded because of inability to complete the questionnaires in English (41%), severe cognitive or physical impairment (43%), or participation in other trials (14%). Of the 1915 approached, 471 refused to participate, resulting in a cohort of 1444 patients, which represents a 75% consent rate. At 3 months after hospitalization, 36 patients had died. Of the remaining 1408 patients, 1135 patients (78.6%) returned a postal follow-up questionnaire. Patients who did not return the questionnaire were more likely to be depressed at baseline, younger, smoke, and either divorced or separated (p <.001). Participants who failed to complete all required sections of the questionnaires (N = 65) were excluded. A further 237 participants randomized to the intervention arm of the trial were then excluded so that treatment effects would not influence the prediction of depression. The current article relates to the remaining 833 patients.

Depression was measured using the self-administered CES-D (23), completed initially on day 2 or 3 of the hospital admission (baseline) and then repeated at 3, 6, and 12 months after discharge by postal questionnaire. The Center for Epidemiological Studies-Depression Scale (CES-D) is a 20-item self-report scale that measures depressive symptomatology. It has good face validity and specificity (24,25), is easy to administer, and has been widely used in cardiac patients (12) and older populations (24). Responses are made on a 4-point scale, and possible scores range from 0 to 60, with higher scores indicating higher levels of depressive symptoms. Consistent with previous research (24,26–31), participants in the IDACC study were categorized as having no depression (CES-D <16), mild depression (CES-D 16 to 26), or moderate to severe depression (CES-D >26). Predictor variables, chosen with regard to evidence from previous research on comorbid depression, included demographic details (eg, marital status, employment, education) and cardiac and depression risk factors (eg, self-reported smoking status, medical history, psychiatric history) from the baseline questionnaire administered during hospitalization, as well as information from hospital administrative records on the index admission and other hospital admissions in the previous 2 years.

Statistical Analyses
Statistical analyses were conducted using STATA (version 7.0) (32) and SPSS for Windows (version 11.0) (33). All statistical tests were two-tailed, and p < .05 defined statistical significance. For some categorical variables, missing responses were combined with "Not sure" responses into a "Not sure/Not stated" category, in order to avoid listwise deletion of cases in the multinomial analysis. Due to the necessary exclusion of the intervention group, control group responses were weighted to represent both depressed groups (control and intervention), with low scoring responses allocated a weight of unity. All weights were adjusted to sum to the sample size of analysis (N = 833) to avoid distortion of significance tests. Initial univariate exploration of the relationships between 3-month CES-D categories and the 20 baseline variables was conducted using Pearson chi-squared analyses (examining adjusted standardized residuals). Exploratory logistic regression analyses showed that different variables contributed to the prediction of mild or moderate to severe depression; therefore, multinomial analyses were chosen (34,35).

To examine the best independent predictors of 3-month mild or moderate to severe depression, the twenty baseline variables were initially tested in univariate multinomial regression models, with the ² statistic employed to examine the OR. All variables fitted Hosmer and Lemeshow’s (34) criteria of significance at p < .25 and were therefore entered in the multivariate multinomial (polytomous) regression analyses. Accordingly, the initial regression model incorporated dummy variables and included all baseline demographic variables, cardiac and depression risk factors, and baseline depression as possible predictors. Using backwards elimination and stepwise regression techniques, variables with high standard errors and those with the largest p values were then individually removed with the model assessed (using the G statistic for difference between the log likelihood ratio tests) at each step for significant change. Initially, significance at p .1 was used as the removal criterion; however, significance was reduced to the conventional level of p .05 when no further variables could be removed at the higher level. As recommended by Hosmer and Lemeshow (34), individual logistic regression diagnostic plots (residual, deviance, leverage, and Cook SD) were assessed to determine the fit of the multinomial model.

	RESULTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 
Weighted results showed high correlations between baseline and three month CES-D scores (r = 0.56, p < .001). Moreover, at baseline and 3 months, respectively, a similar proportion of participants had no (58.5%, 57.8%), mild (23.9%, 24.8%), or moderate to severe (17.5%, 17.4%) depression. However, these results conceal the considerable fluctuation in scores, as a high proportion of individuals (35.8%) moved across the depression categories over the time period, as illustrated by Figure 1.

View larger version (71K): [in this window] [in a new window]   Figure 1. Proportional change in level of depression from baseline to 3 months (weighted)

Multinomial Regression Model
Twenty variables were originally tested as predictors of mild or moderate to severe depression at 3 months post-index hospitalization; of these, five were found to contribute significantly to the final model, as shown in Table 2. Moderate to severe depression at baseline was the best predictor of both moderate to severe depression (OR 51.3) and mild depression (OR 9.6) at 3 months. However, mild depression at baseline also contributed to the model for moderate to severe depression (OR 3.3) and mild depression (OR 2.8) at 3 months. Patients reporting a previous diagnosis of depression, anxiety, or stress were at particularly high risk of moderate to severe depression (OR 5.9), but also of mild depression (OR 3.6), at 3 months when compared with patients who reported no previous diagnosis. Those who were unsure or did not state whether they had a previous diagnosis for these conditions were also at an increased risk of moderate to severe depression (OR 5.9). Compared with nonsmokers, current smokers were at a significantly higher risk of moderate to severe or mild depression at 3 months (OR 3.6 and 1.9, respectively). Patients who reported a previously diagnosed cardiac condition were more likely to experience moderate to severe or mild depression at 3 months (OR 2.0 and 2.3, respectively) compared with those with no previous cardiac diagnosis. Those who were unsure or did not state whether they had a previous cardiac condition were at greater risk of moderate to severe depression (OR 3.2) at 3 months. The only demographic variable to contribute to the model was the patient’s age; those in the 65 to 74 year age group were significantly less likely to have moderate to severe depression at 3 months (OR 0.27) compared with people younger than 55 years.

	DISCUSSION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 
Findings from recent intervention studies of depressed cardiac patients have underlined the need to identify cardiac patients whose depression persists (16,18–20) and who may be at risk of adverse health outcomes. Data from the IDACC study provide an opportunity to examine the natural history of depression in cardiac patients and to identify factors that increase the risk of persistent depression. Our results illustrate considerable fluidity in the level of depressive symptoms during the first 3 months after hospitalization. Although a similar proportion of patients experienced depression at baseline and 3 months (approximately 40%), more than one third of patients experienced a change in level of depression over that time period. Most movement occurred in the group with mild depression at baseline; nearly half were no longer depressed at 3 months, and 16% moved to the moderate to severe level. In contrast, only 10% of those with moderate to severe depression at baseline were no longer depressed at 3 months; 60% remained moderately to severely depressed, and the remainder (30%) moved to the mild level. Moderate to severe depression had emerged in 40% of patients who had experienced only mild depression or no depression at baseline. These findings highlight difficulties in reliably identifying those in whom depression will emerge, persist, or worsen only on the basis of the level of depressive symptoms during hospitalization. Therefore mass screening at the time of hospitalization using a measure of depression severity alone may not be an efficacious method of identifying cardiac patients at risk of depression-related adverse outcomes.

Overall, our multinomial regression model predicted the depression level of 71% of our patient group at 3 months. Although predicting those who were not depressed or moderately to severely depressed at 3 months could be made with some confidence (93% and 60%, respectively), the determinants of mild depression at 3 months were less clear, with only 28% of cases correctly predicted by our model. The outcome for patients with moderate to severe depression at 3 months who had experienced only mild depression or no depression at baseline was also more difficult to predict from baseline characteristics. The 6-month and 12-month assessments undertaken as part of the IDACC study will provide further information on the course of depression in these patients, and it may well shed further light on risk factors emerging after the initial cardiac recovery period.

The strongest predictor of depression at 3 months (both mild and moderate to severe) was having moderate to severe depression at baseline; four other baseline characteristics made significant contributions to the model’s predictive ability: age; smoking status; self-reported previous diagnosis of depression, anxiety, or stress; and self-reported previous diagnosis of a cardiac condition. It is noteworthy that these four variables are routinely sought during patient history taking and therefore are readily accessible to clinicians. Screening hospitalized cardiac patients using this short checklist of risk factors in conjunction with a measure of depression severity may be a more efficacious method of identifying those at risk of persistent depression than mass screening using a measure of depression severity alone. Targeting this group for close monitoring and management of depression may provide most benefits to patients and may represent a cost-efficient means of preventing adverse outcomes associated with depression in cardiac patients.

Most of the risk factors identified by the IDACC study have individually been associated with depression in other studies, but they have not been reported as a group to our knowledge. The finding that a history of depression, anxiety, or stress was a strong predictor of the level of depression at 3 months is consistent with previous research. Lesperance et al. (21) found that people with a history of previous major depression were more likely to be depressed after infarction both in hospital and after discharge. They argued that most efforts in settings with limited psychiatric resources should be directed toward post-MI patients with a history of depression. Furthermore, Glassman et al. (18) reported in the SADHART study that sertraline was found to be robustly superior to placebo only in patients who had a history of at least two prior episodes of depression. The association between smoking and an increased risk of depression at 3 months is consistent with evidence of increased risk of major depression being associated with smoking (37,38). The finding that younger patients were more likely to be depressed at 3 months is consistent with Lesperance et al. (21). However, in their study this association between age and depression did not reach significance, perhaps attributable to a small sample size. Our finding that self-reported previous diagnosis of a cardiac condition was a predictor of depression at 3 months after hospitalization warrants further exploration with data from the 6-month and 12-month IDACC evaluations.

The finding that ORs were generally higher for moderate to severe depression than for mild depression may be related to the criteria used for allocating patients to these categories. The cut-off score used as the criterion for moderate to severe depression in the IDACC study has high sensitivity and specificity for detecting major depression (29). In contrast, our mild depression criterion has lower specificity for depression (29), leading to a more heterogeneous group and hence limiting the strength of any associations with risk factors for depression at 3 months. Of note, Schleifer et al. (39) reported in a follow-up study that cardiac patients meeting diagnostic criteria for major depression during hospitalization were more likely to remain depressed at 3 months than those patients who met criteria for minor depression at hospitalization.

The authors acknowledge some limitations in the generalizability of the sample. Private hospital patients and those patients unable to complete the questionnaires (due to language differences or severity of illness) were excluded, patients refusing to participate tended to be older and female, and those depressed at baseline were less likely to return the 3-month questionnaire, a finding consistent with previous research (21). However, high rates of initial consent (75.0%) and return of 3-month questionnaires (78.6%) were achieved. Of note, the prevalence of depression in the IDACC cohort was similar to that reported previously among hospitalized cardiac patients (1–7,12), with the proportion of patients with moderate to severe depression in this study similar to the proportion of cardiac patients generally identified as having major depression (40,41). The decision to include a number of different types of cardiac conditions in the current study was made on the basis of increasing evidence that depression is associated with a range of cardiac conditions (1,2,6,7). In our study, neither the admission reason nor discharge diagnosis was related to depression level at baseline, nor did these contribute to the prediction of depression at 3 months. A further limitation is the number of participants who failed to respond to one or more questions. In the multinomial regression, our treatment of the missing data produced significant ORs for predicting moderate to severe depression at 3 months for those in the "Not sure/Not stated" category for two variables: self-reported previous diagnosis of depression, anxiety, or stress; self-reported previous diagnosis of a cardiac condition. These results are difficult to interpret, and they should be viewed with caution due to the relatively small number of cases (N=15 and 22, respectively).

While depression in cardiac patients is well documented in the literature, studies have reported a low level of identification by physicians and very low levels of psychiatric intervention for this group (36,39,42). This limited recognition may be partially a result of the complexity of the task confronting clinicians when assessing the psychological state of cardiac patients. Not only must the clinician recognize symptoms of depression, they must also decide whether the depression is likely to be transient or more persistent and, perhaps, require specific treatment. Physicians may be assisted in this difficult assessment task by considering the five clinically accessible predictors identified by the IDACC study: level of depression at hospitalization; younger age; smoking; history of depression, anxiety or stress; and previous diagnosis of a cardiac condition. Accurate and timely identification of depression in cardiac patients has the potential to significantly reduce suffering and adverse health outcomes in this substantial segment of the population.

	ACKNOWLEDGMENTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 
The IDACC project was funded by the South Australian Department of Human Services, partly through grants provided by the Australian National Mental Health Strategy.

The authors gratefully acknowledge the cooperation and support from the Heads of Cardiology Departments (Dr. Leo Maher, Associate Professor, Philip Aylward, and Professor John Horowitz) and Heads of Psychiatry (Professors Robert Barrett, Ross Kalucy, and Sandy McFarlane) at the recruitment sites, and John Knight and Rob Baker of the FMC Cardiothoracic Unit. We are most appreciative of the generous time and professionalism of Lorraine Rayson for data management; Julie Marker, Bronwyn van Heusden, Alicia Gordon, Heather Banham, Marilyn Kingston, and Ruth Kerr for recruitment; the Heads of Psychiatric Consultation Liaison Services, psychiatric registrars, medical, mental health, and cardiac nursing staff for their role in implementing the trial; members of the IDACC Advisory Group for advice and assistance in developing the study methods and protocols; and, each of the patient participants. Additionally, we extend our thanks to Graeme Tucker and Sharon Fielder for their work and comment on the statistical analysis.

Received for publication October 13, 2003.

	REFERENCES

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION ACKNOWLEDGMENTS REFERENCES

 

1. Frasure-Smith N, Lesperance F, Talajic M. Depression following myocardial infarction. Impact on 6-month survival. JAMA 1993; 270: 1819–25.[Abstract]
2. Lesperance F, Frasure-Smith N, Juneau M, Theroux P. Depression and 1-year prognosis in unstable angina. Arch Intern Med 2000; 160: 1354–60.[Abstract/Free Full Text]
3. Friedman MM, Griffin JA. Relationship of physical symptoms and physical functioning to depression in patients with heart failure. Heart Lung 2001; 30: 98–104.[CrossRef][Medline]
4. Jiang W, Alexander J, Christopher E, Kuchibhatla M, Gaulden LH, Cuffe MS, Blazing MA, Davenport C, Califf RM, Krishnan RR, O’Connor CM. Relationship of depression to increased risk of mortality and rehospitalization in patients with congestive heart failure. Arch Intern Med 2001; 161: 1849–56.[Abstract/Free Full Text]
5. McKhann GM, Borowicz LM, Goldsborough MA, Enger C, Selnes OA. Depression and cognitive decline after coronary artery bypass grafting. Lancet 1997; 349: 1282–4.[CrossRef][Medline]
6. Connerney I, Shapiro PA, McLaughlin JS, Bagiella E, Sloan RP. Relation between depression after coronary artery bypass surgery and 12-month outcome: a prospective study. Lancet 2001; 358: 1766–71.[CrossRef][Medline]
7. Barefoot JC, Brummett BH, Helms MJ, Mark DB, Siegler IC, Williams RB. Depressive symptoms and survival of patients with coronary artery disease. Psychosom Med 2000; 62: 790–5.[Abstract/Free Full Text]
8. Levine JB, Covino NA, Slack WV, Safran C, Safran DB, Boro JE, Davis RB, Buchanan GM, Gervino EV. Psychological predictors of subsequent medical care among patients hospitalized with cardiac disease. J Cardiopulm Rehabil 1996; 16: 109–16.[CrossRef][Medline]
9. Frasure-Smith N, Lesperance F, Talajic M. Depression and 18-month prognosis after myocardial infarction. Circulation 1995; 91: 999–1005.[Abstract/Free Full Text]
10. Frasure-Smith N, Lesperance F, Gravel G, Masson A, Juneau M, Talajic M, Bourassa MG. Depression and health-care costs during the first year following myocardial infarction. J Psychosom Res 2000; 48: 471–8.[CrossRef][Medline]
11. Mayou R, Gill D, Thompson D, Day A, Hicks N, Volmink J, Neil A. Depression and anxiety as predictors of outcome after myocardial infarction. Psychosom Med 2000; 62: 212–9.[Abstract/Free Full Text]
12. Penninx BW, Beekman AT, Honig A, Deeg DJ, Schoevers RA, van Eijk JT, van Tilburg W. Depression and cardiac mortality: results from a community-based longitudinal study. Arch Gen Psychiatry 2001; 58: 221–7.[Abstract/Free Full Text]
13. Sauer WH, Berlin JA, Kimmel SE. Selective serotonin reuptake inhibitors and myocardial infarction. Circulation 2001; 104: 1894–8.[Abstract/Free Full Text]
14. DiMatteo MR, Lepper HS, Croghan TW. Depression is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression on patient adherence. Arch Intern Med 2000; 160: 2101–7.[Abstract/Free Full Text]
15. Lane D, Carroll D, Ring C, Beevers DG, Lip GY. Predictors of attendance at cardiac rehabilitation after myocardial infarction. J Psychosom Res 2001; 51: 497–501.[CrossRef][Medline]
16. The ENRICHD Investigators. Enhancing Recovery in Coronary Heart Disease (ENRICHD) study intervention: rationale and design. Psychosom Med 2001; 63: 747–55.[Abstract/Free Full Text]
17. Writing Committee for the ENRICHD Investigators. Effects of treating depression and low perceived social support on clinical events after myocardial infarction: The Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) Randomized Trial. JAMA 2003; 289: 3106–16.[Abstract/Free Full Text]
18. Glassman AH, O’Connor CM, Califf RM, Swedberg K, Schwartz P, Bigger JT, Jr., Krishnan KR, van Zyl LT, Swenson JR, Finkel MS, Landau C, Shapiro PA, Pepine CJ, Mardekian J, Harrison WM. Sertraline treatment of major depression in patients with acute MI or unstable angina. JAMA 2002; 288: 701–9.[Abstract/Free Full Text]
19. Sheps DS, Freedland KE, Golden RN, McMahon RP. ENRICHD and SADHART: Implications for Future Biobehavioral Intervention Efforts. Psychosom Med 2003; 65: 1–2.[Free Full Text]
20. Carney RM, Jaffe AS. Treatment of depression following acute myocardial infarction. JAMA 2002; 288: 750–1.[Free Full Text]
21. Lesperance F, Frasure-Smith N, Talajic M. Major depression before and after myocardial infarction: its nature and consequences. Psychosom Med 1996; 58: 99–110.[Abstract/Free Full Text]
22. Cheok F, Schrader G, Banham D, Marker J, Hordacre A-L. Identification, course and treatment of depression following admission for a cardiac condition: Rationale and patient characteristics for the IDACC (Identifying Depression as a Comorbid Condition) project. Am Heart J 2003; 146: 978–84.[CrossRef][Medline]
23. Radloff L. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Meas 1977; 1: 385–401.[CrossRef]
24. Zich JM, Attkisson CC, Greenfield TK. Screening for depression in primary care clinics: the CES-D and the BDI. Int J Psychiatry Med 1990; 20: 259–77.[Medline]
25. Strik JJ, Honig A, Lousberg R, Denollet J. Sensitivity and specificity of observer and self-report questionnaires in major and minor depression following myocardial infarction. Psychosomatics 2001; 42: 423–8.[Abstract/Free Full Text]
26. Ensel W. Measuring depression: The CES-D Scale. In: Lin N, Dean A, Ensel WM, editors. Social support, life events and depression. New York, Academic Press; 1986.
27. Logsdon MC, McBride AB, Birkimer JC. Social support and postpartum depression. Res Nurs Health 1994; 17: 449–57.[Medline]
28. Klinkman MS, Coyne JC, Gallo S, Schwenk TL. Can case-finding instruments be used to improve physician detection of depression in primary care? Arch Fam Med 1997; 6: 567–73.[Abstract]
29. Geisser ME, Roth RS, Robinson ME. Assessing depression among persons with chronic pain using the Center for Epidemiological Studies-Depression Scale and the Beck Depression Inventory: a comparative analysis. Clin J Pain 1997; 13: 163–70.[CrossRef][Medline]
30. Brummett BH, Babyak MA, Barefoot JC, Bosworth HB, Clapp-Channing NE, Siegler IC, Williams RB Jr., Mark DB. Social support and hostility as predictors of depressive symptoms in cardiac patients one month after hospitalization: a prospective study. Psychosom Med 1998; 60: 707–13.[Abstract/Free Full Text]
31. Blumenthal JA, Lett HS, Babyak MA, White W, Smith PK, Mark DB, Jones R, Mathew JP, Newman MF, for the NORG investigators. Depression as a risk factor for mortality after coronary artery bypass surgery. Lancet 2003; 362: 604–9.[CrossRef][Medline]
32. Stata for Windows. College Station, TX: Stata Corporation; 2002.
33. SPSS for Windows. Chicago: SPSS Inc.; 2001.
34. Hosmer DW, Lemeshow S. Applied logistic regression. New York: John Wiley & Sons, Inc; 2000.
35. Ananth CV, Kleinbaum DG. Regression models for ordinal responses: A review of methods and applications. Int J Epidemiol 1997; 26: 1323–33.[Abstract/Free Full Text]
36. World Health Organization. Composite International Diagnostic Interview, Version 2.1. Geneva: WHO; 1997.
37. Ziegelstein RC, Fauerbach JA, Stevens SS, Romanelli J, Richter DP, Bush DE. Patients with depression are less likely to follow recommendations to reduce cardiac risk during recovery from a myocardial infarction. Arch Intern Med 2000; 160: 1818–23.[Abstract/Free Full Text]
38. Lindeman S, Hamalainen J, Isometsa E, Kaprio J, Poikolainen K, Heikkinen M, Aro H. The 12-month prevalence and risk factors for major depressive episode in Finland: Representative sample of 5993 adults. Acta Psychiatr Scand 2000; 102: 178–84.[CrossRef][Medline]
39. Schleifer S, Marcari-Hinson M, Coyle D, Slater W, Kahn M, Gorlin R, Zucker H. The nature and course of depression following myocardial infarction. Arch Intern Med 1989; 149: 1785–9.[Abstract]
40. Carney RM, Freedland KE, Sheline YI, Weiss ES. Depression and coronary heart disease: A review for cardiologists. Clin Cardiol 1997; 20: 196–200.[Medline]
41. Freedland KE, Rich MW, Skala JA, Carney RM, Davila-Roman VG, Jaffe AS. Prevalence of depression in hospitalized patients with congestive heart failure. Psychosom Med 2003; 65: 119–28.[Abstract/Free Full Text]
42. Chew-Graham CA, Hogg T. Patients with chronic physical illness and co-existing psychological comorbidity: GPs’ views on their role in detection and management. Prim Care Psychiatry 2002; 8: 35–9.

This article has been cited by other articles:

				W. Linden, M. J. Phillips, and J. Leclerc Psychological treatment of cardiac patients: a meta-analysis Eur. Heart J., December 2, 2007; 28(24): 2972 - 2984. [Abstract] [Full Text] [PDF]

				P. J. Lustman, R. E. Clouse, B. D. Nix, K. E. Freedland, E. H. Rubin, J. B. McGill, M. M. Williams, A. J. Gelenberg, P. S. Ciechanowski, and I. B. Hirsch Sertraline for Prevention of Depression Recurrence in Diabetes Mellitus: A Randomized, Double-blind, Placebo-Controlled Trial. Arch Gen Psychiatry, May 1, 2006; 63(5): 521 - 529. [Abstract] [Full Text] [PDF]

				L. E. Boulware, Y. Liu, N. E. Fink, J. Coresh, D. E. Ford, M. J. Klag, and N. R. Powe Temporal Relation among Depression Symptoms, Cardiovascular Disease Events, and Mortality in End-Stage Renal Disease: Contribution of Reverse Causality Clin. J. Am. Soc. Nephrol., May 1, 2006; 1(3): 496 - 504. [Abstract] [Full Text] [PDF]

This Article Abstract Figures Only Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Schrader, G. Articles by Guiver, N. Search for Related Content PubMed PubMed Citation Articles by Schrader, G. Articles by Guiver, N.