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Psychosomatics 41:426-432, October 2000
© 2000 The Academy of Psychosomatic Medicine

Association Between Depressive Symptoms and Mortality in Medical Inpatients

Leticia M. Furlanetto, M.D., Ph.D., Stephanie von Ammon Cavanaugh, M.D., Joao Romildo Bueno, M.D., Ph.D., Steven D. Creech, M.S., and Lynda H. Powell, Ph.D.

Received October 13, 1999; revised February 4, 2000; accepted April 27, 2000. From the Department of Internal Medicine, Federal University of Santa Catarina, Brazil; the Departments of Psychiatry and Preventive Medicine, Rush Medical College, Rush-Presbyterian St. Luke's Medical Center, Chicago, Illinois; and the Department of Psychiatry, Federal University of Rio de Janeiro, Brazil. Address reprint requests to Dr. Furlanetto, Departamento de clinica medica, Hospital Universitario, Universidade Federal de Santa Catarina, P.O. Box: 5199, Florianópolis, SC, Brazil; e-mail leticia{at}hu.usfc.br


  ABSTRACT

 
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The authors interviewed a consecutive series of medical inpatients (N=241) using the Schedule for Affective Disorders and Schizophrenia to determine which depressive symptoms are associated with in-hospital mortality. Fifteen depressive symptoms, pain, and physical discomfort were assessed along with medical comorbidity. Twenty patients died in-hospital (8.3%). Logistic regression showed that anhedonia, hopelessness, worthlessness, indecisiveness, and insomnia predicted in-hospital death after adjusting for physical comorbidity and age. Clinicians should be aware that these depressive symptoms may predict mortality in medical inpatients. Future studies should address which treatment modalities lead to better outcomes.

Key Words: Depression • Prognosis • Mortality

  INTRODUCTION

 
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Major depression and depressive symptoms have been associated with increased mortality.1–13 There is growing evidence that depression is associated with worse outcomes and death in patients with stroke and cardiovascular events.3,4,12,14–16 However, the effect of depressive symptoms on general hospital populations is not well established. Some researchers have found depression to be an independent risk factor for mortality when detected in medical patients,5,17–20 although other researchers have not.21,22

The relationship between individual depressive symptoms and mortality is not well studied. The most consistent finding was provided by two cohort studies with over 2000 participants each showing that hopelessness predicted death after adjusting for other risk factors known to predict cardiovascular disease.13,23 However, in a cohort of French community-dwelling older adults, hopelessness was not associated with increased mortality.10 Furthermore, some studies have suggested that depressed mood predicts mortality.13,18,20 Researchers are, however, reporting the sum of several symptoms that comprise depressed mood scales (e.g., sadness, hopelessness, and worthlessness), such as the Profile of Mood States, questioning as to whether the individual symptom of depressed mood alone predicts death. Community studies have shown increased mortality in older adults with concentration difficulties, appetite loss, fatigue, psychomotor retardation, and weight loss.24,25 In a study of geriatric inpatients, most of whom had dementia, the absence of psychomotor retardation was associated with shorter survival.26

To our knowledge, there have not been any studies examining individual depressive symptoms and in-hospital mortality in adult medical wards. We hypothesize that individual depressive symptoms would predict mortality in this adult population, independent of physical illness. The purpose of the current study is to determine which depressive symptoms, assessed on admission to general medical wards, were associated with death during hospitalization.


  METHODS

 
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Study Population
All consecutive admissions to the adult medical wards of a University Hospital in Florianópolis (Brazil) in a 5- month period were considered eligible to participate (N=392). The University Hospital in Florianópolis is a 250-bed teaching hospital affiliated with the Federal University of Santa Catarina in Brazil. The general medical wards have a total of 90 beds (60 for male and 30 for female patients). We excluded 151 (38.5%) patients for the following reasons: inability to complete the baseline interview because of physical illness or treatment (n=46); DSM-IV27 delirium or dementia (n=62); discharge before baseline interview (n=28); and refusal (n=4). We also excluded those patients on antidepressants (n=11) because improvement of depression or pharmacological treatment (tricyclic antidepressants) might alter the risk of death.

Procedures
An observational prospective cohort study was conducted. A psychiatrist (LMF) interviewed all subjects before their third hospital day. Data on demographic and medical variables were collected during the interview and from the patients' medical charts. This baseline interview also included the Schedule for Affective Disorders and Schizophrenia (SADS)28 and numerical rating scales of pain and physical discomfort.29,30 Mortality and length of stay during the index hospitalization were recorded. The protocol was approved by our Institutional Review Board, and informed consent was obtained.

Measurement of Depressive Symptoms
We analyzed 15 depressive symptoms that we evaluated during the SADS interview. These symptoms were depressed mood, anhedonia, hopelessness, worthlessness, excessive guilt, diminished concentration, indecisiveness, suicidal ideation, anorexia, weight loss, psychomotor retardation, agitation, insomnia, hypersomnia, and fatigue. All of these SADS items, except depressed mood and suicidal tendencies, can be rated from 1 to 6 (1=Not At All, 2=Slight; 3=Mild; 4=Moderate; 5=Severe; 6=Extreme). Depressed mood and suicidal tendencies include these ratings, but they can also be rated as 7 or Very Extreme.28 In order to make our assessment more compatible with DSM-IV27 criteria for major depressive episode, the time frame used was 2 weeks. SADS has been translated and validated in a Brazilian sample.31 The assessment of those depressive symptoms that could be affected by physical illness, treatment, or hospital environment (concentration, anorexia, weight loss, psychomotor retardation, agitation, insomnia, hypersomnia, and fatigue) was conducted in the following two ways: the inclusive approach (symptom counted whenever present); and the modified approach as proposed by Cavanaugh32 (symptom counted only if not easily explained by physical illness, treatments, or hospital environment).

Measurement of Physical Morbidity
Pain and physical discomfort not related to pain were measured using 101-point numerical rating scales (NRS- 101).29,30 These scales ranged from 0 to 100, with 0 being No Pain/Discomfort and 100 being Pain/Discomfort "so severe that patient can not stand it." Patients were asked to complete these scales according to how they felt during that day. We measured length of stay by subtracting the date of discharge or death from the date of admission.

The Charlson comorbidity index of illness33 is a weighted index that takes into account the number and the seriousness of diseases. We used the Charlson combined age-comorbidity index that also takes age into account34 to measure physical comorbidity.

Statistical Analysis
We analyzed data using SPSS for Windows (version 8.0).35 All statistical tests were two-tailed. We considered P<=0.05 as statistically significant. The primary outcome measure was death during the index hospitalization. The baseline variables of patients who died during the index hospitalization were compared with the ones of the patients who were discharged. Categorical variables were compared using chi-square test or Fisher's exact test, as appropriate; continuous variables were compared using independent t-tests or Mann-Whitney U test, as appropriate. Independent variables with skewed distributions were transformed with the natural logarithm before the logistic regression analyses. Each depressive symptom (treated as a continuous variable) was analyzed separately, after adjusting for Charlson combined age–comorbidity index using logistic regression. We submitted symptoms from this logistic regression significantly associated with mortality together in a step-wise logistic regression.


  RESULTS

 
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Characteristics of the Subjects and Survival
Table 1 describes the demographic and medical characteristics of the sample. Of all the baseline characteristics evaluated, only the Charlson combined age–comorbidity index predicted death.


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TABLE 1. Characteristics of study subjects by survival status



Twenty (8.3%) of patients died during hospitalization. Thirteen patients had a diagnosis of major depressive disorder (MDD) using the "modified" criteria for MDD described by Cavanaugh;32 of these 13 patients with MDD, 7 (53.8%) died. Thirteen (5.8%) other patients in the remaining sample died; none of these patients had a diagnosis of MDD, minor depression, or dysthymia using the criteria outlined by Cavanaugh.32

When compared with the study group, those who were excluded were older patients (65±18.4; Z=5.23; P< .01), had higher Charlson combined age-comorbidity index (5.6±2.43; P<.01), and had higher mortality rate (16.9%; {chi}2=4.98; P=0.03). However, in that same comparison, there were no differences in gender (67.5% men; {chi}2=0.41; P=0.1) or in length of stay (14±12; Z=0.43; P=0.13). There were no cases of attempted or completed suicide in either the sample or in the excluded group.

Depressive Symptoms and Mortality
In the univariate analysis (Mann-Whitney U test), 5 of the 7 cognitive-affective depressive symptoms (depressed mood, anhedonia, hopelessness, worthlessness, excessive guilt, indecisiveness, and suicidal tendencies) were significantly increased in those who had died (see Table 2). Using the inclusive approach, of the 8 vegetative symptoms (diminished concentration, anorexia, weight loss, psychomotor retardation, agitation, insomnia, hypersomnia, and fatigue) that could be affected by physical illness, treatments, or hospital environment, only insomnia predicted death (2.17±1.36, in the survivors; and 3.10±1.77 in those that died; P=0.016); the remaining inclusive symptoms were not significant. Using the modified approach, 2 of the 8 vegetative symptoms were significant (see Table 2).


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TABLE 2. Univariate analysis of depressive symptoms and in-hospital mortality2



In the logistic regression, after adjusting for Charlson combined age-comorbidity index, 4 of the 7 cognitive and affective symptoms were significantly associated with mortality (see Table 3); 2 other symptoms showed a nonsignificant trend, depressed mood [Risk Ratio (RR)=1.28, 95% confidence interval (CI)=0.94-1.74, P=0.11] and suicidal tendencies [RR=1.65, 95% CI=0.90–3.0, P=0.10]. Using the inclusive approach, of the 8 vegetative symptoms, only insomnia predicted mortality (see Table 3); the other 7 were not significant. Using the modified approach, only insomnia was significant (see Table 3); psychomotor retardation showed a nonsignificant trend (RR=1.92, 95% CI=0.83–4.40, P=0.13); the other 6 vegetative symptoms were not significant.


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TABLE 3. Depressive symptoms after adjusting for physical illness and age2



After adjusting for the Charlson combined age-comorbidity index, the 5 predictors of death in Table 3 (anhedonia, hopelessness, worthlessness, indecisiveness, and insomnia) were submitted to a forward step-wise regression. After this regression, only insomnia remained significant (RR=1.66, 95% CI=1.20–2.29, P=0.002).


  DISCUSSION

 
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
In our study—before adjusting for physical illness— anhedonia, hopelessness, worthlessness, suicidal ideation, indecisiveness, insomnia, and psychomotor retardation predicted in-hospital mortality. Insomnia, regardless of whether it was because of depression or other causes, also predicted death. However, after adjusting for physical comorbidity and age, only anhedonia, hopelessness, worthlessness, indecisiveness, and insomnia predicted mortality; suicidal ideation and psychomotor retardation showed a nonsignificant trend.

Depressed mood showed a nonsignificant trend for predicting death, both before and after adjusting for physical comorbidity. Possibly, the high frequency of depressed mood in medical inpatients and its association with the severity of physical illness may explain this finding. The comparison of our results with previous research is difficult because the studies associating depressed mood and mortality are, in fact, reporting the sum of mood scales.13,18,20

In our sample, anhedonia and hopelessness increased the risk of in-hospital death after adjusting for physical illness and age. These symptoms may be markers of more severe depressive syndromes in the medically ill,36 which might, in turn, be related to worse outcomes.9,19 This association was first described by Engel37 as the giving-in– given-up complex. The giving-in–given-up complex describes a state in which certain predisposed patients, when facing overwhelming painful experiences, become hopeless and have their capacity "to deal with potential pathogenic processes reduced," thus developing disease.37 Recently, two studies conducted with community-dwelling adults found that hopelessness predicted mortality.13,23 Anda et al.13 followed a cohort of adults with no previous history of ischemic heart disease (IHD) or serious illness and, after a mean of 12.4 years, found hopelessness in moderate and severe levels to be a predictor of nonfatal and fatal IHD, after adjusting for demographic and risk factors. Everson et al.23 identified a dose-response relationship such that men with moderate and high levels of hopelessness were at a significantly increased risk of all-cause and cause- specific mortality compared with men with low hopelessness scores. However, hopelessness was not an independent predictor of mortality in a large community study that followed older adults for 5 years.10 It is possible that the difference in the mean age of the samples explains this discrepancy, where studies conducted with older adult patients might fail to show this association. In older adult populations, the deleterious effect of depressive symptoms may be obscured by a higher expected mortality rate and multiple competing potential causs of death.20,22 An alternative explanation is "selective survival," where those more susceptible to the adverse outcomes of depression died as young adults;11,24 thus, hopelessness in the older adult population is less prognostic.

Worthlessness and indecisiveness were associated with in-hospital mortality both before and after controlling for physical comorbidity. As stated earlier, these may be markers for more severe depressive syndromes36 and worse medical outcomes.9,19 When we performed the univariate analysis, those patients who died during their hospitalization showed significantly higher scores of suicidal tendencies. However, after controlling for physical comorbidity and age, there was a nonsignificant trend for suicidal ideation to predict mortality. Because of the low frequency of suicidal ideation in the medically ill,38 it is possible that a larger sample would be required to show a significant effect of suicidal ideation on mortality.

With regard to the somatic and vegetative symptoms, only insomnia (using both inclusive and modified approaches) independently predicted in-hospital mortality. This shows that insomnia, regardless of whether it is caused by physical illness or depression, predicts mortality; in fact, after controlling for comorbidity and age and when all the symptoms are combined in one model, insomnia was the only predictor of mortality to emerge. Interestingly, patients with diminished concentration, agitation, anorexia, weight loss, hypersomnia, or fatigue did not have increased mortality, whether the inclusive or modified approach was used.

Other researchers have found different results.24–26 Diminished concentration and anorexia were associated with mortality in Fredman et al.24 Jorm et al.25 found fatigue and weight loss to be predictors of death. Shah et al.26 found that the absence of psychomotor retardation predicted mortality. This discrepancy may be explained by differences in study designs: Fredman et al.24 and Jorm et al.25 conducted their studies with community-dwelling older adults and Shah et al.26 conducted the study in continuing care geriatric inpatients, where the majority of the patients had dementia. Compared with the community samples, our sample was more ill and required hospitalization. Hospital patients have a 50%–80%36 chance of incidence of somatic symptoms due to a number of causes including depression, which may explain the different findings for vegetative symptoms in our sample. Concerning Shah et al.,26 it is possible that their results are due to the inclusion of patients with delirium in their sample. In our study, we avoided this potential problem by excluding patients with delirium and dementia. Further studies with different designs are needed to clarify this issue.

Physical illness, treatments, and the consequent functional impairment are associated with the development of depressive symptoms,19 especially in those predisposed;39 thus depression may become autonomous, causing a further decrease in function.40 Additionally, depression itself may lead to worse outcomes for several reasons. First, there may be some direct biological effect as suggested in cardiovascular data, such as exaggerated platelet aggregation and reduced heart rate variability.41–43 Second, depression- related symptoms may prevent patients from participating in their medical care, especially the ones who have already given up hope.37 Third, indecisiveness, anhedonia, worthlessness, and hopelessness may lead to behavior that discourages friends, family, and medical staff from providing necessary assistance, causing delay, or even preventing these patients from receiving optimal treatment.19 Fourth, cognitive symptoms affect how depressed patients perceive and interpret clinical information, which contributes to further stress and creates a vicious circle that increases depressive symptoms and leads to more misperceptions.20,39 Lastly, the individual symptoms may have an additional deleterious effect, because depressed inactive patients and those who have insomnia may be susceptible to unexpected complications, such as thromboembolic events41 and infections.

There are limitations to our study that deserve mention. First, our analyses were based on symptoms of patients who were able to be interviewed. For this reason, we have no information on the group that was too ill to be interviewed or those who left the hospital before the interview. Second, the outcome of mortality was only evaluated for the index hospitalization in our sample. Thus, these findings can be applied to this time period only. Perhaps a longer observation period after discharge might yield different results. Third, this sample is a relatively uneducated one; a more educated group may have a different risk of death when significantly depressed, which may limit the generalizability of our results.

In conclusion, this study indicates that clinicians need to be aware that anhedonia, hopelessness, worthlessness, indecisiveness, and insomnia appear to be the depressive symptoms that best predict mortality. Because insomnia is the best predictor of death, regardless of whether it is because of physical illness or depression, particular attention should be paid to this symptom. At this point, we have very little data about the best treatment for depressive symptoms in the medically ill, such as the effectiveness of antidepressants, benzodiazepines, and cognitive therapy. Future studies should address which treatment modalities leads to better outcomes.


  ACKNOWLEDGMENTS

 
This research was partially supported by the Ministry for Science and Technology of Brazil (CNPq).


  REFERENCES

 
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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