Comorbid Depression and Heart Failure: A Community Cohort Study

Abstract

Objective:
To examine the association between depression and clinical outcomes in heart failure (HF) in a community cohort.

Patients and methods:
HF patients in Minnesota, United States completed depression screening using the 9-item Patient Health Questionnaire (PHQ-9) between 1st Oct 2007 and 1st Dec 2011; patients with PHQ-9≥5 were labelled "depressed". We calculated the risk of death and first hospitalization within 2 years using Cox regression. Results were adjusted for 10 commonly used prognostic factors (age, sex, systolic blood pressure, estimated glomerular filtration rate, serum sodium, ejection fraction, blood urea nitrogen, brain natriuretic peptide, presence of diabetes and ischaemic aetiology). Area under the curve (AUC), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) compared depression as a predictor against the aforementioned factors.

Results:
425 patients (mean age 74, 57.6% males) were included in the study; 179 (42.1%) had PHQ-9≥5. The adjusted hazard ratio of death was 2.02 (95% CI 1.34-3.04) and of hospitalization was 1.42 (95% CI 1.13-1.80) for those with compared to those without depression. Adding depression to the models did not appreciably change the AUC but led to statistically significant improvements in both the IDI (p = 0.001 and p = 0.005 for death and hospitalization, respectively) and NRI (for death and hospitalization, 35% (p = 0.002) and 27% (p = 0.007) were reclassified correctly, respectively).

Conclusion:
Depression is frequent among community patients with HF and associated with increased risk of hospitalizations and death. Risk prediction for death and hospitalizations in HF patients can be improved by considering depression.

Full text

RESEARCH ARTICLE
Comorbid Depression and Heart Failure: A
Community Cohort Study
Bhautesh Dinesh Jani
1
, Frances S. Mair
1
, Véronique L. Roger
2,3
, Susan A. Weston
2
,
Ruoxiang Jiang
2
, Alanna M. Chamberlain
2
*
1General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow,
United Kingdom, 2Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United
States of America, 3Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, United States
of America
*chamberlain.alanna@mayo.edu
Abstract
Objective
To examine the association between depression and clinical outcomes in heart failure (HF)
in a community cohort.
Patients and Methods
HF patients in Minnesota, United States completed depression screening using the 9-item
Patient Health Questionnaire (PHQ-9) between 1
st
Oct 2007 and 1
st
Dec 2011; patients
with PHQ-95 were labelled “depressed”. We calculated the risk of death and first hospitali-
zation within 2 years using Cox regression. Results were adjusted for 10 commonly used
prognostic factors (age, sex, systolic blood pressure, estimated glomerular filtration rate,
serum sodium, ejection fraction, blood urea nitrogen, brain natriuretic peptide, presence of
diabetes and ischaemic aetiology). Area under the curve (AUC), integrated discrimination
improvement (IDI) and net reclassification improvement (NRI) compared depression as a
predictor against the aforementioned factors.
Results
425 patients (mean age 74, 57.6% males) were included in the study; 179 (42.1%) had
PHQ-95. The adjusted hazard ratio of death was 2.02 (95% CI 1.34–3.04) and of hospitali-
zation was 1.42 (95% CI 1.13–1.80) for those with compared to those without depression.
Adding depression to the models did not appreciably change the AUC but led to statistically
significant improvements in both the IDI (p = 0.001 and p = 0.005 for death and hospitaliza-
tion, respectively) and NRI (for death and hospitalization, 35% (p = 0.002) and 27% (p =
0.007) were reclassified correctly, respectively).
Conclusion
Depression is frequent among community patients with HF and associated with increased
risk of hospitalizations and death. Risk prediction for death and hospitalizations in HF
patients can be improved by considering depression.
PLOS ONE | DOI:10.1371/journal.pone.0158570 June 30, 2016 1/11
a11111
OPEN ACCESS
Citation: Jani BD, Mair FS, Roger VL, Weston SA,
Jiang R, Chamberlain AM (2016) Comorbid
Depression and Heart Failure: A Community Cohort
Study. PLoS ONE 11(6): e0158570. doi:10.1371/
journal.pone.0158570
Editor: Toru Hosoda, Tokai University, JAPAN
Received: December 9, 2015
Accepted: June 19, 2016
Published: June 30, 2016
Copyright: © 2016 Jani et al. This is an open access
article distributed under the terms of the Creative
Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: The de-identified
dataset can be found in the Supporting Information
files.
Funding: The authors have no support or funding to
report.
Competing Interests: The authors have declared
that no competing interests exist.
Abbreviations: HF, Heart Failure; PHQ-9, Patient
Health Questionnaire; AUC, Area Under Curve; NRI,
Net Reclassification Improvement; IDI, Integrated
Discrimination Improvement; HR, Hazard Ratio; CI,
Confidence Intervals.

Introduction
Heart failure (HF) is a major health problem with high rates of mortality and hospitalization
reported across Europe and North America [1–3]. Accurate prediction of prognosis in chronic
HF patients is important for decision making and helps identify patients at risk who may bene-
fit from closer monitoring [4,5]. Various risk prediction models have been proposed to predict
mortality and hospitalization in HF [6–11]. A recently published systematic review by Ouwer-
kerk and colleagues has identified 11 of the most commonly used prognostic markers in the lit-
erature for risk prediction of chronic HF outcomes [6]. However, there are a number of
drawbacks with currently available prognostic models, such as limited accuracy and scarcity of
data available on predicting hospitalisation; hence, better prognostic markers are required for
HF patients [7–10].
Depression has been found to be an independent predictor of mortality and hospitalization
in HF [12–16]. However, the clinical utility of depression as a prognostic marker for HF out-
comes has not been examined in comparison with some of the commonly used HF prognostic
markers. Thus, the objective of this study was to examine if the presence of co-morbid depres-
sion provided incremental prognostic information for 2-year mortality and hospitalization risk
prediction over the most commonly used prognostic markers in HF[6].
Methods
Study Setting
This was a prospective cohort study conducted in southeast Minnesota (with an approximate
population of 185,000) [17] in the United States using the Rochester Epidemiology Project
[18–20], a record linkage system which allows near complete capture of health care utilization
for area residents. This is possible because the area is relatively isolated from other urban cen-
ters and has a small number of medical providers, including Mayo Clinic and Olmsted Medical
Center, which deliver nearly all health care to local residents. This study was approved by the
Mayo Clinic and Olmsted Medical Center Institutional Review Boards.
Study Sample
Patients with either incident or prevalent HF were identified during an inpatient or outpatient
visit using natural language processing of the electronic medical record. The diagnoses were
manually validated by trained abstractors using the Framingham criteria, which has been
described previously [14]. HF patients aged 18 years who resided in Olmsted, Dodge or Fill-
more Counties in Minnesota were prospectively recruited between October 2007 and Decem-
ber 2011 and asked to complete a 9-item Patient Health Questionnaire (PHQ-9) [21] for
depression. Written informed consent was obtained from all participants.
Depression Assessment
Depression symptoms were assessed using the PHQ-9 administered by a registered nurse in
person, within 6 weeks of enrolment. A PHQ-9 score of 5 or more denotes mild depression,
while a score of 10 or more is indicative of major depressive disorder [21]. Hence, a score of
PHQ-9 5 was used to define “presence of depressive symptoms”, while a score of PHQ-910
was used to define “presence of moderate to severe depression”. All clinical variables were
either obtained electronically or from patient records.
Depression and Heart Failure Prognosis
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Measurement of Clinical Variables
Systolic blood pressure in mm Hg was obtained within 30 days of recording PHQ-9. Estimated
glomerular filtration rate (eGFR) was estimated using the closest serum creatinine value within
1 year of administering PHQ-9 using the Modification of Diet in Renal Disease Study equation
[22]. Left ventricular ejection fraction (EF) measured in % was obtained using the closest value
from an echocardiogram within 6 months prior to 2 months after the patient’s diagnosis of HF
(inpatient or outpatient) that identified them for recruitment into our study. Serum sodium
(measured in mmol/l), blood urea nitrogen (measured in mg/dl), B-Type natriuretic peptide
(BNP) (measured in pg/ml) and N-Terminal pro-BNP (NT-BNP) (measured in pg/ml) values
were obtained within 1 year of administering PHQ-9. BNP values were used only when
NT-BNP values were not available. Because of the need to use both BNP and NT-BNP data, we
dichotomized them into raised vs. not raised. Raised BNP was defined as values more than 400
pg/ml. Raised NT-BNP was defined as values more than 450 pg/ml for age<50, more than 900
pg/ml for age 50–75, and more than 1800 pg/ml for age >75 [23].
Measurement of Clinical Outcomes
Participants were followed for 2 years after administering PHQ-9 for all-cause death and all-
cause hospitalization. Deaths were obtained from inpatient and outpatient medical records and
death certificates received from the state of Minnesota. Hospitalizations were ascertained using
data from the Rochester Epidemiology Project. For patients hospitalized at the time of their
HF, only subsequent hospitalizations were included in the analysis. In-hospital transfers or
transfers between Olmsted Medical Center and Mayo Clinic were analysed as a single hospitali-
zation event.
Statistical Analysis
Baseline patient characteristics were reported as a frequency (%) for categorical variables and
mean (standard deviation [SD]) or median (25
th
percentile, 75
th
percentile) for continuous var-
iables. Two-sample t-tests and χ
2
tests were used to test differences in baseline characteristics
between patients with and without depressive symptoms for continuous and categorical vari-
ables, respectively. A Kaplan-Meier survival plot was constructed to illustrate the association
between depression and all-cause mortality. A cumulative incidence plot was constructed for
first hospitalization treating death as a competing risk. Cox proportional hazards regression
models were used to examine the associations between presence of depressive symptoms with
all-cause mortality and first hospitalization. The proportional hazards assumption was tested
for both outcomes and found to be valid. Results were reported as hazard ratios (HR) with 95%
confidence intervals (CI).
Ten of the 11 most commonly used prognostic markers for chronic HF outcomes identified
from the published literature by Ouwerkerk and his colleagues were included in the model as
confounding factors [6]; we chose this model as it distinguishes between prognostic markers
for acute and chronic HF patients. Information on the New York Heart Association (NYHA)
functional classification [24] was not consistently available and thus was not included in the
model. Age, systolic blood pressure, estimated glomerular filtration rate, serum sodium and
blood urea nitrogen were included in multivariable models as confounders and modelled as
continuous variables. Ejection fraction was log transformed and included as continuous vari-
ables in the multivariable models. Gender, presence of diabetes, ischaemic aetiology and ele-
vated B-Type natriuretic peptide (BNP) or N-Terminal pro-BNP (NT-BNP) were included as
categorical variables. The 10 confounding factors identified by Ouwerkerk and colleagues were
included in all multivariable models.
Depression and Heart Failure Prognosis
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The prognostic utility of presence of depressive symptoms for 2-year mortality and hospital-
ization risk prediction was compared against a base model consisting of the 10 prognostic
markers described above using three different statistical methods: area under the receiver oper-
ating characteristic curve (AUC), integrated discrimination improvement (IDI) and a continu-
ous version of the net reclassification improvement (NRI) [25,26]. The IDI indicates if adding
presence of depressive symptoms to the prediction model improves the discrimination slope,
defined as the average predicted probability of outcome for those who experienced the outcome
versus those who did not. The IDI is the difference in the discrimination slopes for the models
with and without presence of depressive symptoms. The NRI assesses net improvement in risk
classification. Individuals are divided into those who experienced the outcome and those who
did not. The predicted probability of the outcome is calculated for each individual, first using
the base prediction model and then after adding presence of depressive symptoms to the
model. The NRI is a measure of the number of individuals who experienced the outcome who
were reclassified upward and the number of individuals who did not experience the outcome
who were reclassified downward after adding presence of depressive symptoms to the model.
Outcomes within the first 2 years after HF were included in the analyses. Because the NRI and
IDI analyses require that the outcome be known, patients who were lost to follow-up before 2
years and who were known to be alive at the last follow-up were excluded from the analyses. In
predicting all-cause mortality and hospitalization, values of AUC were reported for the base
model and after adding presence of depressive symptoms. A p-value of less than 0.05 was used
to assess statistical significance. Sensitivity analyses included repeating the analyses for the
presence of moderate to severe depression (PHQ-910), and also repeating the analysis using
PHQ-9 as a continuous variable. All analyses were performed using R 3.0.2 (The R Foundation
for Statistical Computing) [27] and SAS version 9.3 (SAS Institute Inc., Cary, NC).
Results
Patient Population and Characteristics
A total of 1147 patients with chronic HF diagnosed between October 2007 and December 2011
were approached to participate in the study and 663 (58.0%) patients consented. The patients
who were approached but refused to participate were significantly older (mean age 78.6 ± 12.2
vs. 74.1 ± 13.3, respectively, p<0.001) and significantly more likely to be female (54.3% vs
45.4% respectively, p = 0.003) than study participants. Of those, 546 (82.0%) completed the
PHQ-9 at a median time of 39 days (25
th
,75
th
percentile: 27, 58) after enrolment. Eleven
patients were excluded because they were lost to follow-up before the end of 2 years and 110
were excluded due to missing covariate values, resulting in 425 patients (mean age (SD) 73.5
(13.2); 57.6% men) included in the analyses (Fig 1). Among the study participants, 179 (42.1%)
patients had depressive symptoms based on a PHQ-9 score 5, while 61 (14.4%) patients were
classified as having moderate to severe depression based on a PHQ-9 score 10. The 10 clinical
measures included in the base prognostic model are presented for patients with and without
depressive symptoms in Table 1. No statistically significant differences were observed between
the two patient groups, with the exception of age as patients with depressive symptoms were
younger.
Presence of Depressive Symptoms, All-cause Mortality and
Hospitalization
At the end of 2 years, 99 (23.3%) patients had died and 299 (70.4%) patients had at least 1 hos-
pitalization. Patients with depressive symptoms had worse survival and hospitalization-free
Depression and Heart Failure Prognosis
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survival over 2 years of follow-up (Fig 2). Presence of depressive symptoms was associated with
more than a 2-fold increased risk of all-cause mortality within 2 years, unadjusted and after
adjusting for the 10 most commonly used prognostic factors (Table 2). The unadjusted and
adjusted risk of hospitalization was almost 50% higher among HF patients with depression
compared to those without it.
Prognostic Utility of Depression in Prediction of All-cause Mortality and
First Hospitalization
Table 3 compares the prognostic utility of adding depressive symptoms to the base model in
predicting all-cause mortality and hospitalization within 2 years. The difference between the
two models was not statistically significant for AUC values for either of the two outcomes.
However, the IDI and NRI values showed statistically significant improvement for predicting
Fig 1. Patient Recruitment. PHQ-9 = 9-item Patient Health Questionnaire.
doi:10.1371/journal.pone.0158570.g001
Table 1. Established prognostic factors at baseline in chronic heart failure patients with and without depressive symptoms.
Patients with depressive symptoms
(N = 179)
Patients without depressive symptoms
(N = 246)
p-value
Age (years), mean (SD) 71.77 (13.50) 74.79 (12.77) 0.02
Male 108 (60.34%) 137 (55.69%) 0.34
Systolic BP (mm Hg), mean(SD) 123.00 (23.12) 124.57 (23.04) 0.49
Estimated glomerular filtration rate, mean (SD) 56.32 (25.29) 57.70 (21.16) 0.54
Ejection fraction (%), median (25th, 75th
percentile)
45.33 (31.00, 60.00) 50.00 (33.20, 60.00) 0.26
Serum sodium (mmol/l), median (25
th
,75
th
percentile)
140.00 (137.00,141.00) 139.00 (137.00,141.00) 0.88
Elevated level of BNP/NT-BNP 126 (70.39%) 177 (71.95%) 0.73
Ischemic etiology 76 (42.46%) 105 (42.68%) 0.96
Prior diabetes mellitus 76 (42.46%) 87 (35.37%) 0.14
Legend: Results are reported as n (%) unless otherwise noted. Presence of depressive symptoms defined as 9-item Patient Health Questionnaire (PHQ-9)
5. BNP = B-Type natriuretic peptide; BP = blood pressure; NT-BNP = N-Terminal pro-BNP; PHQ-9 = 9-item Patient Health Questionnaire; SD = standard
deviation.
doi:10.1371/journal.pone.0158570.t001
Depression and Heart Failure Prognosis
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all-cause mortality after adding depressive symptoms. Regarding hospitalizations, the IDI and
NRI-continuous showed statistically significant improvement after adding depressive symp-
toms to the model.
Sensitivity Analysis
Results of sensitivity analyses are presented in S1 File. A stronger association was observed
between presence of moderate to severe depression (PHQ-910) and all-cause mortality and
hospitalization, when compared to mild or no depressive symptoms. Additionally, the NRI and
IDI values improved significantly in predicting death and hospitalization, while there was no
significant change in the AUC values Furthermore, when analysing the PHQ-9 score as a con-
tinuous variable, a 10% increase in all-cause death and a 5% increase in hospitalization were
observed per point increase in PHQ-9 score after adjustment for the 10 most commonly used
prognostic factors.
Discussion
In a community cohort in the US, patients with chronic HF were found to have a high preva-
lence of depressive symptoms. Depression was associated with a higher risk of death and hospi-
talization compared to those without depression. These findings remain unchanged after
adjusting for the 10 most commonly used prognostic factors in risk prediction for HF out-
comes. Finally, adding depression to an existing prognostic model improved the prognostic
utility in predicting death and hospitalization.
Fig 2. (A) Survival Plot for All-cause Mortality for Chronic Heart Failure Patients. (B) 1-Cumulative
Incidence Plot for First Hospitalization Treating Death as a Competing Risk for Chronic Heart Failure
Patients. Depressed Defined as 9-item Patient Health Questionnaire (PHQ-9) 5.
doi:10.1371/journal.pone.0158570.g002
Table 2. Hazard ratios for all-cause death and first hospitalization within 2 years after HF for chronic
heart failure patients with vs without depressive symptoms.
All-Cause Death Hospitalization
Number of patients 425 425
Number of events 99 299
Unadjusted HR (95% CI) 1.87 (1.26, 2.78) 1.48 (1.18, 1.86)
Adjusted*HR (95% CI) 2.02 (1.34–3.04) 1.42 (1.13–1.80)
Legend: Presence of depressive symptoms defined as 9-item Patient Health Questionnaire (PHQ-9) 5.
CI = Confidence interval; HR = Hazard Ratio.
*Adjusted for age, sex, systolic blood pressure, estimated glomerular filtration rate, blood urea nitrogen,
serum sodium, elevated B-Type natriuretic peptide (BNP) or N-Terminal pro-BNP, ejection fraction,
ischaemic aetiology and prior diabetes.
doi:10.1371/journal.pone.0158570.t002
Depression and Heart Failure Prognosis
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Published results on the prevalence of depression in HF are varied. In our cohort, the preva-
lence of depression was 40.7% based on a symptom questionnaire, which is congruent with the
reported prevalence of 33.6% in a meta-analysis [15]. HF patients with co-existing depression
were approximately twice as likely to die in our study, which is consistent with previous find-
ings [15]. Depression was also associated with an increase in the risk of hospitalization, which
is again consistent with previous findings [14,15,28–31].
Two previous studies have assessed the prognostic utility of depression; however they have
used “history of previous depression”as opposed to “current depression”as was used in our
study [32,33]. Herein, addition of depression did not improve AUC values from the base
model for predicting death and hospitalization. The lack of sensitivity of AUC in judging prog-
nostic utility of a new marker has been discussed previously and the present study underscores
the importance of incorporating methods such as IDI and NRI in risk prediction [26,34].
Limitations, Strengths and Implications
Depressive symptoms were measured only at enrolment and we cannot account for changes
during follow-up. Some of the symptoms of depression overlap with common symptoms of
HF, such as fatigue, low energy, psychomotor retardation, and sleep disturbances [13]. While
the NYHA functional status was not consistently available in our cohort, evidence suggests
inconsistency and high inter-operator variability in clinical recordings of NYHA in practice,
which illustrates the practical problems in using it as a prognostic marker [35]. Further, depres-
sion has been shown to predict death and hospitalization in HF independent of NYHA func-
tional status [28]. Finally, the population of southeast Minnesota is chiefly white and thus, our
results should be examined in other racial groups.
Our study has a number of notable strengths. The participants were recruited from a com-
munity cohort, including both inpatients and outpatients, which is of optimal clinical rele-
vance. Depression was prospectively ascertained using a validated instrument and follow-up
was complete for critical outcomes in HF. Analytically, we used robust and complementary
risk prediction methods which optimize our ability to assess the prognostic value of
depression.
Despite the high prevalence of depressive symptoms in HF [15], it remains under recog-
nized [36], and no study to date, to the best of our knowledge, has demonstrated the benefits of
routine depression screening [37]. Indeed, treatment with anti-depressants has not shown any
clear benefit in reducing depressive symptoms, deaths and hospitalization in HF [38–40].
Table 3. Comparison of the prognostic utility of adding depressive symptoms to the base model in predicting all-cause mortality and hospitaliza-
tion within 2 years after heart failure in chronic heart failure patients.
Outcome Model AUC (95% CI) IDI, % (95% CI) NRI-continuous, % (95% CI)
All-cause Death Base model*0.781(0.729–0.834)
Base model + depressive symptoms 0.800(0.748–0.852) 3.10(1.28–4.92) 35.04(12.80–57.27)
p-value 0.06 0.001 0.002
Hospitalization Base model*0.667(0.609–0.724)
Base model + depressive symptoms 0.679(0.621–0.736) 1.73(0.53–2.93) 27.23(7.34–47.11)
p-value 0.36 0.005 0.007
Legend: Presence of depressive symptoms defined as 9-item Patient Health Questionnaire (PHQ-9) 5. AUC = area under curve; CI = confidence interval;
IDI = integrated discrimination improvement; NRI = net re-classification improvement.
*Base model includes age, sex, systolic blood pressure, estimated glomerular filtration rate, blood urea nitrogen, serum sodium, elevated B-Type natriuretic
peptide (BNP) or N-Terminal pro-BNP, ejection fraction, ischaemic aetiology and prior diabetes.
doi:10.1371/journal.pone.0158570.t003
Depression and Heart Failure Prognosis
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There is some conflicting evidence about the use of cognitive behavioural therapy (CBT) in
reducing depressive symptoms in HF patients, with a review suggesting no benefit [41], while
some of the recent studies showing improvement in depressive symptoms with CBT [42,43].
On the other hand, other psychological interventions such as mindfulness-based stress reduc-
tion [44] have been shown to have the potential to improve depressive symptoms in HF
patients. The recent guidelines published by the American Heart Association and the American
College of Cardiology guidelines discuss the importance of depression as an important co-mor-
bidity in heart failure patients and its association with reduced poor quality of life and poor
health outcomes [4]. Some research suggests that lack of perceived social support may be an
important mediator of poor prognosis associated with depression in HF patients [45,46],which
in turn is potentially modifiable [47]. Further research should address these knowledge gaps.
It is important to underscore that ascertaining depression relies on a clinical assessment,
which is efficient and not costly. We demonstrated the incremental information conferred by
depression over well-established clinical factors, thereby indicating that assessing mental health
and depression should be part of the holistic clinical evaluation of patients living with HF.
Conclusion
In HF, depression is frequent and is associated with an increase in deaths and hospitalizations.
Depression increases the prognostic value of established and commonly used factors in HF
patients. Further research is needed to determine the role of depression screening and ascertain
the best strategies for managing depressive symptoms in HF patients.
Supporting Information
S1 File. Sensitivity Analysis.
(DOCX)
S2 File. Dataset.
(XLSX)
Acknowledgments
The study was undertaken during BDJ’s visiting fellowship to Mayo Clinic.
Author Contributions
Conceived and designed the experiments: BJ VR SW FM AC. Analyzed the data: BJ SW RJ AC.
Wrote the paper: BJ FM VR SW RJ AC.
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