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Comorbid Depression and Heart Failure: A Community Cohort Study.
Bhautesh Dinesh Jani1, Frances S Mair1, Véronique L Roger2,3
1General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom.
Plos One
|July 1, 2016
Summary
Depression is common in heart failure (HF) patients and increases their risk of death and hospitalization. Including depression screening in risk prediction models for HF can improve accuracy.
Area of Science:
- Cardiology
- Psychiatry
- Public Health
Background:
- Heart failure (HF) is a chronic condition with significant morbidity and mortality.
- Depression is a common comorbidity in HF patients, but its impact on clinical outcomes requires further investigation in community settings.
Purpose of the Study:
- To investigate the association between depression and clinical outcomes, specifically death and hospitalization, in a community-based cohort of HF patients.
- To evaluate the added value of depression screening in improving risk prediction models for HF patients.
Main Methods:
- A cohort of 425 HF patients in Minnesota were screened for depression using the 9-item Patient Health Questionnaire (PHQ-9).
- Patients with PHQ-9 scores ≥5 were classified as depressed.
- Cox regression models were used to assess the risk of death and hospitalization within 2 years, adjusting for 10 prognostic factors. Model performance was compared using AUC, IDI, and NRI.
Main Results:
- 42.1% of HF patients screened positive for depression (PHQ-9≥5).
- Depression was associated with a significantly increased adjusted hazard ratio for death (2.02) and hospitalization (1.42) within 2 years.
- Incorporating depression into risk models significantly improved discrimination (IDI) and reclassification (NRI) for both death and hospitalization.
Conclusions:
- Depression is a prevalent condition among community-dwelling HF patients.
- Depression is an independent predictor of increased mortality and hospitalizations in HF patients.
- Screening for depression and incorporating it into risk prediction models can enhance the management and outcomes of HF patients.
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