Integrating palliative care into routine care of patients with heart failure: models for clinical collaboration


DOI: 10.1007/s10741-017-9599-2

Cite this article as:
Lewin, W.H. & Schaefer, K.G. Heart Fail Rev (2017). doi:10.1007/s10741-017-9599-2


Heart failure (HF) affects nearly 5.7 million Americans and is described as a chronic incurable illness carrying a poor prognosis. Patients living with HF experience significant symptoms including dyspnea, pain, anxiety, fatigue, and depression. As the illness advances into later stages, symptoms become more intense and refractory to standard treatments, leading to recurrent acute-care utilization and contributing to poor quality of life. Advanced HF symptoms have been described to be as burdensome, if not more than, those in cancer populations. Yet access to and provision of palliative care (PC) for this population has been described as suboptimal. The Institute of Medicine recently called for better access to PC for seriously ill patients. Despite guidelines recommending the inclusion of PC into the multidisciplinary HF care team, there is little data offering guidance on how to best operationalize PC skills in caring for this population. This paper describes the emerging literature describing models of PC integration for HF patients and aims to identify key attributes of these care models that may help guide future multi-site clinical trials to define best practices for the successful delivery of PC for patients living with advanced HF.


Palliative care Supportive cardiology Early palliative care Models of care 

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Brookdale Department of Geriatrics and Palliative MedicineThe Mount Sinai HospitalNew YorkUSA
  2. 2.Icahn School of Medicine at Mount SinaiNew YorkUSA
  3. 3.Division of Palliative MedicineBrigham and Women’s HospitalBostonUSA
  4. 4.Department Psychosocial Oncology and Palliative CareDana-Farber Cancer InstituteBostonUSA
  5. 5.Harvard Medical SchoolBostonUSA

Personalised recommendations