Optimising the Detection and Multidisciplinary Management of Multimorbid Heart Failure in Primary Care

  • Stewart, Simon (Principal Chief Investigator)
  • Beilby, Prof Justin (Chief Investigator)
  • Hills, Danny (Chief Investigator)
  • Scuffham, Paul A. (Chief Investigator)
  • Ambagtsheer, Rachel (Chief Investigator)
  • Wechkunanukul, Hannah (Chief Investigator)

Project Details

Funder/Funding Scheme

Medical Research Future Fund - Primary Health Care Research Initiative (2023 Primary Health Care Research Grant Opportunity Stream 1) MRF 2031996

Project Description

Chronic Heart failure (HF) is a complex chronic condition that, when left undetected and untreated, results in ill-health, poor quality of life (QoL), recurrent hospitalisation, and premature mortality. CHF typically occurs in older people with a combination of hypertension, diabetes, coronary artery disease (CAD), atrial fibrillation (AF) and respiratory disease (many of whom have depression and/or renal failure). Without new strategies, CHF will drive unsustainable rises in hospital cases, GP visits, premature deaths, and healthcare costs among the 11.6M Australians (47%) with a chronic condition. Currently, there is limited evidence to support primary health care teams to proactively find and optimally care for people with CHF before they present to hospital acutely ill and in danger of dying. In an important partnership with the Australian Primary Health Care Nurses Association (APNA), consumers living with chronic disease, and in close engagement with participating and committed GPs/primary care clinics, our diverse, multidisciplinary team will conduct a multi-centre, pragmatic trial of an innovative primary health care nurse (PHCN)-focused, model of care to address the critical lack of evidence to address CHF in primary care.
StatusActive
Effective start/end date1/03/2428/02/28

Keywords

  • primary care
  • chronic heart failure (CHF)
  • treatment
  • prevention

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.