Early Detection of Fluid Retention in Patients with Advanced Heart Failure: A Review of a Novel Multisensory Algorithm, HeartLogicTM.
Michelle FeijenAnastasia D EgorovaSaskia Lambertha Maria Anna BeeresRoderick W TreskesPublished in: Sensors (Basel, Switzerland) (2021)
Heart failure (HF) hospitalisations due to decompensation are associated with shorter life expectancy and lower quality of life. These hospitalisations pose a significant burden on the patients, doctors and healthcare resources. Early detection of an upcoming episode of decompensation may facilitate timely optimisation of the ambulatory medical treatment and thereby prevent heart-failure-related hospitalisations. The HeartLogicTM algorithm combines data from five sensors of cardiac implantable electronic devices into a cumulative index value. It has been developed for early detection of fluid retention in heart failure patients. This review aims to provide an overview of the current literature and experience with the HeartLogicTM algorithm, illustrate how the index can be implemented in daily clinical practice and discuss ongoing studies and potential future developments of interest.
Keyphrases
- heart failure
- healthcare
- machine learning
- ejection fraction
- left ventricular
- acute heart failure
- deep learning
- clinical practice
- end stage renal disease
- newly diagnosed
- systematic review
- cardiac resynchronization therapy
- chronic kidney disease
- atrial fibrillation
- blood pressure
- neural network
- electronic health record
- patient reported outcomes
- artificial intelligence
- climate change
- current status
- social media
- case control