Patient phenotype profiling using echocardiography and natriuretic peptides to personalise heart failure therapy.
Frank Lloyd DiniErberto CarluccioStefano GhioNicola Riccardo PuglieseGiangiacomo GaleottiMichele CorrealeMatteo BeltramiCarlo Gabriele TocchettiValentina MercurioStefania PaolilloAlberto Palazzuolinull nullPublished in: Heart failure reviews (2023)
Heart failure (HF) is a progressive condition with a clinical picture resulting from reduced cardiac output (CO) and/or elevated left ventricular (LV) filling pressures (LVFP). The original Diamond-Forrester classification, based on haemodynamic data reflecting CO and pulmonary congestion, was introduced to grade severity, manage, and risk stratify advanced HF patients, providing evidence that survival progressively worsened for those classified as warm/dry, cold/dry, warm/wet, and cold/wet. Invasive haemodynamic evaluation in critically ill patients has been replaced by non-invasive haemodynamic phenotype profiling using echocardiography. Decreased CO is not infrequent among ambulatory HF patients with reduced ejection fraction, ranging from 23 to 45%. The Diamond-Forrester classification may be used in combination with the evaluation of natriuretic peptides (NPs) in ambulatory HF patients to pursue the goal of early identification of those at high risk of adverse events and personalise therapy to antagonise neurohormonal systems, reduce congestion, and preserve tissue/renal perfusion. The most benefit of the Guideline-directed medical treatment is to be expected in stable patients with the warm/dry profile, who more often respond with LV reverse remodelling, while more selective individualised treatments guided by echocardiography and NPs are necessary for patients with persisting congestion and/or tissue/renal hypoperfusion (cold/dry, warm/wet, and cold/wet phenotypes) to achieve stabilization and to avoid further neurohormonal activation, as a result of inappropriate use of vasodilating or negative chronotropic drugs, thus pursuing the therapeutic objectives. Therefore, tracking the haemodynamic status over time by clinical, imaging, and laboratory indicators helps implement therapy by individualising drug regimens and interventions according to patients' phenotypes even in an ambulatory setting.
Keyphrases
- left ventricular
- heart failure
- ejection fraction
- end stage renal disease
- newly diagnosed
- computed tomography
- blood pressure
- pulmonary hypertension
- machine learning
- multiple sclerosis
- stem cells
- acute heart failure
- chronic kidney disease
- high resolution
- emergency department
- prognostic factors
- deep learning
- physical activity
- acute coronary syndrome
- cardiac resynchronization therapy
- mitral valve
- atrial fibrillation
- mass spectrometry
- smoking cessation
- big data
- patient reported
- cell therapy
- adverse drug