Non-invasive markers for sudden cardiac death risk stratification in dilated cardiomyopathy.
Vivetha PooranachandranWill NicolsonZakariyya ValiXin LiGhulam Andre NgPublished in: Heart (British Cardiac Society) (2021)
Dilated cardiomyopathy (DCM) is a common yet challenging cardiac disease. Great strides have been made in improving DCM prognosis due to heart failure but sudden cardiac death (SCD) due to ventricular arrhythmias remains significant and challenging to predict. High-risk patients can be effectively managed with implantable cardioverter defibrillators (ICDs) but because identification of what is high risk is very limited, many patients unnecessarily experience the morbidity associated with an ICD implant and many others are not identified and have preventable mortality. Current guidelines recommend use of left ventricular ejection fraction and New York Heart Association class as the main markers of risk stratification to identify patients who would be at higher risk of SCD. However, when analysing the data from the trials that these recommendations are based on, the number of patients in whom an ICD delivers appropriate therapy is modest. In order to improve the effectiveness of therapy with an ICD, the patients who are most likely to benefit need to be identified. This review article presents the evidence behind current guideline-directed SCD risk markers and then explores new potential imaging, electrophysiological and genetic risk markers for SCD in DCM.
Keyphrases
- ejection fraction
- heart failure
- end stage renal disease
- left ventricular
- newly diagnosed
- chronic kidney disease
- prognostic factors
- type diabetes
- peritoneal dialysis
- systematic review
- randomized controlled trial
- gene expression
- coronary artery disease
- mesenchymal stem cells
- mass spectrometry
- atrial fibrillation
- patient reported outcomes
- photodynamic therapy
- dna methylation
- acute coronary syndrome
- deep learning
- patient reported
- climate change
- artificial intelligence
- genome wide
- cardiac resynchronization therapy
- electronic health record
- big data