Utilizing artificial intelligence to detect cardiac amyloidosis in patients with severe aortic stenosis: A step forward to diagnose the underdiagnosed

Excerpt

Transthyretin amyloid cardiomyopathy (ATTR-CM) is a progressive cardiac disease caused by the deposition of amyloid fibrils in cardiac tissues and is characterised by heart failure (HF), conduction disorders, supraventricular arrhythmias, and frequently occurs with concomitant valvular lesions [1, 2]. Due to the lack of treatment options in the past, ATTR-CM has historically become an underdiagnosed disease [3]. Unsurprisingly, since the approval of disease-modifying drugs [4], many efforts have been undertaken to diagnose ATTR-CM as soon as possible as early diagnosis remains crucial to initiate timely treatment and improve prognosis [5]. Indeed, risk scores for the presence of ATTR-CM in patients with heart failure with preserved ejection fraction (HFpEF) have been developed [6‐8], clinical pathways have been implemented to decrease diagnostic delay [9, 10], myths of ATTR-CM have been busted [11, 12], and family screening protocols have been employed to diagnose those with a genetic predisposition of ATTR-CM as early as possible [13]. Although these efforts have undoubtedly led to better care for the general ATTR-CM patient, little efforts have been employed to diagnose ATTR-CM in a population notoriously known to include ATTR-CM: patients with severe aortic stenosis (AS) [14, 15]. …