Natural history and disease progression of early cardiac amyloidosis evaluated by echocardiography

Funding Acknowledgements

Type of funding sources: None.

Background

Since the diagnosis of cardiac amyloidosis (CA) is often delayed, echocardiographic findings are frequently indicative of already advanced cardiomyopathy.

Aims

to describe early echocardiographic features in patients subsequently diagnosed with CA and to delineate disease progression.

Methods

Pre-amyloid diagnosis echocardiographic studies were screened for structural and functional parameters and stratified according to the pathogenetic amyloid subtype (immunoglobulin light-chain (AL) or amyloid transthyretin (ATTR)). Abnormalities were defined based on published guidelines.

Results

Our cohort included 75 CA patients of whom 42 (56%) were diagnosed with AL and 33 (44%) with ATTR. Forty-two patients had an earlier echocardiography exam available for review. Patients presented with increased wall thickness (1.3 (IQR 1.0, 1.5)cm) ≥3 years before the diagnosis of CA and relative wall thickness (RWT) was increased (0.47 (IQR 0.41, 0.50)) ≥7 years pre-diagnosis. Between 1 to 3 years before CA diagnosis restrictive left ventricular (LV) filling pattern was present in 19% of patients and LV ejection fraction (LVEF)≤50% was present in 21% of patients. Right ventricular dysfunction was detected concomitantly with disease diagnosis. The echocardiographic phenotype of ATTR versus AL-CA showed increased RWT (0.74 (IQR 0.62, 0.92) vs. 0.62 (IQR 0.54, 0.76), p = 0.004) and LV mass index (144 (IQR 129, 191) vs. 115 (IQR 105, 146)g/m2,p = 0.020) and reduced LVEF (50 (IQR 44, 58) vs. (60 (IQR 53, 60)%, p = 0.009) throughout the time course of CA progression, albeit survival time was similar.

Conclusions

Increased wall thickness and diastolic dysfunction in CA develop over a time course of several years and can be diagnosed in their earlier stages by standard echocardiography

Abstract Figure. Schematic proposed timeline of CA

P6539Automated detection of atrial fibrillationb based on vocal features analysis

Background

Early identification of atrial fibrillation (AF) has been a long-standing clinical challenge and an unmet need. AF may be present without the patient being aware of any symptoms, leading to a delay in or failure of diagnosis. ESC guidelines recommend opportunistically screening patients >65 years of age using pulse palpation or an ECG rhythm strip, however these methods have limited effectivity. Analysis of natural speech signals has been used as a monitoring tool for various medical conditions and has recently been reported to enable estimation of heart rate. In this study we evaluated the efficacy of vocal features analysis in the detection of AF and in discriminating between sinus rhythm (SR) and episodes of AF.

Methods

In this prospective multicenter study, patients with persistent AF admitted for cardioversion were enrolled. A total of 86 patients in 2 centers were included. Prior to cardioversion, the patients spoke specific vowels and words according to a pre-specified protocol; an ECG tracing was simultaneously recorded. Following successful cardioversion and recovery from sedation, these recordings were repeated in SR. The recordings of the first 34 patients were used to develop an algorithm of AF detection based on analysis of acoustic features in SR and AF conditions. The algorithm was then validated in all of the patients (n=86) in the following manner: 25% of SR recordings were used to train the algorithm while the remaining 75% of SR and 100% of AF data from every patient were tested with the algorithm to distinguish between AF and SR.

Results

A total of 513 recordings from 86 patients were analyzed. Classification of the recordings as AF or SR was performed using varying cutoff values of the separation parameter. The resulting curve showing the specificity and sensitivity of the developed algorithm for distinguishing AF from SR is presented. Two specific examples of working points are a specificity of 92% and sensitivity of 83%, and a specificity of 82% and sensitivity of 92%.

Conclusions

This study demonstrates the feasibility of detecting AF and discriminating it from SR using analysis of acoustic features extracted from spoken vowels. The potential use of this method for wider population screening should be further evaluated.

Bile duct carcinoma in an adolescent

Bile duct carcinoma is a relatively uncommon lesion that usually presents in the 6th to 7th decade. The only prior reported cases under the age of 20 were two patients with diseases known to predispose to the development of this tumor. We report a case of a 17-year-old girl with a bile duct tumor without any known predisposing factors.