Echocardiographic Patterns of Left Ventricular Diastolic Function in Cardiac Amyloidosis: An Updated Evaluation.

AIMS: Multimodal imaging has allowed cardiac amyloidosis (CA) to be increasingly recognised as a treatable cause of heart failure with preserved ejection fraction, but its prognosis remains poor due to late diagnosis. To assess the left ventricular diastolic function (LVDF) patterns in a large contemporary CA cohort according to the current recommendations and to identify their determinants. METHODS AND RESULTS: We conducted a monocentric, observational study on a cohort of CA patients from a tertiary CA referral centre. Diastolic function was analysed using standard echocardiography and clinical, laboratory and survival parameters were collected. Four hundred and sixty-four patients with one of the three main type of CA were included: 41% had grade III diastolic dysfunction (restrictive mitral pattern), 25% had grade II diastolic dysfunction, and 25% had grade I diastolic dysfunction; 9% were unclassified. No difference was found between the main CA types. After multivariate analyses, grades II and III were independently associated with dyspnoea, elevated NT-proBNP, cardiac infiltration and systolic dysfunction (global longitudinal strain). Grade I patients had a better prognosis. CONCLUSIONS: All LVDF patterns can be observed in CA. One quarter of CA patients have grade I LVDF, reflecting the emergence of earlier stage-related phenotypes with a better prognosis.

Assessing Cardiac Amyloidosis Subtypes by Unsupervised Phenotype Clustering Analysis.

BACKGROUND: Cardiac amyloidosis (CA) is a set of amyloid diseases with usually predominant cardiac symptoms, including light-chain amyloidosis (AL), hereditary variant transthyretin amyloidosis (ATTRv), and wild-type transthyretin amyloidosis (ATTRwt). CA are characterized by high heterogeneity in phenotypes leading to diagnosis delay and worsened outcomes. OBJECTIVES: The authors used clustering analysis to identify typical clinical profiles in a large population of patients with suspected CA. METHODS: Data were collected from the French Referral Center for Cardiac Amyloidosis database (Hôpital Henri Mondor, Créteil), including 1,394 patients with suspected CA between 2010 and 2018: 345 (25%) had a diagnosis of AL, 263 (19%) ATTRv, 402 (29%) ATTRwt, and 384 (28%) no amyloidosis. Based on comprehensive clinicobiological phenotyping, unsupervised clustering analyses were performed by artificial neural network-based self-organizing maps to identify patient profiles (clusters) with similar characteristics, independent of the final diagnosis and prognosis. RESULTS: Mean age and left ventricular ejection fraction were 72 ± 13 years and 52% ± 13%, respectively. The authors identified 7 clusters of patients with contrasting profiles and prognosis. AL patients were distinctively located within a typical cluster; ATTRv patients were distributed across 4 clusters with varying clinical presentations, 1 of which overlapped with patients without amyloidosis; interestingly, ATTRwt patients spread across 3 distinct clusters with contrasting risk factors, biological profiles, and prognosis. CONCLUSIONS: Clustering analysis identified 7 clinical profiles with varying characteristics, prognosis, and associations with diagnosis. Especially in patients with ATTRwt, these results suggest key areas to improve amyloidosis diagnosis and stratify prognosis depending on associated risk factors.

Epidemiological characteristics and therapeutic management of patients with chronic heart failure who use smartphones: Potential impact of a dedicated smartphone application (report from the OFICSel study).

BACKGROUND: The effectiveness of transitional care services for patients discharged from hospital after acute heart failure is challenging, especially in terms of reducing subsequent heart failure hospitalizations. The increased adoption of smartphone applications in society offers a new opportunity to interact with patients to avoid rehospitalization. Thus, electronic health (e-health) can enhance the impact of existing therapeutic education programmes. AIMS: To determine the prevalence of smartphone use among patients with chronic heart failure, and to assess the epidemiological characteristics and therapeutic management of these patients, with a broader aim of developing smartphone-based therapeutic education programmes for patients. METHODS: The French Observatoire français de l'insuffisance cardiaque et du sel (OFICSel) registry was conducted in 2017 by 300 cardiologists, and included both inpatients and outpatients who had been hospitalized for heart failure at least once in the previous 5 years. Data collection included demographic and heart failure-related variables, which were provided by the cardiologist and by the patient via a questionnaire. RESULTS: Among the 2822 patients included, 2517 completed the questionnaire. Of this total, 907 patients (36%) were smartphone users. Compared with non-users, smartphone users were younger, were more frequently men, more frequently lived in cities, had a higher educational level and were more frequently professionally active. Smartphone users less frequently had diabetes, hypertension, atrial fibrillation or ischaemic cardiopathy. Only 22% of patients were actively participating in a therapeutic education programme. CONCLUSION: Smartphones were used by more than one-third of patients with heart failure in France in 2017, underscoring the feasibility of developing a smartphone application to deliver therapeutic education to the population with chronic heart failure.