Assessing Cardiac Amyloidosis Subtypes by Unsupervised Phenotype Clustering Analysis.
Aged
Aged, 80 and over
Amyloidosis
/ classification
Cardiomyopathies
/ classification
Cluster Analysis
Disease Progression
Echocardiography
/ methods
Female
Follow-Up Studies
Humans
Male
Phenotype
Prognosis
Prospective Studies
Stroke Volume
/ physiology
Time Factors
Ventricular Function, Left
/ physiology
cardiac amyloidosis
clustering
diagnosis
phenotype
prognosis
Journal
Journal of the American College of Cardiology
ISSN: 1558-3597
Titre abrégé: J Am Coll Cardiol
Pays: United States
ID NLM: 8301365
Informations de publication
Date de publication:
30 11 2021
30 11 2021
Historique:
received:
17
06
2021
revised:
30
08
2021
accepted:
01
09
2021
entrez:
26
11
2021
pubmed:
27
11
2021
medline:
5
1
2022
Statut:
ppublish
Résumé
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. The authors used clustering analysis to identify typical clinical profiles in a large population of patients with suspected CA. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 34823661
pii: S0735-1097(21)07172-2
doi: 10.1016/j.jacc.2021.09.858
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2177-2192Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Funding Support and Author Disclosures Dr Bonnefous is supported by a PhD grant from GlaxoSmithKline. Dr Oghina has received honoraria from Pfizer. Dr Damy has received research and/or consultant fees from GlaxoSmithKline, Alnylam, Pfizer, Prothena, Ionis, Akcea, and Janssen. Dr Audureau has received consultant fees from GBT and Hemanext. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.