Evidence map›Paper›PMID 38095711›Full record

ArticleClinical research in cardiology : official journal of the German Cardiac Society2024

Multi-parametric non-contrast cardiac magnetic resonance for the differentiation between cardiac amyloidosis and hypertrophic cardiomyopathy.

Henning Steen, Moritz Montenbruck, Alexandros Kallifatidis, Florian André, Norbert Frey, Sebastian Kelle, Grigorios Korosoglou

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Article in Clinical research in cardiology : official journal of the German Cardiac Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Henning SteenDepartment of Cardiology, Angiology and Pneumology, University Hospital Heidelberg, Heidelberg, Germany.
Moritz MontenbruckDepartment of Cardiology, Marien Hospital Hamburg, Hamburg, Germany.
Alexandros KallifatidisDepartment of Radiology, Saint Luke Hospital, Thessaloniki, Greece.
Florian AndréDepartment of Cardiology, Angiology and Pneumology, University Hospital Heidelberg, Heidelberg, Germany.
Norbert FreyDepartment of Cardiology, Angiology and Pneumology, University Hospital Heidelberg, Heidelberg, Germany.
Sebastian KelleDepartment of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité Berlin, Berlin, Germany.
Grigorios KorosoglouDepartments of Cardiology, Vascular Medicine and Pneumology, GRN Hospital Weinheim, Roentgenstrasse 1, 69469, Weinheim, Germany. gkorosoglou@hotmail.com.ORCID http://orcid.org/0000-0002-6038-2253

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo evaluate the ability of fast strain-encoded (SENC) cardiac magnetic resonance (CMR) derived myocardial strain and native T1 mapping to discriminate between hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis.

methodsNinety nine patients (57 with hypertrophic cardiomyopathy and 42 with cardiac amyloidosis) were systematically analysed. LV-ejection fraction, LV-mass index, septal wall thickness and native T1 mapping values were assessed. In addition, global circumferential and longitudinal strain and segmental circumferential and longitudinal strain in basal, mid-ventricular, and apical segments were calculated. A ratio was built by dividing native T1 values by basal segmental strain (T1-to-basal segmental strain ratio).

resultsMyocardial strain was equally distributed in apical and basal segments in HCM patients, whereas an apical sparing with less impaired apical strain was noticed in cardiac amyloidosis (apical-to-basal-ratio of 1.01 ± 0.23 versus 1.20 ± 0.28, p < 0.001). T1 values were significantly higher in amyloidosis compared to HCM patients (1170.7 ± 66.4 ms versus 1078.3 ± 57.4ms, p < 0.001). The T1-to-basal segmental strain ratio exhibited high accuracy for the differentiation between the two clinical entities (Sensitivity = 85%, Specificity = 77%, AUC = 0.90, 95% CI = 0.81-0.95, p < 0.001). Multivariable analysis showed that age and the T1-to-basal-strain-ratio were the most robust factors for the differentiation between HCM and cardiac amyloidosis.

conclusionThe T1-to-basal-segmental strain ratio, combining information from segmental circumferential and longitudinal strain and native T1 mapping aids the differentiation between HCM and cardiac amyloidosis with high accuracy and within a fast CMR protocol, obviating the need for contrast agent administration.

Indexed as

AmyloidosisCardiomyopathy, HypertrophicHumansMagnetic Resonance ImagingMagnetic Resonance Imaging, CineMagnetic Resonance SpectroscopyMyocardiumCardiac amyloidosisFast strain-Encoded CMR (fast-SENC)Hypertrophic cardiomyopathyMyocardial hypertrophyNon-contrastT1 mapping

Identifiers

PMID38095711

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.