Prognostic value of left atrial parameters derived from magnetic resonance in dilated cardiomyopathy: a systematic review and meta-analysis

Based on the existing literature, LA strain parameters show high predictive value for adverse cardiovascular events in DCM patients, while volume parameters show relatively low predictive value.

Authors

Institutions

Publication Details

Journal
Quantitative Imaging in Medicine and Surgery
Published
2026-01-28
DOI
https://doi.org/10.21037/qims-2025-1686
Primary Topic
Cardiovascular Function and Risk Factors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prognostic value of left atrial parameters derived from magnetic resonance in dilated cardiomyopathy: a systematic review and meta-analysis

Yipei Song, Haibo Ren, Mengyao Hu, Qimin Fang et al.
Quantitative Imaging in Medicine and Surgery
Cardiovascular Function and Risk Factors
article

Prognostic value of left atrial parameters derived from magnetic resonance in dilated cardiomyopathy: a systematic review and meta-analysis

Yipei Song, Haibo Ren, Mengyao Hu, Qimin Fang, Lianggeng Gong
article en

Abstract

Based on the existing literature, LA strain parameters show high predictive value for adverse cardiovascular events in DCM patients, while volume parameters show relatively low predictive value.

Quantitative Imaging in Medicine and SurgeryVol. 16(2)
Nanchang University (CN), Second Affiliated Hospital of Nanchang University (CN)
Good health and well-being
Openalex Percentile: Top 76%
Cardiovascular Function and Risk Factors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.