Evidence map›Paper›PMID 42483458›Full record

ReviewFrontiers in cardiovascular medicine2026

Multidimensional cardiac functional phenotyping beyond ejection fraction: a multimodality imaging framework for cardiovascular care.

Yuan Li, Yujian Liu

Abstract readReview
In one paragraph

Review in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yuan LiDepartment of Ultrasound, Zigong Fourth People's Hospital, Zigong, Sichuan, China.
Yujian LiuDepartment of Radiology, Zigong First People's Hospital, Zigong, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiac functional assessment has traditionally relied on left ventricular ejection fraction (LVEF), but this single volumetric index cannot fully capture the complexity of cardiac physiology. Contemporary multimodality imaging enables cardiac function to be evaluated across five interrelated domains: pump function, myocardial deformation, diastolic function, flow dynamics, and tissue characterization. Integrating biomarkers across these domains may provide a more pathophysiologically grounded interpretation of cardiac dysfunction than LVEF alone. Clinically, multidimensional functional phenotyping may refine disease characterization and risk stratification in conditions such as heart failure with preserved ejection fraction, cardiomyopathies, and valvular heart disease. This review proposes a five-dimensional, phenotype-oriented multimodality imaging framework for cardiovascular care and emphasizes a selective stepwise escalation strategy from standard echocardiography to advanced echocardiographic analysis, cardiac magnetic resonance, computed tomography, nuclear imaging, and selected flow-based assessment, particularly 4D flow cardiovascular magnetic resonance, when clinically indicated. The framework is intended to support clinically actionable interpretation rather than indiscriminate use of multiple imaging modalities. We also discuss practical barriers to implementation, including cost-effectiveness, workflow burden, vendor variability, limited standardization of 4D flow cardiovascular magnetic resonance, and the need for interpretable and externally validated artificial intelligence-based integration.

Indexed as

artificial intelligencecardiac functiondiastolic functionfunctional phenotypingmultimodality imagingmyocardial deformationtissue characterization

Identifiers

PMID42483458
PMCPMC13385054

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.