Evidence map›Paper›PMID 39151094›Full record

ReviewBiomolecules & biomedicine2025

Transesophageal echocardiography: Revolutionizing perioperative cardiac care.

Jiuqing Liang, Xiaoyu Ma, Genqiang Liang

Abstract readReview
In one paragraph

Review in Biomolecules & biomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

3 authors.

Jiuqing LiangDepartment of Anesthesiology, Zhuhai People's Hospital (Zhuhai Hospital affiliated with Jinan University), Zhuhai, China.
Xiaoyu MaDepartment of Anesthesiology, Zhuhai People's Hospital (Zhuhai Hospital affiliated with Jinan University), Zhuhai, China.
Genqiang LiangDepartment of Anesthesiology, Zhuhai People's Hospital (Zhuhai Hospital affiliated with Jinan University), Zhuhai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular diseases (CVDs) are a major challenge in global health. Despite significant advances in treatment and management, the incidence and mortality rates of CVDs have been rising in recent years, particularly in the United States. With continuous advancements in medical technology, perioperative transesophageal echocardiography (TEE) has become a key technology in cardiac surgery, enhancing surgical success rates and patient safety. The application of TEE spans preoperative planning, intraoperative monitoring, and postoperative evaluation, especially in complex procedures such as mitral valve repair and aortic valve replacement, where it plays an indispensable role. Simultaneously, the introduction of artificial intelligence (AI) brings new prospects for TEE image analysis and diagnostic support, significantly improving diagnostic accuracy and real-time decision-making capabilities. However, the application of TEE technology faces challenges such as high costs, uneven technological diffusion, and the high skill requirements for medical personnel. Therefore, establishing standardized training protocols and strengthening multidisciplinary collaboration are crucial. This paper reviews the application of TEE in cardiac surgery and its path toward educational and practical standardization from a global perspective, emphasizing its importance in improving the postoperative quality of life for patients and exploring future directions in technological innovation and educational optimization.

Indexed as

Cardiac Surgical ProceduresCardiovascular DiseasesEchocardiography, TransesophagealPerioperative CareArtificial IntelligenceHumans

Identifiers

PMID39151094
PMCPMC11734828

What OpenQuestion holds

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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 OpenQuestion graph.