Evidence map›Paper›PMID 37900003›Full record

ArticleKidney diseases (Basel, Switzerland)2023

Defining Biventricular Abnormalities by Cardiac Magnetic Resonance in Pre-Dialysis Patients with Chronic Kidney Disease.

Li Qi, Beibei Zhi, Jun Zhang, Lingyan Zhang, Song Luo, Longjiang Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Kidney diseases (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Li QiDepartment of Radiology, Jinling Hospital, Nanjing Medical University, Nanjing, China.
Beibei ZhiDepartment of Radiology, Jinling Hospital, Nanjing Medical University, Nanjing, China.
Jun ZhangDepartment of Radiology, Jinling Hospital, Nanjing Medical University, Nanjing, China.
Lingyan ZhangDepartment of Radiology, Jinling Hospital, Nanjing Medical University, Nanjing, China.
Song LuoDepartment of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
Longjiang ZhangDepartment of Radiology, Jinling Hospital, Nanjing Medical University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The aim of the study was to investigate biventricular structural and functional abnormalities in pre-dialysis patients across stages of chronic kidney disease (CKD) by cardiac magnetic resonance (CMR). Methods: Fifty-one CKD patients with CMR exams were retrospectively analyzed. Patients were divided into three groups according to estimated glomerular filtration rate (eGFR): CKD 1 group (patients with normal eGFR≥90 mL/min/1.73 m Results: There was an increasing trend in RV mass index (RVMi) and LV mass index (LVMi) with the occurrence and development of CKD from HC group to CKD 4-5 group although no significant difference was observed between CKD 1 group and HC group. LV global radial strain and LV global circumferential strain dropped and native T1 value elevated significantly in CKD 4-5 group compared with the other three groups (all Conclusion: In pre-dialysis CKD patients, left and right ventricular remolding has occurred. RVMi and LVMi were the first changed CMR indexes in the development of CKD when eGFR began to drop. Because fluid volume overload was the independent risk factor for RVMi and LVMi increase, reasonable controlling fluid volume overload may slow down the progression of biventricular remolding and may reduce related cardiovascular disease risk.

Indexed as

Cardiac magnetic resonanceChronic kidney diseaseLeft ventricleRight ventricle

Identifiers

PMID37900003
PMCPMC10601957

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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.