Evidence map›Paper›PMID 38223620›Full record

ArticleAfrican health sciences2023

The predictive value of eGFR combined with BNP detection in acute kidney injury after acute myocardial infarction.

Chen-Yu Geng, Fang-Ze Wang, Rui Zhang, Yan-Yu Liu, Jian Wang

Open access · diamondAbstract read
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Article in African health sciences, 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
0.4field-weighted citation impact, top 29% of its field
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 citations in OpenAlex.

  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

5 authors at 2 institutions in 1 country.

Chen-Yu GengSchool of Clinical Medicine, Weifang Medical University, Weifang 261000, China.
Fang-Ze WangDepartment of Cardiology, Weifang People's Hospital, Weifang 261000, China.
Rui ZhangDepartment of Cardiology, Weifang People's Hospital, Weifang 261000, China.
Yan-Yu LiuSchool of Clinical Medicine, Weifang Medical University, Weifang 261000, China.
Jian WangDepartment of Cardiology, Weifang People's Hospital, Weifang 261000, China.
Weifang People's Hospital · CNWeifang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The combined detection of eGFR and BNP may provide some value in predicting the occurrence of AKI after AMI, and the study is designed to propose and validate this hypothesis. Methods: In retrospective research, AMI patients hospitalized at Weifang People's Hospital from January to December 2020 were included. Whether AKI occurred within a week of admission, patients were divided into two groups. Clinical data from two groups of patients were collected, and the Logistic regression model analysed the risk factors for AKI after AMI. The association between eGFR and BNP was analysed using Pearson linear correlation. The predictive value of eGFR and BNP alone and combined detection on AKI after AMI was analysed using the receiver operating characteristic (ROC) curve. Results: Multivariate logistic regression showed that eGFR, BNP, HDLC, UA, and K ions were AKI risk factors (P < 0.05). The eGFR correlates negatively with BNP (R = -0.324, P < 0.05). The area under the curve (AUC) of eGFR and BNP alone and combined prediction for post-AMI AKI were 0.793, 0.826, and 0.831, respectively. Conclusion: The combined detection of eGFR and BNP has a high predictive value for AKI development in AMI patients.

Indexed as

Acute Kidney InjuryMyocardial InfarctionHospitalizationHumansRetrospective StudiesRisk FactorsROC CurveAKIAMIBNPeGFRpredictive value

Identifiers

PMID38223620
PMCPMC10782295
OpenAlexW4387584827

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.