Evidence map›Paper›PMID 35971094›Full record

ArticleBMC nephrology2022

Prediction model of delayed graft function based on clinical characteristics combined with serum IL-2 levels.

Shitao Zhao, Yuan Liu, Chen Zhou, Zide Chen, Zeyu Cai, JiaLiang Han, Jiansheng Xiao, Qi Xiao

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC nephrology, 2022. 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
1.3field-weighted citation impact, top 23% 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, 6 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

8 authors at 2 institutions in 1 country.

Shitao Zhao *Department of Transplantation, The First Affiliated Hospital of Nanchang University, 17 Yongwai Zhengjie Street, Nanchang, 330006, Jiangxi, China.
Yuan Liu *Department of Transplantation, The First Affiliated Hospital of Nanchang University, 17 Yongwai Zhengjie Street, Nanchang, 330006, Jiangxi, China.
Chen Zhou *Department of Transplantation, The First Affiliated Hospital of Nanchang University, 17 Yongwai Zhengjie Street, Nanchang, 330006, Jiangxi, China.
Zide ChenDepartment of Transplantation, The First Affiliated Hospital of Nanchang University, 17 Yongwai Zhengjie Street, Nanchang, 330006, Jiangxi, China.
Zeyu CaiDepartment of Transplantation, The First Affiliated Hospital of Nanchang University, 17 Yongwai Zhengjie Street, Nanchang, 330006, Jiangxi, China.
JiaLiang HanDepartment of Transplantation, The First Affiliated Hospital of Nanchang University, 17 Yongwai Zhengjie Street, Nanchang, 330006, Jiangxi, China.
Jiansheng XiaoDepartment of Transplantation, The First Affiliated Hospital of Nanchang University, 17 Yongwai Zhengjie Street, Nanchang, 330006, Jiangxi, China. super_xiaoj@163.com.
Qi XiaoDepartment of Transplantation, The First Affiliated Hospital of Nanchang University, 17 Yongwai Zhengjie Street, Nanchang, 330006, Jiangxi, China. xiaoqidoctor@163.com.
First Affiliated Hospital of Nanchang University · CNNanchang University · CN

Funding

Identifying Approaches to Measure and Reduce Harmful, Low-Value Care Among Older Americans with Mild Cognitive Impairment and DementiaR01AG070017 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI MAFI, JOHN N. · 2021 to 2025
$2.6M
NIA NIH HHS R01 AG070017
6 · The paper itself

Abstract

backgroundKidney transplantation is an effective treatment for end-stage renal disease (ESRD). Delayed graft function (DGF) is a common complication after kidney transplantation and exerts substantial effects on graft function and long-term graft survival. Therefore, the construction of an effective model to predict the occurrence of DGF is particularly important.

methodsSeventy-one patients receiving their first kidney transplant at the First Affiliated Hospital of Nanchang University from October 2020 to October 2021 were enrolled in the discovery cohort. Based on clinical characteristics and serum markers, a logistic regression model was used to simulate the risk of DGF in the discovery cohort. The DGF prediction model was named the prediction system and was composed of risk factors related to DGF. Thirty-two patients receiving a kidney transplant at the First Affiliated Hospital of Nanchang University from October 2021 to February 2022 were enrolled in the validation cohort. The validation cohort was used to verify the accuracy and reliability of the prediction model.

resultsCold ischemia time (CIT), donor history of diabetes mellitus, donor interleukin-2 (IL-2) level and donor terminal creatinine level constitute the prediction system. In the validation test, the area under the receiver operating characteristic curve (AUC) was 0.867 for the prediction system, and good calibration of the model was confirmed in the validation cohort.

conclusionsThis study constructed a reliable and highly accurate prediction model that provides a practical tool for predicting DGF. Additionally, IL-2 participates in the kidney injury process and may be a potential marker of kidney injury.

Indexed as

Delayed Graft FunctionKidney TransplantationBiomarkersGraft SurvivalHumansInterleukin-2Reproducibility of ResultsRisk FactorsTissue DonorsBiomarkersInterleukin-2Cold ischemia timeCreatinineDelayed graft functionDiabetesInterleukin-2Kidney transplantation

Identifiers

PMID35971094
PMCPMC9377118
OpenAlexW4291884213

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.