Evidence map›Paper›PMID 37055509›Full record

SynthesisScientific reports2023

Predictive value of urinary cell cycle arrest biomarkers for all cause-acute kidney injury: a meta-analysis.

Feng Huang, Yan Zeng, Linghai Lv, Yaoyao Chen, Yan Yan, Laimin Luo, Rong Pan, Jiaming Jiang, Xin Wei

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
2.8field-weighted citation impact, top 9% 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
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  11. Article
  12. Review
  13. Early Diagnosis of Pediatric Acute Kidney Injury: An Achievable Goal?Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2024
    Article
  14. Biomarkers in acute kidney injury.Annals of intensive care · 2024
    Review
  15. Review
  16. Observational
  17. 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

9 authors at 2 institutions in 1 country.

Feng Huang *Department of Nephrology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.
Yan Zeng *Department of Nephrology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.
Linghai LvDepartment of Nephrology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.
Yaoyao ChenNanchang University, Nanchang, 330006, China.
Yan YanDepartment of Nephrology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.
Laimin LuoDepartment of Nephrology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.
Rong PanDepartment of Nephrology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.
Jiaming JiangDepartment of Nephrology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.
Xin WeiDepartment of Nephrology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China. weixin842270@126.com.
Nanchang University · CNFirst Affiliated Hospital of Nanchang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The cell cycle arrest markers tissue inhibitor metalloproteinases-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7) have been identified as potential biomarkers of acute kidney injury (AKI) in critically ill adults in intensive care units and cardiac surgery-associated AKI (CSA-AKI). However, the clinical impact on all-cause AKI remains unclear. Here, we report a meta-analysis performed to evaluate the predictive value of this biomarker for all-cause AKI. The PubMed, Cochrane, and EMBASE databases were systematically searched up to April 1, 2022. We used the Quality Assessment Tool for Diagnosis Accuracy Studies (QUADAS-2) to assess the quality. We extracted useful information from these studies and calculated the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Twenty studies with 3625 patients were included in the meta-analysis. The estimated sensitivity of urinary [TIMP-2] × [IGFBP7] in the diagnosis of all-cause AKI was 0.79 (95% CI 0.72, 0.84), and the specificity was 0.70 (95% CI 0.62, 0.76). The value of urine [TIMP-2] × [IGFBP7] in the early diagnosis of AKI was assessed using a random effects model. The pooled positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 2.6 (95% CI 2.1, 3.3), 0.31 (95% CI 0.23, 0.40), and 8 (95% CI 6, 13), respectively. The AUROC was 0.81 (95% CI 0.78-0.84). No significant publication bias was observed in eligible studies. Subgroup analysis indicated that the diagnostic value was related to the severity of AKI, time measurement, and clinical setting. This study shows that urinary [TIMP-2] × [IGFBP7] is a reliable effective predictive test for all cause-AKI. However, whether and how urinary [TIMP-2] × [IGFBP7] can be used in clinical diagnosis still requires further research and clinical trials.

Indexed as

Acute Kidney InjuryTissue Inhibitor of Metalloproteinase-2BiomarkersCell Cycle CheckpointsHumansInsulin-Like Growth Factor Binding ProteinsROC CurveBiomarkersInsulin-Like Growth Factor Binding ProteinsTissue Inhibitor of Metalloproteinase-2

Identifiers

PMID37055509
PMCPMC10102152
OpenAlexW4365452229

What OpenQuestion 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 OpenQuestion graph.