Evidence map›Paper›PMID 35494424›Full record

ReviewPractical laboratory medicine2022

Improving acute kidney injury diagnostic precision using biomarkers.

Denise Hasson, Shina Menon, Katja M Gist

Open access · goldAbstract readReview
In one paragraph

Review in Practical laboratory medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
3.5field-weighted citation impact, top 6% 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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Denise HassonDepartment of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA.
Shina MenonDepartment of Pediatrics, Seattle Children's Hospital, University of Washington School of Medicine, Seattle, WA, USA.
Katja M GistDepartment of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA.
Cincinnati Children's Hospital Medical Center · USSeattle Children's Hospital · US

Funding

RESEARCH TRAINING IN PEDIATRIC NEPHROLOGYT32DK007695 · NIDDK · CINCINNATI CHILDRENS HOSP MED CTR · PI DEVARAJAN, PRASAD · 1992 to 2023
$2.2M
NIDDK NIH HHS T32 DK007695
6 · The paper itself

Abstract

Acute kidney injury (AKI) is common in hospitalized patients of all ages and is associated with significant morbidity and mortality. Accurate prediction and early identification of AKI is of utmost importance because no therapy exists to mitigate AKI once it has occurred. Yet, serum creatinine lacks adequate sensitivity and specificity, and quantification of urine output is challenging in incontinent children without indwelling bladder catheters. Integration of clinically available biomarkers have the potential to delineate unique AKI phenotypes that could have important prognostic and therapeutic implications. Plasma Cystatin C, urine neutrophil gelatinase associated lipocalin (NGAL) and the urinary product of tissue inhibitor metalloproteinase (TIMP-2) and insulin growth factor binding protein-7 (IGFBP7) are clinically available. These biomarkers have been studied in heterogenous populations across the age spectrum and in a variety of clinical settings for prediction of AKI. The purpose of this review is to describe and discuss the clinically available AKI biomarkers including how they have been used to delineate AKI phenotypes.

Indexed as

Acute kidney injuryBiomarkersPhenotypePrecision medicine

Identifiers

PMID35494424
PMCPMC9046880
OpenAlexW4225319029

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.