Evidence map›Paper›PMID 31079110›Full record

ArticleBlood purification2019

Plasma Biomarkers in Predicting Renal Recovery from Acute Kidney Injury in Critically Ill Patients.

Marco Fiorentino, Fadi A Tohme, Raghavan Murugan, John A Kellum

Open access · bronzeAbstract read
In one paragraph

Article in Blood purification, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.6field-weighted citation impact, top 34% 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

7 citing papers in PubMed, 15 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

4 authors at 1 institution in 2 countries.

Marco FiorentinoThe Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Fadi A TohmeThe Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Raghavan MuruganThe Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
John A KellumThe Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA, Kellum@pitt.edu.
University of Pittsburgh · US

Funding

Biological Markers of Recovery for the KidneyR01DK070910 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KELLUM, JOHN A · 2006 to 2010
$1.8M
NIDDK NIH HHS R01 DK070910NIDDK NIH HHS Y01 DK003508
6 · The paper itself

Abstract

backgroundNumerous studies have suggested a possible role for acute kidney injury (AKI) biomarkers in predicting renal recovery both before and after renal replacement therapy (RRT). However, definitions for recovery and whether to include patients dying but free of RRT may influence results.

objectivesTo validate plasma neutrophil gelatinase-associated lipocalin (pNGAL) as a useful biomarker for predicting or improving the ability of clinical predictors alone to predict recovery following AKI, including in our model plasma B-type natriuretic peptide (pBNP) to account for cardiovascular events.

methodsWe analyzed 69 patients enrolled in the Acute Renal Failure Trial Network study. pNGAL and pBNP were measured on days 2, 7, and 14. We analyzed their predictive ability for subsequent recovery, defined as alive and independent from dialysis in 60 days. In sensitivity analyses, we explored changes in results with alternative definitions of recovery.

resultsTwenty-nine patients (42%) recovered from AKI. Neither pNGAL nor pBNP, alone or in combination, was accurate predictors of renal recovery-the best area under the receiver-operating characteristics curve (AUC) was for pNGAL using the largest relative change (AUC 0.59, 95% CI 0.45-0.74). The best clinical model achieved superior performance to biomarkers (AUC 0.69, 95% CI 0.56-0.81). The AUC was greatest (0.75, 95% CI 0.60-0.91) when pNGAL + pBNP on day 14 were added to the clinical model but this increase did not achieve statistical significance. However, integrated discrimination improvement analysis showed that the addition of pNGAL and pBNP on day 14 to the clinical model significantly improved the prediction of renal recovery (p = 0.008).

conclusionspNGAL and pBNP can improve the accuracy of clinical parameters in predicting AKI recovery and a full model using biomarkers together with age achieved adequate discrimination.

Indexed as

Recovery of FunctionAcute Kidney InjuryAdultAgedBiomarkersFemaleHumansKidneyLipocalin-2MaleMiddle AgedNatriuretic Peptide, BrainPredictive Value of TestsRenal Replacement TherapyBiomarkersLipocalin-2Natriuretic Peptide, BrainPro-BNP1-108Acute kidney injuryBiomarkersNeutrophil gelatinase-associated lipocalinRenal recoveryRenal replacement therapy

Identifiers

PMID31079110
PMCPMC6773527
OpenAlexW2944030127

What OpenQuestion holds

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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.