Evidence map›Paper›PMID 34032326›Full record

ArticleJournal of clinical laboratory analysis2021

A risk score based on procalcitonin for predicting acute kidney injury in COVID-19 patients.

Ruo Ran Wang, Min He, Yan Kang

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical laboratory analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.0field-weighted citation impact, top 20% 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

9 citing papers in PubMed, 16 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 1 institution in 1 country.

Ruo Ran WangDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
Min HeDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
Yan KangDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0002-5715-3900
Sichuan University · CN

Funding

Project of Novel Coronavirus Pneumonia in West China Hospital HX2019nCoV027
6 · The paper itself

Abstract

backgroundAcute kidney injury (AKI) has been reported developing commonly in coronavirus disease 2019 (COVID-19) patients and could increase the risk of poor outcomes in these patients. We design this study to explore the value of serum procalcitonin (PCT) on predicting AKI and construct risk score for predicting AKI in COVID-19 patients.

methodsPatients diagnosed with COVID-19 and hospitalized in Renmin Hospital of Wuhan University between January 30 and February 24, 2020, were included. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the strongest predictors of AKI. Multivariate logistic regression analysis was conducted to find independent risk factors for AKI and construct risk score using odds ratio (OR) value of those risk factors. Receiver operating characteristics (ROC) curves were plotted, and area under the ROC curve (AUC) value was calculated to evaluate the predictive value of single PCT level and the constructed risk score.

resultsAmong 389 included COVID-19 patients, 28 (7.2%) patients developed AKI. LASSO regression showed hypertension, saturation of arterial oxygen (SaO

conclusionPCT level is correlated with AKI in COVID-19 patients. The efficient risk score consisted of SaO

Indexed as

Acute Kidney InjuryCOVID-19AdultAgedAged, 80 and overBiomarkersChinaFemaleHumansMaleMiddle AgedProcalcitoninRisk AssessmentRisk FactorsSARS-CoV-2BiomarkersProcalcitoninacute kidney injurybiomarkerCOVID-19procalcitonin

Identifiers

PMID34032326
PMCPMC8183912
OpenAlexW3165520518

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.