Evidence map›Paper›PMID 35402050›Full record

ArticleInternational journal of hepatology2022

Urinary BA Indices as Prognostic Biomarkers for Complications Associated with Liver Diseases.

Wenkuan Li, Jawaher Abdullah Alamoudi, Nagsen Gautam, Devendra Kumar, Macro Olivera, Yeongjin Gwon, Sandeep Mukgerjee, Yazen Alnouti

Open access · goldAbstract read
In one paragraph

Article in International journal of hepatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 44% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. Review
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 3 institutions in 1 country.

Wenkuan LiDepartment of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center, Omaha, NE 68198, USA.ORCID https://orcid.org/0000-0002-0312-9083
Jawaher Abdullah AlamoudiDepartment of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center, Omaha, NE 68198, USA.ORCID https://orcid.org/0000-0003-1776-9249
Nagsen GautamDepartment of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center, Omaha, NE 68198, USA.ORCID https://orcid.org/0000-0001-5876-6760
Devendra KumarDepartment of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center, Omaha, NE 68198, USA.ORCID https://orcid.org/0000-0003-0119-6028
Macro OliveraDepartment of Internal Medicine, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA.ORCID https://orcid.org/0000-0003-1376-3739
Yeongjin GwonDepartment of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, USA.ORCID https://orcid.org/0000-0001-6974-337X
Sandeep MukgerjeeDepartment of Internal Medicine, College of Medicine, Creighton University Medical Center, Omaha, NE, USA.ORCID https://orcid.org/0000-0002-0538-3253
Yazen AlnoutiDepartment of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center, Omaha, NE 68198, USA.ORCID https://orcid.org/0000-0002-3995-3242
Nebraska Medical Center · USUniversity of Nebraska Medical Center · USCHI Health Creighton University Medical Center - Bergan Mercy · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatobiliary diseases and their complications cause the accumulation of toxic bile acids (BA) in the liver, blood, and other tissues, which may exacerbate the underlying condition and lead to unfavorable prognosis. To develop and validate prognostic biomarkers for the prediction of complications of cholestatic liver disease based on urinary BA indices, liquid chromatography-tandem mass spectrometry was used to analyze urine samples from 257 patients with cholestatic liver diseases during a 7-year follow-up period. The urinary BA profile and non-BA parameters were monitored, and logistic regression models were used to predict the prognosis of hepatobiliary disease-related complications. Urinary BA indices were applied to quantify the composition, metabolism, hydrophilicity, and toxicity of the BA profile. We have developed and validated the bile-acid liver disease complication (BALDC) model based on BA indices using logistic regression model, to predict the prognosis of cholestatic liver disease complications including ascites. The mixed BA and non-BA model was the most accurate and provided higher area under the receiver operating characteristic (ROC) and smaller akaike information criterion (AIC) values compared to both non-BA and MELD (models for end stage liver disease) models. Therefore, the mixed BA and non-BA model could be used to predict the development of ascites in patients diagnosed with liver disease at early stages of intervention. This will help physicians to make a better decision when treating hepatobiliary disease-related ascites.

Identifiers

PMID35402050
PMCPMC8986411
OpenAlexW4220688399

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.