Evidence map›Paper›PMID 31478958›Full record

ArticleClinical and translational gastroenterology2019

Development and Validation of a Model to Predict Acute Kidney Injury in Hospitalized Patients With Cirrhosis.

Kavish R Patidar, Chenjia Xu, Hani Shamseddeen, Yao-Wen Cheng, Marwan S Ghabril, V V Pavan K Mukthinuthalapati, Zachary P Fricker, Samuel Akinyeye, Lauren D Nephew, Archita P Desai and 4 more

Open access · goldAbstract readValidation Study
In one paragraph

Article in Clinical and translational gastroenterology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
1.4field-weighted citation impact, top 19% 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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 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

14 authors at 6 institutions in 1 country.

Kavish R PatidarDivision of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Chenjia XuDepartment of Biostatistics, Indiana University, Indianapolis, Indiana, USA.
Hani ShamseddeenDivision of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Yao-Wen ChengHospital Medicine, California Pacific Medical Center, San Francisco, California, USA.
Marwan S GhabrilDivision of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
V V Pavan K MukthinuthalapatiDepartment of Medicine, Cook County Health and Hospitals System, Chicago, Illinois, USA.
Zachary P FrickerEvans Department of Medicine, Section of Gastroenterology, Boston University School of Medicine, Boston, Massachusetts, USA.
Samuel AkinyeyeSection of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, Texas, USA.
Lauren D NephewDivision of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Archita P DesaiDivision of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Melissa AndersonDivision of Nephrology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Tarek M El-AchkarDivision of Nephrology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Naga P ChalasaniDivision of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Eric S OrmanDivision of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Indiana University – Purdue University Indianapolis · USIndiana University School of MedicineBaylor College of Medicine · USBoston University · USCalifornia Pacific Medical Center · USCook County Health and Hospitals System · US

Funding

Indiana Clinical and Translational Sciences InstituteKL2TR001106 · NCATS · INDIANA UNIVERSITY INDIANAPOLIS · PI DENNE, SCOTT C., SHEKHAR, ANANTHA · 2013 to 2017
$3.2M
Unplanned Early Readmissions of Hospitalized Patients with CirrhosisK23DK109202 · NIDDK · INDIANA UNIVERSITY INDIANAPOLIS · PI ORMAN, ERIC · 2016 to 2021
$811k
NCATS NIH HHS KL2 TR001106NIDDK NIH HHS K23 DK109202
6 · The paper itself

Abstract

objectivesAcute kidney injury (AKI) is a common complication in hospitalized patients with cirrhosis which contributes to morbidity and mortality. Improved prediction of AKI in this population is needed for prevention and early intervention. We developed a model to identify hospitalized patients at risk for AKI.

methodsAdmission data from a prospective cohort of hospitalized patients with cirrhosis without AKI on admission (n = 397) was used for derivation. AKI development in the first week of admission was captured. Independent predictors of AKI on multivariate logistic regression were used to develop the prediction model. External validation was performed on a separate multicenter cohort (n = 308).

resultsIn the derivation cohort, the mean age was 57 years, the Model for End-Stage Liver Disease score was 17, and 59 patients (15%) developed AKI after a median of 4 days. Admission creatinine (OR: 2.38 per 1 mg/dL increase [95% CI: 1.47-3.85]), international normalized ratio (OR: 1.92 per 1 unit increase [95% CI: 1.92-3.10]), and white blood cell count (OR: 1.09 per 1 × 10/L increase [95% CI: 1.04-1.15]) were independently associated with AKI. These variables were used to develop a prediction model (area underneath the receiver operator curve: 0.77 [95% CI: 0.70-0.83]). In the validation cohort (mean age of 53 years, Model for End-Stage Liver Disease score of 16, and AKI development of 13%), the area underneath the receiver operator curve for the model was 0.70 (95% CI: 0.61-0.78). DISCUSSION: A model consisting of admission creatinine, international normalized ratio, and white blood cell count can identify patients with cirrhosis at risk for in-hospital AKI development. On further validation, our model can be used to apply novel interventions to reduce the incidence of AKI among patients with cirrhosis who are hospitalized.

Indexed as

Models, StatisticalAcute Kidney InjuryAgedFemaleForecastingHospitalizationHumansLiver CirrhosisMaleMiddle AgedProspective StudiesRisk Assessment

Identifiers

PMID31478958
PMCPMC6775340
OpenAlexW2971635659

What OpenQuestion 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 OpenQuestion graph.