Evidence map›Paper›PMID 37796371›Full record

Trial reportInternal and emergency medicine2024

Development and validation of prognostic nomogram for cirrhotic patients with acute kidney injury upon ICU admission.

Tianbao Liao, Yanyan Lu, Tingting Su, Liyuan Bei, Xian Li, Yang Lu, Shuang Ren, Lina Huang, Lu-Huai Feng

Abstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in Internal and emergency medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-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

6 citing papers in PubMed, 6 citations in OpenAlex.

  1. Bridging Critically Ill Patients With Cirrhosis to Transplant With Renal Replacement Therapy: A Multicenter Cohort Study.Liver international : official journal of the International Association for the Study of the Liver · 2026
    Observational
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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

9 authors at 4 institutions in 1 country.

Tianbao Liao *Department of President's Office, Youjiang Medical University for Nationalities, Baise, China.
Yanyan Lu *Department of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, China.
Tingting SuDepartment of ECG Diagnostics, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Liyuan BeiDepartment of Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Xian LiDepartment of Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Yang LuDepartment of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, China.
Shuang RenDepartment of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, China.
Lina HuangDepartment of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, China.
Lu-Huai FengDepartment of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, China. 823900187@qq.com.ORCID 0000-0002-1715-4028
Tumor Hospital of Guangxi Medical University · CNGuangxi Medical University · CNAffiliated Hospital of Youjiang Medical University for Nationalities · CNThe People's Hospital of Guangxi Zhuang Autonomous Region · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to develop and validate a prognostic nomogram that accurately predicts the short-term survival rate of cirrhotic patients with acute kidney damage (AKI) upon ICU admission. For this purpose, we examined the admission data of 3060 cirrhosis patients with AKI from 2008 to 2019 in the MIMIC-IV database. All included patients were randomly assigned to derivation and validation cohorts in a 7:3 ratio. The derivation cohort used the least absolute shrinkage and selection operator (LASSO) regression model to identify independent predictors of AKI. A prognostic nomogram was constructed via multivariate logistic regression analysis in the derivation cohort and subsequently verified in the validation cohort. Nomogram's discrimination, calibration, and clinical utility were evaluated using the C-index, calibration plot, and decision curve analysis (DCA). A total of 2138 patients were enrolled in the derivation cohort, with a median follow-up period of 15 days, a median survival time of 41 days, and a death rate of 568 patients (26.6%). The cumulative survival rates at 15 and 30 days were 75.8% and 57.5%, respectively. The results of the multivariate analysis indicated that advanced AKI stage, use of vasoactive drugs, advanced age, lower levels of ALB, lower mean sBp, longer INR, and longer PT were all independent risk factors that significantly influenced the all-cause mortality of cirrhosis patients with AKI (all p < 0.01). The C-indices for the derivation and the validation cohorts were 0.821 (95% CI 0.800-0.842) and 0.831 (95% CI 0.810-0.852), respectively. The model's calibration plot demonstrated high consistency between predicted and actual probabilities. Furthermore, the DCA showed that the nomogram was clinically valuable. Therefore, the developed and internally validated prognostic nomogram exhibited favorable discrimination, calibration, and clinical utility in forecasting the 15-day and 30-day survival rates of cirrhosis patients with AKI upon admission to the ICU.

Indexed as

Acute Kidney InjuryNomogramsHumansIntensive Care UnitsLiver CirrhosisPrognosisAcute kidney injuryIntensive care unitModelNomogramPrognosis

Identifiers

PMID37796371
OpenAlexW4387357919

What OpenQuestion holds

Textmetadata
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.