Evidence map›Paper›PMID 39548240›Full record

ArticleScientific reports2024

Early prediction of acute-on-chronic liver failure development in patients with diverse chronic liver diseases.

Yuqiang Shen, Wan Xu, Yang Chen, Shengfen Wen, Qijiong Chen, Shanna Liu, Xinjian Zhu, Ling-Ling Tang, Li Li, Bin Ju

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
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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

10 authors.

Yuqiang Shen *Department of Information Technology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
Wan Xu *Hangzhou Xiaoshan District Center for Disease Control and Prevention (Hangzhou Xiaoshan District Health Supervision Institute), Hangzhou, China.
Yang ChenDepartment of Information Technology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
Shengfen WenSanOmics AI Co., Ltd, Hangzhou, China.
Qijiong ChenHangzhou Xiaoshan District Center for Disease Control and Prevention (Hangzhou Xiaoshan District Health Supervision Institute), Hangzhou, China.
Shanna LiuDepartment of Information Technology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
Xinjian ZhuDepartment of Information Technology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
Ling-Ling TangDepartment of Infectious Diseases, Key Laboratory of Artificial Organs and Computational Medicine of Zhejiang Province, Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, China. lingling.tang@shulan.com.
Li LiDepartment of Hepatobiliary Surgery, The First People's Hospital of Kunming, Kunming, China. lilikm26@163.com.
Bin JuSanOmics AI Co., Ltd, Hangzhou, China. Jubin_hz@163.com.

Funding

Key R&D Program of Zhejiang 2023C03101National Key R&D Program of China 2022YFC3602000the Spring City Plan: the High-level Talent Promotion and Training Project of Kunming 2022SCP002
6 · The paper itself

Abstract

Acute-on-chronic liver failure (ACLF) is a syndrome characterized by the acute decompensation of chronic liver disease, resulting in organ failure and high short-term mortality. The progression of ACLF is dynamic and reversible in a considerable proportion of patients during hospitalization. Early detection and accurate assessment of ACLF are essential; however, ideal methods for this purpose are still lacking. Therefore, this study aimed to develop a new score for predicting the onset of ACLF in patients with various chronic liver diseases.A total of 6,188 patients with various chronic liver diseases were included in the study. Clinical and laboratory data were collected, and the occurrence of ACLF within 28 days was recorded. The Lasso-Cox regression method was employed to develop prediction models for the onset of ACLF at 7, 14, and 28 days. Among 5,221 patients without ACLF, 477 progressed to ACLF within 28 days. Seven predictors were identified as significantly associated with the occurrence of ACLF at 7, 14, and 28 days. A new scoring system was developed as follows: [NEUT ≥ 7, 10

Indexed as

Acute-On-Chronic Liver FailureAdultAgedChronic DiseaseDisease ProgressionFemaleHumansLiver DiseasesMaleMiddle AgedPrognosisROC CurveACLFDiverse chronic liver diseasesPrediction modelProgressive score

Identifiers

PMID39548240
PMCPMC11568263

What OpenQuestion holds

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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.