Evidence map›Paper›PMID 37408816›Full record

ArticleJournal of clinical and translational hepatology2023

Liver Stiffness Measurement can Predict Liver Inflammation in Chronic Hepatitis B Patients with Normal Alanine Transaminase.

Ling-Ling Huang, Xue-Ping Yu, Qing-Fa Ruan, Yan-Xue Lin, Huan Li, Wen Jin, Rui-Feng Liu, Yan-Lan Liang, Yu-Rui Liu, Yue-Yong Zhu and 3 more

Open access · diamondAbstract read
In one paragraph

Article in Journal of clinical and translational hepatology, 2023. 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
0.9field-weighted citation impact, top 26% 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

4 citing papers in PubMed, 4 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

13 authors at 2 institutions in 1 country.

Ling-Ling HuangDepartment of Hepatology, Hepatology Research Institute, The First Affiliated hospital, Fujian Medical University, Fuzhou, Fujian, China.
Xue-Ping YuDepartment of Infectious Diseases, The First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, Fujian, China.
Qing-Fa RuanHepatology Center, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, Fujian, China.
Yan-Xue LinDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Huan LiHepatology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Wen JinHepatology, Fujian Medical University Xiamen Humanity Hospital, Xiamen, Fujian, China.
Rui-Feng LiuDepartment of Hepatology, Hepatology Research Institute, The First Affiliated hospital, Fujian Medical University, Fuzhou, Fujian, China.
Yan-Lan LiangDepartment of Infectious Diseases, The First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, Fujian, China.
Yu-Rui LiuDepartment of Hepatology, Hepatology Research Institute, The First Affiliated hospital, Fujian Medical University, Fuzhou, Fujian, China.
Yue-Yong ZhuDepartment of Hepatology, Hepatology Research Institute, The First Affiliated hospital, Fujian Medical University, Fuzhou, Fujian, China.
Jia-Ji JiangDepartment of Hepatology, Hepatology Research Institute, The First Affiliated hospital, Fujian Medical University, Fuzhou, Fujian, China.
Ri-Cheng MaoDepartment of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.ORCID https://orcid.org/0000-0001-5534-8299
Da-Wu ZengDepartment of Hepatology, Hepatology Research Institute, The First Affiliated hospital, Fujian Medical University, Fuzhou, Fujian, China.ORCID https://orcid.org/0000-0003-3818-0062
Fujian Medical University · CNFudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: To determine whether liver stiffness measurement (LSM) indicates liver inflammation in chronic hepatitis B (CHB) with different upper limits of normal (ULNs) for alanine aminotransferase (ALT). Methods: We grouped 439 CHB patients using different ULNs for ALT: cohort I, ≤40 U/L (439 subjects); cohort II, ≤35/25 U/L (males/females; 330 subjects); and cohort III, ≤30/19 U/L (males/females; 231 subjects). Furthermore, 84 and 96 CHB patients with normal ALT (≤40 U/L) formed the external and prospective validation groups, respectively. We evaluated the correlation between LSM and biopsy-confirmed liver inflammation, and determined diagnostic accuracy using area under the curve (AUC). A noninvasive LSM-based model was developed using multivariate logistic regression. Results: Fibrosis-adjusted LSM values significantly increased with increasing inflammation. The AUCs of LSM in cohorts I, II, and III were 0.799, 0.796, and 0.814, respectively, for significant inflammation (A≥2) and 0.779, 0.767, and 0.770, respectively, for severe inflammation (A=3). Cutoff LSM values in all cohorts for A≥2 and A=3 were 6.3 and 7.5 kPa, respectively. Internal, external, and prospective validations showed high diagnostic accuracy of LSM for A≥2 and A=3, and no significant differences in AUCs among the four groups. LSM and globulin independently predicted A≥2. The AUC of an LSM-globulin model for A≥2 exceeded those of globulin, ALT, and AST, but was similar to that of LSM. Conclusions: LSM predicted liver inflammation and guided the indication of antiviral therapy for CHB in patients with normal ALT.

Indexed as

Alanine aminotransferaseFibrosisHepatitis B virusLiver inflammationLiver stiffness measurement

Identifiers

PMID37408816
PMCPMC10318296
OpenAlexW4317208883

What Socratic holds

Textmetadata
LicenceCC BY-NC
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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 Socratic graph.