Evidence map›Paper›PMID 31305465›Full record

ArticleMedicine2019

A simple algorithm for non-invasive diagnosis of significant liver histological changes in patients with CHB and normal or mildly elevated alanine transaminase levels.

Qiang Li, Chenlu Huang, Wei Xu, Qiankun Hu, Liang Chen

Open access · goldAbstract readValidation Study
In one paragraph

Article in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 29% 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

5 citing papers in PubMed, 9 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

5 authors at 1 institution in 2 countries.

Qiang LiDepartment of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Chenlu Huang
Wei Xu
Qiankun Hu
Liang Chen
Shanghai Public Health Clinical Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significant liver histological changes (SLHC) were defined as moderate to severe liver inflammation (A2 or higher) and/or fibrosis (F2 or higher) using the METAVIR scoring system. This study aimed to develop an algorithm for the non-invasive detection of SLHC in patients with chronic hepatitis B (CHB) and normal or mildly elevated alanine transaminase (ALT) levels.Using liver histology as gold standard, we developed a simple algorithm for the diagnosis of SLHC in a training set (504 patients), and then validated the diagnostic accuracy in a validation set (166 patients).A new algorithm (AAG) attributed to age, ALT, and gamma-glutamyl transpeptidase (GGT) was developed. In the training set, the area under ROC curve (AUROC) of AAG was significantly higher than that of ALT, aspartate transaminase (AST), GPR, and APRI for the diagnosis of SLHC (0.74, 0.68, 0.65, 0.56, and 0.53, respectively; all P < .05). In the validation set, the AUROC of AAG was also higher than that of ALT, AST, GPR, and APRI (0.73, 0.65, 0.62, 0.62, and 0.61, respectively; all P < .05). Using AAG ≥ 2, the sensitivity and negative predictive value was 84% to 98% and 75% to 94%, respectively, for the diagnosis of SLHC. Using AAG ≥ 6, the specificity and positive predictive value was 93% to 97% and 67% to 79%, respectively, for the diagnosis of SLHC.The AAG algorithm represents a novel noninvasive method for the diagnosis of SLHC in CHB patients with normal or mildly elevated ALT levels.

Indexed as

AlgorithmsAdultAlanine TransaminaseBiomarkersBiopsyFemaleHepatitis B, ChronicHumansInflammationLiverLiver CirrhosisMaleRetrospective StudiesSensitivity and SpecificityAlanine TransaminaseBiomarkers

Identifiers

PMID31305465
PMCPMC6641827
OpenAlexW2961676072

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.