Evidence map›Paper›PMID 35938229›Full record

Trial reportDiabetes/metabolism research and reviews2022

A novel model for detecting advanced fibrosis in patients with nonalcoholic fatty liver disease.

Xinyu Yang, Mingfeng Xia, Xinxia Chang, Xiaopeng Zhu, Xiaoyang Sun, Yinqiu Yang, Liu Wang, Qiling Liu, Yuying Zhang, Yanlan Xu and 8 more

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Diabetes/metabolism research and reviews, 2022. 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
–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

5 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Review
  5. Article
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

18 authors.

Xinyu YangDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
Mingfeng XiaDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
Xinxia ChangDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
Xiaopeng ZhuDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
Xiaoyang SunDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
Yinqiu YangDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
Liu WangDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
Qiling LiuDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.ORCID 0000-0002-9412-5001
Yuying ZhangDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
Yanlan XuDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
Huandong LinDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
Lin LiuDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
Xiuzhong YaoDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Xiqi HuDepartment of Pathology, Shanghai Medical College, Fudan University, Shanghai, China.
Jian GaoDepartment of Clinical Nutrition, Zhongshan Hospital, Center of Clinical Epidemiology, EBM of Fudan University, Fudan University, Shanghai, China.
Hongmei YanDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.ORCID 0000-0001-7341-4368
Xin GaoDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.ORCID 0000-0003-1864-7796
Hua BianDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.ORCID 0000-0001-8449-0665

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsThe study aimed to develop a novel noninvasive model to detect advanced fibrosis based on routinely available clinical and laboratory tests. MATERIALS AND

methodsA total of 309 patients who underwent liver biopsy were randomly divided into the estimation group (n = 201) and validation group (n = 108). The model was developed using multiple regression analysis in the estimation group and further verified in the validation group. Diagnostic accuracy was evaluated using the receiver operating characteristic (ROC) curve.

resultsThe model was named NAFLD Fibrosis Index (NFI): -10.844 + 0.046 × age - 0.01 × platelet count + 0.19 × 2h postprandial plasma glucose (PG) + 0.294 × conjugated bilirubin - 0.015 × ALT + 0.039 × AST + 0.109 × total iron binding capacity -0.033 × parathyroid hormone (PTH). The area under the ROC curve (AUC) of NFI was 0.86 (95% CI: 0.79-0.93, p < 0.001) in the estimation group and 0.80 (95% CI: 0.69-0.91, p < 0.001) in the validation group, higher than NFS, FIB4, APRI, and BARD, and similar to FibroScan (NFI AUC = 0.77, 95% CI: 0.66-0.89, p = 0.001 vs. FibroScan AUC = 0.76, 95% CI: 0.62-0.90, p = 0.002). By applying the low cut-off value (-2.756), advanced fibrosis could be excluded among 49.3% and 48% of patients in the estimation group (sensitivity: 93.1%, NPV: 97.9%, specificity: 55.2%, and PPV: 26.0%) and validation group (sensitivity: 81.3%, NPV: 94.2%, specificity: 53.3%, and PPV: 23.2%), respectively, allowing them to avoid liver biopsy.

conclusionsThe study has established a novel model for advanced fibrosis, the diagnostic accuracy of which is superior to the current clinical scoring systems and is similar to FibroScan.

Indexed as

Non-alcoholic Fatty Liver DiseaseAlanine TransaminaseAspartate AminotransferasesBiopsyHumansInfant, NewbornLiverLiver CirrhosisPredictive Value of TestsROC CurveSeverity of Illness IndexAlanine TransaminaseAspartate Aminotransferasesadvanced fibrosisdiagnosislogistic modelsnonalcoholic fatty liver disease

Identifiers

PMID35938229
PMCPMC9788169

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.