Evidence map›Paper›PMID 36991386›Full record

ArticleLipids in health and disease2023

Development and validation of an online dynamic nomogram based on the atherogenic index of plasma to screen nonalcoholic fatty liver disease.

Hewei Peng, Junchao Zhang, Xianhua Huang, Miao Xu, Jingru Huang, Yunli Wu, Xian-E Peng

Open access · goldAbstract read
In one paragraph

Article in Lipids in health and disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 17% 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, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
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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

7 authors at 2 institutions in 1 country.

Hewei Peng *Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Xuefu North Road 1St, Shangjie Town, Minhou Country, Fuzhou, 350108, Fujian, China.
Junchao Zhang *Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Xuefu North Road 1St, Shangjie Town, Minhou Country, Fuzhou, 350108, Fujian, China.
Xianhua HuangDepartment of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Xuefu North Road 1St, Shangjie Town, Minhou Country, Fuzhou, 350108, Fujian, China.
Miao XuDepartment of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Xuefu North Road 1St, Shangjie Town, Minhou Country, Fuzhou, 350108, Fujian, China.
Jingru HuangGrade 2022, Clinical Medicine Major, Integrated Chinese and Western medicine school, Fujian University of Traditional Chinese Medicine, 350108, Fuzhou, China.
Yunli WuKey Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China.
Xian-E PengDepartment of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Xuefu North Road 1St, Shangjie Town, Minhou Country, Fuzhou, 350108, Fujian, China. fmuxe@163.com.
Fujian Medical University · CNFujian University of Traditional Chinese Medicine · CN

Funding

National Natural Science Foundation of China 81473047Natural Science Foundation of Fujian Province 2019J01316
6 · The paper itself

Abstract

backgroundNonalcoholic fatty liver disease (NAFLD), a common liver disease worldwide, can be reversed early in life with lifestyle and medical interventions. This study aimed to develop a noninvasive tool to screen NAFLD accurately.

methodsRisk factors for NAFLD were identified using multivariate logistic regression analysis, and an online NAFLD screening nomogram was developed. The nomogram was compared with reported models (fatty liver index (FLI), atherogenic index of plasma (AIP), and hepatic steatosis index (HSI)). Nomogram performance was evaluated through internal and external validation (National Health and Nutrition Examination Survey (NHANES) database).

resultsThe nomogram was developed based on six variables. The diagnostic performance of the present nomogram for NAFLD (area under the receiver operator characteristic curve (AUROC): 0.863, 0.864, and 0.833, respectively) was superior to that of the HSI (AUROC: 0.835, 0.833, and 0.810, respectively) and AIP (AUROC: 0.782, 0.773, and 0.728, respectively) in the training, validation, and NHANES sets. Decision curve analysis and clinical impact curve analysis presented good clinical utility.

conclusionThis study establishes a new online dynamic nomogram with excellent diagnostic and clinical performance. It has the potential to be a noninvasive and convenient method for screening individuals at high risk for NAFLD.

Indexed as

Non-alcoholic Fatty Liver DiseaseHumansLiver Function TestsNomogramsNutrition SurveysRisk FactorsDynamic nomogramNonalcoholic fatty liver diseaseNoninvasive models

Identifiers

PMID36991386
PMCPMC10053077
OpenAlexW4361291980

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.