ArticleDisease markers2020
Predicting Liver Disease Risk Using a Combination of Common Clinical Markers: A Screening Model from Routine Health Check-Up.
Article in Disease markers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- MetsObesity: a novel classification system for predicting 15-year cardiovascular risk in the UK Biobank population.Journal of advanced research · 2026Article
- Effects of drinking slightly acidic electrolyzed water on the growth health, blood physiology, and intestinal development in mice.Scientific reports · 2025Article
- Noninvasive diagnosis of AIH/PBC overlap syndrome based on prediction models.Open medicine (Warsaw, Poland) · 2022Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly detection is crucial for the prognosis of patients with autoimmune liver disease (AILD). Due to the relatively low incidence, developing screening tools for AILD remain a challenge.
aimsTo analyze clinical characteristics of AILD patients at initial presentation and identify clinical markers, which could be useful for disease screening and early detection.
methodsWe performed observational retrospective study and analyzed 581 AILD patients who were hospitalized in the gastroenterology department and 1000 healthy controls who were collected from health management center. Baseline characteristics at initial presentation were used to build regression models. The model was validated on an independent cohort of 56 patients with AILD and 100 patients with other liver disorders.
resultsAsymptomatic AILD individuals identified by the health check-up are increased yearly (from 31.6% to 68.0%,
conclusionScreening for AILD with described parameters can detect AILD in routine health check-up early, effectively and economically. Eight variables in routine health check-up are associated with AILD and the combination of them shows good ability of identifying high-risk individuals.
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