ArticleCureus2021
The Pattern of Dyslipidemia in Chronic Liver Disease Patients.
Article in Cureus, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Targeted Lipidomics RevealsFood science & nutrition · 2026Article
- Lipid abnormalities in chronic viral hepatitis: associations and machine learning-enhanced prediction.BMC gastroenterology · 2026Article
- Acute-on-chronic liver failure in metabolic dysfunction-associated fatty liver disease patients: a disease multiplier.Hepatology international · 2024Review
- Alterations in lipids after initiation of highly effective modulators in people with cystic fibrosis.Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society · 2023Article
- [Analysis of curative effect and short-term survival rate of plasma exchange and double plasma molecular adsorption combined with half-volume plasma exchange in the treatment of liver failure].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2023Article
- Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI).PLoS computational biology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 7 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction Patients with chronic liver disease are expected to report derangements in serum lipid profiles. Lipid profile monitoring is not a part of the routine management of these patients in our hospital. Few recent studies show how lipid profile varies with the severity of disease and should be considered in the management planning of such patients. The objective of this study was to determine the pattern of dyslipidemia in chronic liver disease patients. Materials and methods A cross-sectional study was conducted involving 171 patients of all genders aged between 18 years and 60 years presenting with chronic liver disease with disease severity graded on Child-Pugh class as A, B, and C. Lipid profile was acquired in all these patients and was compared across various subgroups. Individual serum lipid parameters were graded as normal, high, or very high. Each patient was required to provide written informed consent. Statistical Package for Social Sciences (SPSS) version 21.0 (IBM Corp. Armonk, NY) was used to analyze data statistically, taking a p-value of ≤0.05 as significant. Results The mean age of patients was 51.2±7.3 years. The male to female ratio came out to be 1.5:1, with 103 (60.2%) male and 68 (39.8%) female patients included in the study. The disease was classified as Child-Pugh A in 20 (11.7%) patients, Child-Pugh B in 67 (39.2%) patients, and Child-Pugh C in 84 (49.1%) patients. Forty-four (25.7%) patients were hypertensive while 62 (36.3%) were diabetic. The mean body mass index (BMI) of these patients was 25.9±2.4 kg/m
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.