Article

External Validation and Clinical Utility of Non-Invasive Prediction Models for Non-Alcoholic Fatty Liver Disease in Malaysia

2020-11-06

Abstract excerpt

<title>Abstract</title> <p><bold>Background: </bold>Many prediction models have been developed to detect non-alcoholic fatty liver disease (NAFLD). The drawbacks of many of models are the use of parameters that are not routinely measured locally. This study aimed to evaluate the external validity of a series of prediction models for NAFLD, which were selected based on the routinely measured and tested clinical pa...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 2cd0c88d-2565-55bf-97ab-39446af805b7
DOI: 10.21203/rs.3.rs-102663/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

External Validation and Clinical Utility of Non-Invasive Prediction Models for Non-Alcoholic Fatty Liver Disease in MalaysiaDOI 10.21203/rs.3.rs-102663/v1Read

Machine learning-based mortality prediction models for non-alcoholic fatty liver disease in the general United States populationSemantic proximity Read

External Validation and Comparison of Simple Tools to Screen for Nonalcoholic Fatty Liver disease in Chinese community populationSemantic proximity Read

Nonalcoholic fatty liver disease test: an external validation cohort.Semantic proximity Read

Clinical strategy of diagnosing and following patients with nonalcoholic fatty liver disease based on invasive and noninvasive methods.Semantic proximity Read

Reference Interval of Enhanced Liver Fibrosis (Elf) Score in Healthy Multi-ethnic Malaysian PopulationSemantic proximity Read

Steatosis Non-Invasive Tests Accurately Predict Metabolic Dysfunction-Associated Steatotic Liver Disease, While Fibrosis Non-Invasive Tests Fall Short: Validation in U.S. Adult PopulationSemantic proximity Read

External validation of biomarkers of fatty liver in the general population: the Bagnacavallo studySemantic proximity Read

Machine Learning–Based Prediction of Ultrasound-Detected Metabolic Dysfunction–Associated Steatotic Liver Disease Using Routine Clinical and Biochemical ParametersSemantic proximity Read

Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosisSemantic proximity Read

Non-Invasive Evaluation of MAFLD and the Contribution of Genes: An MRI-PDFF-Based Cross-Sectional StudySemantic proximity Read

Factors Associated with Risk of NAFLD and Progression in Multiethnic Asians. A Latent Class and Machine Learning AnalysisSemantic proximity Read

SFI, a sex hormone binding globulin based nomogram for predicting non-alcoholic fatty liver in the Chinese populationSemantic proximity Read