ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2024
Development and Validation of a Risk Prediction Model for NAFLD: A Study Based on a Physical Examination Population.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Development of a liver stiffness measurement-based nomogram model to identify early CKD risk in patients with MAFLD.BMC gastroenterology · 2026Article
- Advancing non-alcoholic fatty liver disease prediction: a comprehensive machine learning approach integrating SHAP interpretability and multi-cohort validation.Frontiers in endocrinology · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose: To construct and validate a precise and personalized predictive model for non-alcoholic fatty liver disease (NAFLD) to enhance NAFLD screening and healthcare administration. Patients and Methods: A total of 730 participants' clinical information and outcome measurements were gathered and randomly divided into training and validation sets in a ratio of 3:7. Using the least absolute shrinkage and selection operator (LASSO) regression and multiple logistic regression, a nomogram was established to select risk predictor variables. The NAFLD prediction model was validated through the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). Results: After random grouping, the cohort comprised 517 in the training set and 213 in the validation set. The prediction model employed nine of the 20 selected variables, namely gender, hypertension, waist circumference, body mass index, blood platelet, triglycerides, high-density lipoprotein cholesterol, plasma glucose, and alanine aminotransferase. ROC curve analysis yielded an area under the curve values of 0.877 (95% Confidence Interval [CI]: 0.848-0.907) for the training set and 0.871 (95% CI: 0.825-0.917) for the validation set. Optimal critical values were determined as 0.472 (0.786, 0.825) in the training set and 0.457 (0.743, 0.839) in the validation set. Calibration curves for both sets showed proximity to the ideal diagonal, with P-values of 0.972 and 0.370 for the training and validation sets, respectively ( Conclusion: We constructed a nomogram model that could complement traditional NAFLD detection methods, aiding in individualized risk assessment for NAFLD.
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