Development and Validation of a Risk Prediction Model for NAFLD: A Study Based on a Physical Examination Population

Abstract

Chunmei Yang,1,2 Tingwan Du,1 Yueying Zhao,1 Youhui Qian,1 Jiashi Tang,1 Xiaohong Li,2,* Ling Ma1,3,* 1Department of Nutrition and Food Hygiene, School of Public Health, Southwest Medical University, Luzhou, 646000, People’s Republic of China; 2Health Management Center, The Affiliated Hospital, Southwest Medical University, Luzhou, 646000, People’s Republic of China; 3Environmental Health Effects and Risk Assessment Key Laboratory of Luzhou, School of Public Health, Southwest Medical University, Luzhou, 646000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaohong Li; Ling Ma, Email [email protected]; [email protected]: 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 (P > 0.05). DCA indicated favorable clinical applicability of the model.Conclusion: We constructed a nomogram model that could complement traditional NAFLD detection methods, aiding in individualized risk assessment for NAFLD.Keywords: non-alcoholic fatty liver disease, nomogram, prediction model, LASSO

Keywords

• non-alcoholic fatty liver disease
• nomogram
• prediction model
• lasso