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| Establishment and analysis of a nonalcoholic fatty liver disease nomogram prediction model based on serum Betatrophin level |
| GAO Fulai XIE Changshun ZHANG Lili |
| Department of Gastroenterology, the First Hospital of Qinhuangdao, Hebei Province, Qinhuangdao 066000, China |
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Abstract Objective To establish a nomogram predictive model by analyzing the risk factors of nonalcoholic fatty liver disease (NAFLD), and to establish a nomogram model based on serum Betatrophin level, and evaluate the accuracy of prediction. Methods From January 2017 to April 2018, 180 patients with NAFLD admitted to the First Hospital of Qinhuangdao were selected as the NAFLD group, and 72 patients with normal liver fat content (LF < 9.15%) were selected as control group. Univariate analysis of factors associated with NAFLD, statistically significant indicators were included in the multivariate Logistic regression model, and the risk factors for the NAFLD were analyzed. Based on the results of the regression analysis, a preoperative model was established. The receiver operating characteristic curve (ROC) and the Hosmer-Lemeshow test were used to evaluate the model prediction performance. Results Multivariate analysis showed that Betatrophin, triglyceride (TG) and aspartate aminotransferase (AST) were risk factors for NAFLD, and high-density lipoprotein (HDL) was a protective factor for NAFLD (P < 0.05). The nomogram prediction model for predicting the occurrence of NAFLD was successfully established by using R software. The Hosmer-Lemeshow test showed that the model had a predicted performance of 85.71% and the model fit well (P < 0.05). The area under the curve (AUC) for predicting the occurrence of NAFLD was 0.877, the specificity was 88.68%, and the sensitivity was 82.98%. Conclusion The nomogram prediction model based on the serum Betatrophin level of NAFLD has good specificity and sensitivity. The model is well fitted and has high clinical value.
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