肝纤维化与异常肝功能和肝癌密切相关。准确的非侵入性肝纤维化评估对于预防疾病进展和治疗决策具有重要意义。本研究的目的是开发和验证一种用于非酒精性脂肪性肝病患者评估重要纤维化的非侵入性预测模型。2017-2018年，从NHANES数据库中提取了所有参与者的信息。从中选择了显著纤维化的符合条件患者(n=123)和非显著纤维化患者(n=898)组成原始数据集。使用最小绝对收缩和选择算子(Lasso)回归进行变量选择，并使用多变量逻辑回归分析开发预测模型。评估模型的实用性包括其区分能力、校准性和临床可用性。使用自举重采样内部验证来评估预测模型的准确性。本研究建立了一个由9个常见临床指标组成的新模型，并开发了一个在线计算器来展示该模型。与先前提出的肝纤维化评分系统相比，该模型在训练组(0.812，95%CI 0.769-0.855)和验证组(0.805，95%CI 0.762-0.847)中显示出最佳的区分能力和预测性能，具有最高的曲线下面积。特异度(0.823)、敏感度(0.699)、阳性似然比(3.949)和阴性似然比(0.366)同样出色。预测概率与显著纤维化的实际发生概率的校准图显示出卓越的一致性，表明模型校准优秀。结合决策曲线分析，该模型在0.1-0.8阈值概率范围内具有很大的益处，并且在临床显著纤维化的诊断中具有良好的应用价值。本研究提出了一种将临床指标结合起来的新型非侵入性诊断模型，为非酒精性脂肪性肝病患者的显著纤维化提供了精确、方便的个体化诊断。版权所有 © 2023 郭、沈、薛和李。

Liver fibrosis is closely related to abnormal liver function and liver cancer. Accurate noninvasive assessment of liver fibrosis is of great significance for preventing disease progression and treatment decisions. The purpose of this study was to develop and validate a non-invasive predictive model for the asses`sment of significant fibrosis in patients with non-alcoholic fatty liver disease.Information on all participants for 2017-2018 was extracted from the NHANES database. The eligible patients with significant fibrosis (n=123) and non-significant fibrosis (n=898) were selected to form the original dataset. Variable selection was performed using least absolute shrinkage and selection operator (Lasso) regression, and multivariate logistic regression analysis was used to develop a prediction model. The utility of the model is assessed in terms of its discrimination, calibration and clinical usability. Bootstrap-resampling internal validation was used to measure the accuracy of the prediction model.This study established a new model consisting of 9 common clinical indicators and developed an online calculator to show the model. Compared with the previously proposed liver fibrosis scoring system, this model showed the best discrimination and predictive performance in the training cohort (0.812,95%CI 0.769-0.855) and the validation cohort (0.805,95%CI 0.762-0.847), with the highest area under curve. Specificity(0.823), sensitivity(0.699), positive likelihood ratio(3.949) and negative likelihood ratio(0.366) were equally excellent. The calibration plot of the predicted probability and the actual occurrence probability of significant fibrosis shows excellent consistency, indicating that the model calibration is outstanding. Combined with decision curve analysis, this model has a great benefit in the range of 0.1-0.8 threshold probability, and has a good application value for the diagnosis of clinical significant fibrosis.This study proposes a new non-invasive diagnostic model that combines clinical indicators to provide an accurate and convenient individualized diagnosis of significant fibrosis in patients with non-alcoholic fatty liver disease.Copyright © 2023 Guo, Shen, Xue and Li.