背景:非酒精性脂肪性肝病(NAFLD)是一种常见的慢性肝病,影响全球四分之一的成年人。迄今为止,仅针对中国≥60岁的老年人开发了少数NAFLD风险预测模型.这项研究提出了在中国年龄≥60岁人群中NAFLD风险预测模型的发展,并提出了基于关键风险因素的个性化健康干预措施,以降低人群中NAFLD的发病率。
方法:对上海市9041名社区居民进行横断面调查。三种NAFLD风险预测模型(I,II,和III)采用基于最小绝对收缩和选择算子回归分析的多元逻辑回归分析构建,和随机森林模型来选择个体特征,分别。为了确定最优模型,三种模式的歧视,校准,临床应用,并使用接收器工作特性(ROC)曲线评估预测能力,校准图,决策曲线分析,和净重新分类指数(NRI),分别。为了评估最优模型的有效性,先前发表的NAFLD风险预测模型(肝脂肪变性指数[HSI]和ZJU指数)使用以下五个指标进行评估:准确性,精度,召回,F1分数,平衡的准确性。构造了最优模型的动态列线图,使用Netica软件直观显示了用于预测老年人NAFLD风险的贝叶斯网络模型。
结果:模型I的ROC曲线下面积,II,训练数据集中的III分别为0.810、0.826和0.825,测试数据分别为0.777、0.797和0.790。模型之间的准确性或NRI没有发现显着差异;因此,变量最少的模型III被确定为最优模型。与恒生指数和ZJU指数相比,模型III具有最高的精度(0.716),精度(0.808),召回(0.605),F1得分(0.692),和平衡精度(0.723)。模型III的风险阈值为20%-80%。模型III包括体重指数,丙氨酸氨基转移酶水平,甘油三酯水平,和淋巴细胞计数。
结论:开发了动态列线图和贝叶斯网络模型来识别中国老年人的NAFLD风险,提供个性化健康管理策略并降低NAFLD发病率。
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver condition that affects a quarter of the global adult population. To date, only a few NAFLD risk prediction models have been developed for Chinese older adults aged ≥ 60 years. This
study presented the development of a risk prediction model for NAFLD in Chinese individuals aged ≥ 60 years and proposed personalised health interventions based on key risk factors to reduce NAFLD incidence among the population.
METHODS: A cross-sectional survey was carried out among 9,041 community residents in Shanghai. Three NAFLD risk prediction models (I, II, and III) were constructed using multivariate logistic regression analysis based on the least absolute shrinkage and selection operator regression analysis, and random forest model to select individual characteristics, respectively. To determine the optimal model, the three models\' discrimination, calibration, clinical application, and prediction capability were evaluated using the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis, and net reclassification index (NRI), respectively. To evaluate the optimal model\'s effectiveness, the previously published NAFLD risk prediction models (Hepatic steatosis index [HSI] and ZJU index) were evaluated using the following five indicators: accuracy, precision, recall, F1-score, and balanced accuracy. A dynamic nomogram was constructed for the optimal model, and a Bayesian network model for predicting NAFLD risk in older adults was visually displayed using Netica software.
RESULTS: The area under the ROC curve of Models I, II, and III in the training dataset was 0.810, 0.826, and 0.825, respectively, and that of the testing data was 0.777, 0.797, and 0.790, respectively. No significant difference was found in the accuracy or NRI between the models; therefore, Model III with the fewest variables was determined as the optimal model. Compared with the HSI and ZJU index, Model III had the highest accuracy (0.716), precision (0.808), recall (0.605), F1 score (0.692), and balanced accuracy (0.723). The risk threshold for Model III was 20%-80%. Model III included body mass index, alanine aminotransferase level, triglyceride level, and lymphocyte count.
CONCLUSIONS: A dynamic nomogram and Bayesian network model were developed to identify NAFLD risk in older Chinese adults, providing personalized health management strategies and reducing NAFLD incidence.