External validation of non-invasive prediction models for identifying ultrasonography-diagnosed fatty liver disease in a Chinese population.
External validation of non-invasive prediction models for identifying ultrasonography-diagnosed fatty liver disease in a Chinese population.
复制标题
用于识别中国人群中超声诊断的脂肪肝疾病的非侵入性预测模型的外部验证。
DOI：
10.1097/md.0000000000007610
复制
发表时间：
2017-07
期刊：
影响因子：
1.6
通讯作者：
Wang T
中科院分区：
文献类型：
--
作者：
Shen YN;Yu MX;Gao Q;Li YY;Huang JJ;Sun CM;Qiao N;Zhang HX;Wang H;Lu Q;Wang T
脂肪肝疾病（FLD）的几种预测模型可供使用，外部验证有限，综合评估较少。目的是在总体人群和肥胖亚群中对FLD的4种预测模型（脂肪肝指数、肝脏脂肪变性指数、ZJU指数和脂肪肝脂肪变性指数）进行外部验证和直接比较。本研究采用横断面研究方法，对山西省北部地区4247名20 ~ 65岁的受试者进行了研究。使用标准方案收集人体测量和生化特征。肝脏超声诊断为FLD。我们评估了所有模型的歧视，校准和决策曲线分析。原始模型在总体人群的区分方面表现良好，接受者工作特征曲线（AUC）下的面积约为0.85，而肥胖个体的AUC约为0.68。然而，在总体人群和肥胖亚群中，预测的风险与观察到的风险并不匹配。FLI 2006是区分度最好的2个模型之一（总体人群和肥胖亚组的AUC分别为0.87和0.72），在校准方面具有最好的性能，并获得了最高的净效益。总体而言，FLI 2006是识别高风险个体的最佳工具，具有很大的临床实用性。尽管如此，它的表现不足以量化FLD的实际风险，需要（重新）校准以供临床使用。
Supplemental Digital Content is available in the text Several prediction models for fatty liver disease (FLD) are available with limited externally validation and less comprehensive evaluation. The aim was to perform external validation and direct comparison of 4 prediction models (the Fatty Liver Index, the Hepatic Steatosis Index, the ZJU index, and the Framingham Steatosis Index) for FLD both in the overall population and the obese subpopulation. This cross-sectional study included 4247 subjects aged 20 to 65 years recruited from the north of Shanxi Province in China. Anthropometric and biochemical features were collected using standard protocols. FLD was diagnosed by liver ultrasonography. We assessed all models in terms of discrimination, calibration, and decision curve analysis. The original models performed well in terms of discrimination for the overall population, with the area under the receiver operating characteristic curves (AUCs) around 0.85, while AUCs for obese individuals were around 0.68. Nevertheless, the predicted risks did not match well with the observed risks both in the overall population and the obese subpopulation. The FLI 2006 was 1 of the 2 best models in terms of discrimination (AUCs were 0.87 and 0.72 for the overall population and the obese subgroup, respectively) and had the best performance in terms of calibration, and attained the highest net benefit. The FLI 2006 is overall the best tool to identify high risk individuals and has great clinical utility. Nonetheless, it does not perform well enough to quantify the actual risk of FLD, which need to be (re)calibrated for clinical use.