医学
肝硬化
代谢综合征
非酒精性脂肪肝
胰岛素抵抗
血脂异常
脂肪变性
糖尿病
内科学
纤维化
脂肪肝
特征选择
疾病
人工智能
机器学习
胰岛素
内分泌学
计算机科学
作者
Rafael García-Carretero,Luis Vigil-Medina,Óscar Barquero-Pérez,Javier Ramos-López
出处
期刊:Metabolic Syndrome and Related Disorders
[Mary Ann Liebert]
日期:2019-11-01
卷期号:17 (9): 444-451
被引量:18
标识
DOI:10.1089/met.2019.0052
摘要
Aim: We investigated the prevalence and the most relevant features of nonalcoholic steatohepatitis (NASH), a stage of nonalcoholic fatty liver disease, (NAFLD) in which the inflammation of hepatocytes can lead to increased cardiovascular risk, liver fibrosis, cirrhosis, and the need for liver transplant. Methods: We analyzed data from 2239 hypertensive patients using descriptive statistics and supervised machine learning algorithms, including the least absolute shrinkage and selection operator and random forest classifier, to select the most relevant features of NASH. Results: The prevalence of NASH among our hypertensive patients was 11.3%. In univariate analyses, it was associated with metabolic syndrome, type 2 diabetes, insulin resistance, and dyslipidemia. Ferritin and serum insulin were the most relevant features in the final model, with a sensitivity of 70%, specificity of 79%, and area under the curve of 0.79. Conclusion: Ferritin and insulin are significant predictors of NASH. Clinicians may use these to better assess cardiovascular risk and provide better management to hypertensive patients with NASH. Machine-learning algorithms may help health care providers make decisions.
科研通智能强力驱动
Strongly Powered by AbleSci AI