Faculty of Business Administration, Ono Academic College, 104 Zahal Street, 55000, Kiryat Ono, Israel.
Departments of Internal Medicine "C", "D" and "E", Tel-Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Weizmann 6 St, Tel Aviv, Israel.
J Med Syst. 2021 Jan 11;45(2):22. doi: 10.1007/s10916-020-01693-5.
Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide, with a prevalence of 20%-30% in the general population. NAFLD is associated with increased risk of cardiovascular disease and may progress to cirrhosis with time. The purpose of this study was to predict the risks associated with NAFLD and advanced fibrosis on the Fatty Liver Index (FLI) and the 'NAFLD fibrosis 4' calculator (FIB-4), to enable physicians to make more optimal preventive medical decisions. A prospective cohort of apparently healthy volunteers from the Tel Aviv Medical Center Inflammation Survey (TAMCIS), admitted for their routine annual health check-up. Data from the TAMCIS database were subjected to machine learning classification models to predict individual risk after extensive data preparation that included the computation of independent variables over several time points. After incorporating the time covariates and other key variables, this technique outperformed the predictive power of current popular methods (an improvement in AUC above 0.82). New powerful factors were identified during the predictive process. The findings can be used for risk stratification and in planning future preventive strategies based on lifestyle modifications and medical treatment to reduce the disease burden. Interventions to prevent chronic disease can substantially reduce medical complications and the costs of the disease. The findings highlight the value of predictive analytic tools in health care environments. NAFLD constitutes a growing burden on the health system; thus, identification of the factors related to its incidence can make a strong contribution to preventive medicine.
非酒精性脂肪性肝病（NAFLD）是全球最常见的肝脏疾病，普通人群中的患病率为 20%-30%。NAFLD 与心血管疾病风险增加有关，并随着时间的推移可能进展为肝硬化。本研究的目的是预测基于脂肪肝指数（FLI）和“NAFLD 纤维化 4”计算器（FIB-4）的 NAFLD 和晚期纤维化相关风险，以使医生能够做出更优的预防医疗决策。本研究为前瞻性队列研究，纳入了来自特拉维夫医疗中心炎症调查（TAMCIS）的貌似健康志愿者，他们因常规年度健康检查而入院。对 TAMCIS 数据库的数据进行了机器学习分类模型分析，以在广泛的数据准备（包括在多个时间点计算独立变量）后预测个体风险。在纳入时间协变量和其他关键变量后，该技术的预测能力优于当前流行的方法（AUC 提高了 0.82 以上）。在预测过程中发现了新的有力因素。这些发现可用于风险分层，并根据生活方式改变和医疗治疗来规划未来的预防策略，以减轻疾病负担。预防慢性病的干预措施可以显著减少医疗并发症和疾病成本。这些发现强调了预测分析工具在医疗保健环境中的价值。NAFLD 对医疗系统构成了越来越大的负担；因此，确定与发病率相关的因素可以为预防医学做出重要贡献。
