Predictive models for incident cirrhosis in non-alcoholic fatty liver disease using genetic and electronic medical record-based risk factors

项目总结/摘要 非酒精性脂肪肝（NAFLD）影响着美国超过8000万人，并与以下疾病有关 肝脏相关死亡的36%。虽然NAFLD是肝硬化和肝脏相关疾病增长最快的原因， 尽管有并发症，但并非所有NAFLD患者最终都会发展为肝硬化。我们能够识别哪些病人 处于最高风险的人是有限的，这使得分配密集的生活方式干预具有挑战性， 对高危人群进行药物治疗。发生肝硬化的最强预测因子是纤维化分期， 但现有的纤维化仅识别已经进展为肝硬化的患者， 高级表型分型，如活组织检查或瞬时弹性成像，这不是普遍可用的。将 这对于开发改进的疾病进展模型至关重要。该项目侧重于两个因素， 改善肝硬化进展风险分层：使用电子医学的遗传学和机器学习 记录（EMR）数据。肝纤维化和肝硬化的遗传率高达50%，且有许多遗传因素 变异与肝硬化的风险有关。EMR是临床应用中使用的丰富但复杂的数据源。 实践当用这种高维数据构建模型时，非线性效应和相互作用 机器学习算法可能优于更常用的 逻辑回归模型在这方面。该项目的总体目标是生成预测模型， NAFLD患者最有可能通过整合遗传学和基于EMR的 机器学习的预测器具体目标是（1）描述遗传危险因素对 从NAFLD到肝硬化的进展速度，（2）训练和验证用于事件的机器学习模型 基于EMR数据的肝硬化，以及（3）生成结合EMR和遗传数据的集成模型。 为了实现这些目标，陈博士将获得进一步的培训，在电子病历数据处理，基础知识， 统计遗传学、机器学习和预测建模。陈博士的长期目标是成为 领先的独立研究者生成模型来预测NAFLD的结果， 相应地优先治疗患者。NIDDK K 08奖将为陈博士提供必要的 时间和培训，以实现他的职业目标，并改善对NAFLD患者的护理。总的来说，这个项目 将提高预测哪些NAFLD患者最有可能发展为肝硬化的能力， 通过帮助医疗服务提供者优先考虑风险最高的人， 干预

Project Summary/Abstract Non-alcoholic fatty liver disease (NAFLD) affects >80 million people in the United States and is implicated in up 36% of liver-related deaths. While NAFLD is the fastest-growing cause of cirrhosis and liver-related complications, not all patients with NAFLD ultimately develop cirrhosis. Our ability to identify which patients are at highest risk is limited, which makes it challenging to allocate intensive lifestyle intervention and pharmacologic therapy to those at highest risk. The strongest predictor of incident cirrhosis is fibrosis stage, but existing fibrosis only identifies patients who have already progressed toward cirrhosis and requires advanced phenotyping such as biopsy or transient elastography which are not universally available. It will be critical to develop improved models for disease progression. This project focuses on two factors which may improve risk stratification of progression to cirrhosis: genetics and machine learning using electronic medical record (EMR) data. Heritability of liver fibrosis and cirrhosis is as high as 50%, and a number of genetic variants have been linked to risk of cirrhosis. The EMR is a rich but complex source of data used in clinical practice. When constructing models with such high-dimensional data, non-linear effects and interactions between predictors are common; machine learning algorithms may outperform the more commonly-used logistic regression models in this respect. The overall goal of this project is to generate predictive models for which patients with NAFLD are most likely to progress to cirrhosis by integrating genetics and EMR-based predictors with machine learning. The specific aims are (1) characterizing the effect of genetic risk factors on rate of progression from NAFLD to cirrhosis, (2) training and validating machine learning models for incident cirrhosis based on EMR data, and (3) generating integrated models incorporating both EMR and genetic data. To accomplish these aims, Dr. Chen will obtain further training in processing of EMR data, the fundamentals of statistical genetics, and machine learning and predictive modeling. Dr. Chen’s long-term goal is to become a leading, independent investigator generating models to predict outcomes in NAFLD and eventually even prioritize patients for treatment accordingly. An NIDDK K08 award will provide Dr. Chen with the necessary time and training to achieve his career goals and improve care for patients with NAFLD. Overall, this project will improve ability to predict which patients with NAFLD are most likely to develop cirrhosis and therefore enhance precision health by helping medical providers prioritize persons at highest risk to more intensive intervention.