LIVOPT -- LIVer cirrhosis - Optimizing Prediction of Patient OuTcomes

项目摘要 肝硬化是美国（US）死亡率的主要原因，在数百万人中被诊断出并导致死亡。 每年有4万多人死亡，主要是老年人。这些患者发生严重 并发症（如感染、腹水、肝性脑病）、住院和死亡。知之甚少 流行病学和疾病进展和老年患者更脆弱，可能有不同的风险 数据区.临床挑战是准确和早期预测谁是最高风险的需要 住院和/或死亡，并了解老年人与年轻人之间的差异。. 迄今为止的一个障碍是缺乏流行病学代表性的患者样本， 肝硬化，并超越了一个卫生系统，因为许多数据存储库是倾斜或有限的：CMS只 捕捉老年人和体弱者，NIS不允许纵向观察，UNOS代表较少 超过百分之一的肝硬化患者。因此，我们将在以前的工作基础上使用一个独特的数据集， 芝加哥地区以患者为中心的结局研究网络（CAPriCORN）。CAPriCORN捕获了 电子健康记录（EHR）来自2011年至今的9个卫生系统（包括学术中心，县， 弗吉尼亚州和私人），捕捉大芝加哥大都市区的大多数不同的病人群体， 即约16万肝硬化患者。数据集是广泛的，允许使用传统的（例如， 回归）和新的分析技术（如深度学习）来实现研究的目标。我们的目标是 模型老年人肝硬化进展，并预测老年患者住院和死亡的风险 肝硬化我们还旨在进行亚组分析，重点是种族和社会经济因素， 老人准确和早期预测老年患者死亡率和住院风险的影响 肝硬化允许对最脆弱的患者进行有针对性的干预，以改善结果。

PROJECT ABSTRACT Cirrhosis is a leading cause of mortality in the United States (US), diagnosed in millions of people and resulting in over 40,000 deaths each year, predominately in older people. These patients are at higher risk of serious complications (e.g. infection, ascites, hepatic encephalopathy), hospitalization and death. Little is known about the epidemiology and disease progression and older patients are more vulnerable and might have different risk profiles. The clinical challenge is to accurately and early predict who is at highest risk for requiring hospitalization and/or death and understanding how this might be different in older versus younger adults. . One barrier to date has been the lack of an epidemiologically representative patient sample that captures those with cirrhosis and goes beyond one health system, as many data repositories are skewed or limited: CMS only captures elderly and those with debility, NIS does not allow longitudinal observation, UNOS represents less than one percent of those with cirrhosis. Hence, we will build on our previous work to use a unique dataset, the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN). CAPriCORN captures the electronic health records (EHR) from nine health systems from 2011-present (incl. academic center, county, VA and private), catching most of the diverse patient population in the greater Chicago metropolitan area, namely ~160,000 patients with liver cirrhosis. The dataset is extensive, allowing the use of traditional (e.g., regression) and novel analytical techniques (e.g. deep learning) to pursue the aims of the study. We aim to model cirrhosis progression in the elderly and to predict the risk of hospitalization and death in older patients with cirrhosis. We also aim to perform sub-group analyses focusing on race and socioeconomic factors in the elderly. The impact of accurate and early prediction of mortality and hospitalization risk in elderly patients with cirrhosis allows for targeted interventions of the most vulnerable patients to improve outcomes.