LIVOPT -- LIVer cirrhosis - Optimizing Prediction of Patient OuTcomes

项目摘要 在美国，肝硬变是导致死亡的主要原因，在数百万人中被诊断出来，并导致 每年有超过40,000人死亡，主要是老年人。这些患者严重感染的风险更高。 并发症(如感染、腹水、肝性脑病)、住院和死亡。人们对此知之甚少 流行病学和疾病进展和年龄较大的患者更容易受到伤害，可能有不同的风险 配置文件。临床上的挑战是准确和早期地预测谁有最高的风险需要 住院和/或死亡，并了解这在老年人和年轻人中可能有何不同。。 到目前为止，一个障碍是缺乏具有流行病学代表性的患者样本来捕捉这些 对于肝硬变，并不仅仅是一个医疗系统，因为许多数据存储库是不对称的或有限的：仅限CMS 捕捉老年人和体弱者，NIS不允许纵向观察，UNOS代表较少 比1%的肝硬化症患者要多。因此，我们将在以前工作的基础上使用唯一的数据集 芝加哥地区以患者为中心的结果研究网络(摩羯座)。摩羯座捕捉到 自2011年至今，9个医疗系统的电子健康记录(EHR)(包括学术中心，县， 弗吉尼亚州和私人)，吸引了大芝加哥大都市区的大多数多样化的患者群体， 即~16万名肝硬变患者。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)，以追求研究的目标。我们的目标是 老年患者的肝硬变进展模型和住院和死亡风险的预测 患有肝硬变。我们还打算进行分组分析，重点放在种族和社会经济因素上 老年人。准确、早期预测老年糖尿病患者死亡率和住院风险的影响 肝硬变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.