Natural history, risk prediction and cost of cirrhosis in insured Americans.

总结 肝硬化是美国（US）死亡的主要原因，在150 - 940万美国人中诊断出， 导致每年超过4万人死亡每年有5-7%的肝硬化患者会经历危及生命的经历。 失代偿事件，如腹水、肝性脑病（HE）、胃肠道出血（GIB），或 发展成肝细胞癌（HCC）。这些事件通常导致住院、残疾甚至死亡。 挑战在于准确预测那些可能发生失代偿事件的患者 很可能会死对肝硬化患者进行准确的危险分层将有助于早期识别 以及指南推荐的护理和新兴疗法的优先级（例如，他汀类药物）。几 存在预测模型，但它们中没有一个能充分回答这个问题。此外，没有纵向成本 在美国已经对肝硬化患者进行了护理分析和成本预测。的关怀 肝硬化患者的治疗是复杂的，通常涉及昂贵的反复住院和手术。在2015年， 据报单是医院费用就达163亿元。我们提出的研究将分析一个大的国家 行政卫生付款人的详细信息，诊断，程序，实验室测试，药物， 住院和门诊护理，以及2011年至2018年期间投保美国人的标准化费用。这样的 一个基于人群的肝硬化队列，包括成本数据，提供了一个独特的和前所未有的机会， 研究疾病进展，开发高度准确的预测模型，并研究成本。 目标1。描述一个大型纵向队列中随时间推移的肝硬化自然史， 美国肝硬化患者 目标1.1：数据准备和变量转换 目的1.2：肝硬化利益相关者小组判定潜在风险相关协变量 目的1.3：描述肝硬化的自然史 目标2.预测肝硬化患者失代偿、住院和死亡的风险， 大型纵向管理数据集（UNITED Health Group） 目标2.1：建立失代偿风险模型 目标2.2：建立住院风险模型 目标2.3：将UHG数据集与国家死亡指数合并，并建立死亡风险模型 目的3：预测肝硬化患者各方面护理的相关成本 目标3.1：确定按肝硬化状态/表型分层的标准化成本 目标3.2：建立随时间变化的护理成本模型

SUMMARY Cirrhosis is a leading cause of mortality in the United States (US), diagnosed in 1.5-9.4 million Americans and results in over 40,000 deaths each year. Every year 5-7% patients with cirrhosis experience life-threatening decompensating events, such as ascites, hepatic encephalopathy (HE), gastrointestinal bleeding (GIB), or develop hepatocellular carcinoma (HCC). These events often result in hospitalization, disability, or even death. The challenge is to accurately predict those patients who are likely to develop decompensating events and are likely to die. Accurate risk stratification of persons with cirrhosis will allow for early identification and prioritization for guideline recommended care and emerging therapies (e.g., statins). Several predictive models exist but none of them adequately answers this question. Furthermore, no longitudinal cost of care analyses and cost prediction has been performed in the US for persons with cirrhosis. The care of patients with cirrhosis is complex, often involving costly recurrent hospitalizations and procedures. In 2015, the hospital costs alone were reported to be $16.3 billion. Our proposed research will analyze a large national administrative health payer with detailed information on diagnoses, procedures, laboratory tests, medications, in- and outpatient care, as well as standardized costs for insured Americans between 2011 and 2018. Such a large population-based cirrhosis cohort, which includes cost data, offers a unique and unprecedented opportunity to study disease progression, develop highly accurate prediction models, and study costs. Aim 1. To describe the natural history of cirrhosis over time in a large longitudinal cohort of insured Americans with liver cirrhosis in the United States Aim 1.1: Data preparation and variable transformation Aim 1.2: Adjudicate potentially risk-relevant covariates by a cirrhosis stakeholder panel Aim 1.3: Describe the natural history of cirrhosis Aim 2. To predict the risk of decompensation, hospitalization and death in patients with cirrhosis using a large longitudinal administrative dataset (UNITED Health Group) Aim 2.1: Model the risk of decompensation Aim 2.2: Model the risk of hospitalization Aim 2.3: Merge the UHG dataset with the National Death Index and model the risk of death Aim 3: To predict costs associated with all aspects of care in patients with liver cirrhosis Aim 3.1: Ascertain standardized cost stratified by state/phenotypes of liver cirrhosis Aim 3.2: Model the cost of care over time