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