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.