LIVOPT -- LIVer cirrhosis - Optimizing Prediction of Patient OuTcomes

LIVOPT——肝硬化——优化患者结果的预测

• 批准号: 10490243
• 负责人: Daniela P Ladner
• 金额: $ 63.51万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10490243
• 关键词: Affect; Area; Ascites; Attention; Black race; Caring; Cause of Death; Cessation of life; Chicago; Cirrhosis; Clinical; County; Data; Data Set; Development; Diagnosis; Disease Progression; Disease model; Elderly; Electronic Health Record; Epidemiology; Event; Gastrointestinal Hemorrhage; Graph; Health system; Hepatic Encephalopathy; Hospitalization; Infection; Inpatients; Insurance; Intensive Care; Intervention; Knowledge; Life Experience; Liver; Liver Cirrhosis; Machine Learning; Medical; Modeling; Outcomes Research; Patient Representative; Patient-Focused Outcomes; Patients; Pattern; Persons; Population; Primary carcinoma of the liver cells; Privatization; Probability; Public Health; Race; Research; Resources; Risk; Risk Estimate; Sampling; Sampling Biases; Socioeconomic Factors; Subgroup; Techniques; Time; Transplantation; United Network for Organ Sharing; United States; United States Centers for Medicare and Medicaid Services; United States Department of Veterans Affairs; Work; adjudicate; black patient; cost; data repository; deep learning; disability; disorder risk; end stage liver disease; high risk; improved outcome; metropolitan; mortality; mortality risk; novel; older patient; organ allocation; patient population; patient subsets; population based; predictive modeling; risk prediction; transplant model; young adult

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.

项目摘要