Background: Chronic liver diseases (CLD) are a group of common, costly, and clinically consequential disorders that are increasingly prevalent in Veterans. Patients with CLD are typically referred to specialty care for further evaluation. Such referrals can improve clinical outcomes, but universal referral is not feasible due to resource limitations. Moreover, universal specialty care referral is not only impractical – it is also unnecessary. Most patients with CLD will have stable disease for years to decades (favorable prognosis), while others will rapidly progress to liver fibrosis and cirrhosis (unfavorable prognosis). Given the vast number of Veterans with CLD, we need a way to systematically and reliably identify patients with an unfavorable prognosis who are at increased risk for poor clinical outcomes (and would therefore benefit from early specialty care referral). Identifying patients with an unfavorable prognosis is challenging due to a lack of clinical signs and symptoms in most patients with CLD; however, signs are routinely detectable on radiological imaging. Because these radiologic findings are qualitative in nature and encoded within imaging data, they have traditionally been of limited clinical utility. We propose to utilize a novel technology, analytic morphomics, to leverage this phenotypic data. Analytic morphomics utilizes high throughput computational image processing algorithms to provide precise and detailed measurements of organs and body tissues. Within computed tomography (CT) scans, an immense amount of data about patient phenotypes has been largely ignored and unused. By digitally extracting and quantitatively analyzing structural data from these scans, we hypothesize that we can identify patients with an unfavorable prognosis. Objectives: (1) to refine and validate risk prediction models using analytic morphomics in Veterans with chronic liver disease; (2) to increase the throughput and automation of the analytic morphomics image processing algorithms using machine learning; (3) to quantify the clinical impact of a risk-based specialty care clinic triage strategy (using prediction models) versus a standard triage strategy (usual care), using simulation modeling. Methods: This study will use advanced quantitative methods including analytic morphomics and deep learning (a type of machine learning). Aim 1 will refine and validate risk prediction models using analytic morphomics in Veterans with CLD. Aim 2 will examine the role of deep learning to increase the throughput and automation of the image processing algorithms used in Aim 1. Finally, Aim 3 will quantify the clinical impact of a risk-based specialty care clinic triage strategy (using prediction models) versus a standard triage strategy (usual care). Impacts: The Veterans Health Administration (VHA) is in a unique position to link routinely- collected imaging data to important clinical outcomes. This study will lay the groundwork for the use of this currently underused data source in VHA. The use of morphomic data has the potential to improve patient care for not only CLD, but also a variety of conditions. Next Steps: Findings will inform our partners about the potential clinical impact of risk-based triage for specialty care and lay the groundwork for future efforts to implement such an approach.
NIH Reporter Project Information
None at this time.
Cancer, Other Conditions
Treatment - Observational, Prevention, TRL - Applied/Translational
Care Management Tools, Healthcare Algorithms
None at this time.