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Predicting which patients with cirrhosis will develop overt hepatic encephalopathy: Beyond psychometric testing
Zachary M Saleh1, Elliot B Tapper2
1Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA.
Metabolic Brain Disease
|October 29, 2022
Summary
Identifying covert hepatic encephalopathy is difficult. A new algorithm using electronic health records and patient outcomes may improve prediction and treatment for hepatic encephalopathy across diverse populations.
Area of Science:
- Hepatology
- Neuroscience
- Digital Health
Background:
- Covert hepatic encephalopathy (CHE) diagnosis and progression prediction remain challenging.
- Current psychometric testing for CHE is often inaccurate and difficult to implement in diverse patient groups.
- There is a need for more accessible and reliable methods for CHE assessment.
Purpose of the Study:
- To develop and propose a novel algorithm for identifying covert hepatic encephalopathy (CHE).
- To predict the progression from covert hepatic encephalopathy to overt hepatic encephalopathy (OHE).
- To enable risk-stratification and early therapeutic intervention for patients with CHE.
Main Methods:
- Utilizing easily accessible data from electronic health records.
- Incorporating simple clinical assessment tools.
- Integrating patient-reported outcomes into the diagnostic process.
Main Results:
- The proposed algorithm leverages readily available data for improved CHE detection.
- Patient-reported outcomes enhance the prediction of CHE and its progression to OHE.
- The approach facilitates targeted therapies and monitoring of treatment impact.
Conclusions:
- A novel algorithm integrating EHR data, clinical assessments, and patient-reported outcomes shows promise for CHE management.
- This approach offers a more accessible and potentially accurate method for risk-stratification and early intervention.
- The findings support the use of integrated patient data for improving hepatic encephalopathy care.
