A Resilience Approach for Diagnosing and Predicting HBV-Related Diseases Based on Blood Tests

Abstract

Chronic hepatitis B virus (HBV) infection, which threatens global public health, is a major contributor to liver-related morbidity and mortality. Examinations for liver diseases related to chronic HBV infection—including laboratory tests, ultrasounds, computed tomography (CT), and liver biopsies—may take up medical resources, particularly since they overlap in most instances. Thus, there is an urgent need to establish an economical and effective diagnosis method in order to streamline the medical process for HBV-related diseases. Using complex network models constructed based on clinical blood tests, we provide such a method by defining the novel measure of functional resilience to assess patients’ liver conditions. By combining network models and dynamics, we discovered the pivotal items and their corresponding thresholds, which can guide further research on preventing disease deterioration in critical states of these diseases. The macro-averaged precision of our method, functional resilience, is 84.74%, whereas the macro-averaged precision of physicians’ experience without assistance from imaging or biopsy is 55.63%. From an economic perspective, our approach could save the equivalent of at least 30 USD per visit for most Chinese patients and at least 400 USD per visit for most US patients, compared with general diagnostic methods. Globally, this will add to savings of at least 10.5 billion USD annually. Our method can comprehensively evaluate the condition of patients’ livers and help avert the waste of medical resources during the diagnosis of liver disease by reducing excessive imaging exams.