× closure
(a) Phase transition diagram between CHB, LC and HCC caused by chronic hepatitis B infection, including acute-on-chronic liver failure (ACLF) and HCC death. Each blue ball represents a patient with the corresponding liver disease. When the ball crosses the peak, the patient experiences a phase transition to the next state. (bd) Build a clinical testing project network for CHB, LC, and HCC. Different colors of nodes represent their degree, and link thickness represents the strength of the correlation between items. The red line indicates a negative correlation between items. (eg) Three radar plots illustrating the topology of the corresponding network. Image source: Houge et al.
Chronic hepatitis B virus (HBV) infection poses a significant threat to global public health, resulting in liver-related morbidity and mortality. Current diagnostic methods for hepatitis B-related diseases, such as laboratory tests, ultrasound, computed tomography (CT), and liver biopsy, often overlap and consume valuable medical resources. To address this challenge, a team of researchers developed an innovative and cost-effective method to diagnose and predict HBV-related diseases based on blood tests.
In a research article published in the journal projectTitled “A Resilience Approach to Diagnosis and Prediction of HBV-Related Diseases Based on Blood Tests,” the scientists introduced a novel measurement method called functional resilience.
By building a complex network model using clinical blood tests, researchers successfully assessed liver status in patients with chronic hepatitis B virus infection. Their approach combines network models and dynamics to identify key indicators and corresponding thresholds, enabling early detection and prevention of disease progression.
Using their method “functional elasticity”, the research team achieved an impressive macro-average accuracy of 84.74%. In contrast, doctors relying solely on their own experience, without the aid of imaging or biopsy, achieved a macroscopic mean accuracy of 55.64%. From an economic perspective, this approach has the potential to save most Chinese patients at least $30 per visit and most U.S. patients $400 compared with traditional diagnostic methods. Globally, annual savings are expected to exceed $10.5 billion.
By comprehensively assessing the patient’s liver status, this innovative diagnostic method reduces excessive imaging examinations, thereby optimizing the use of medical resources in the diagnosis of liver disease. The findings make a significant contribution to the field of prevention and management of hepatitis B-related diseases.
The conclusions of this study reveal the liver function status of patients with chronic hepatitis B (CHB), cirrhosis (LC), and hepatocellular carcinoma (HCC) from a network perspective. By constructing a network based on 25 blood test items, the researchers analyzed the evolutionary dynamics of liver function and identified key indicators and correlation thresholds of key states among CHB, LC, and HCC. This finding has profound implications for future research into the prevention of hepatitis B-related diseases, providing insights into early warning signs of worsening liver function.
The research team simulated progressive liver dysfunction caused by chronic hepatitis B virus infection by removing or changing network links, providing valuable insights into transitions between states. Although the morphology of the critical liver remains unknown, these findings inspire future research and pave the way for improved diagnostic, preventive, and therapeutic strategies.
The impact of this research extends beyond the scientific and medical communities as it addresses pressing global health issues. By simplifying the diagnostic process and reducing healthcare costs, this new approach has the potential to transform the lives of millions of patients affected by hepatitis B-related illnesses around the world.
More information:
Gege Hou et al., A resilient approach to diagnosing and predicting HBV-related disease based on blood testing, project (2023). DOI: 10.1016/j.eng.2023.06.013