Physiological network approach to prognosis in cirrhosis: A shifting paradigm.
Tope OyeladeKevin P MooreAlireza ManiPublished in: Physiological reports (2024)
Decompensated liver disease is complicated by multi-organ failure and poor prognosis. The prognosis of patients with liver failure often dictates clinical management. Current prognostic models have focused on biomarkers considered as individual isolated units. Network physiology assesses the interactions among multiple physiological systems in health and disease irrespective of anatomical connectivity and defines the influence or dependence of one organ system on another. Indeed, recent applications of network mapping methods to patient data have shown improved prediction of response to therapy or prognosis in cirrhosis. Initially, different physical markers have been used to assess physiological coupling in cirrhosis including heart rate variability, heart rate turbulence, and skin temperature variability measures. Further, the parenclitic network analysis was recently applied showing that organ systems connectivity is impaired in patients with decompensated cirrhosis and can predict mortality in cirrhosis independent of current prognostic models while also providing valuable insights into the associated pathological pathways. Moreover, network mapping also predicts response to intravenous albumin in patients hospitalized with decompensated cirrhosis. Thus, this review highlights the importance of evaluating decompensated cirrhosis through the network physiologic prism. It emphasizes the limitations of current prognostic models and the values of network physiologic techniques in cirrhosis.
Keyphrases
- heart rate variability
- heart rate
- liver failure
- network analysis
- heart failure
- poor prognosis
- ejection fraction
- blood pressure
- hepatitis b virus
- mental health
- end stage renal disease
- healthcare
- public health
- resting state
- functional connectivity
- cardiovascular events
- newly diagnosed
- risk factors
- climate change
- chronic kidney disease
- mass spectrometry
- type diabetes
- cardiovascular disease
- low dose
- atrial fibrillation
- high dose
- bone marrow
- physical activity
- multiple sclerosis
- health information
- machine learning
- room temperature
- health promotion
- human health