Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure

Abstract

Background: A healthy individual has a high degree of functional connectivity between organ systems, which can be represented graphically in a network map. Disruption of this system connectivity is associated with mortality in life-threatening acute illnesses, demonstrated by a network approach. However, this approach has not been applied to chronic multisystem diseases and may be more reliable than conventional individual organ prognostic scoring methods. Cirrhosis is a chronic disease of the liver with multisystem involvement. Development of an efficient model for prediction of mortality in cirrhosis requires a profound understanding of the pathophysiologic processes that lead to poor prognosis. In the present study, we use a network approach to evaluate the differences in organ system connectivity between survivors and non-survivors in a group of well-characterized patients with cirrhosis. Methods: 201 patients with cirrhosis originally referred to the Clinic five at the University Hospital of Padova, with 13 clinical variables available representing hepatic, metabolic, haematopoietic, immune, neural and renal organ systems were retrospectively enrolled and followed up for 3, 6, and 12 months. Software was designed to compute the correlation network maps of organ system interaction in survivors and non-survivors using analysis indices: A. Bonferroni corrected Pearson's correlation coefficient and B. Mutual Information. Network structure was quantitatively evaluated using the measures of edges, average degree of connectivity and closeness, and qualitatively using clinical significance. Pair-matching was also implemented as a further step after initial general analysis to control for sample size and Model for End-Stage Liver Disease (MELD-Na) score between the groups. Results: There was a higher number of significant correlations in survivors, as indicated by both the analysis indices of Bonferroni corrected Pearson's correlation coefficient and the Mutual Information analysis. The number of edges, average degree of connectivity and closeness were significantly higher in survivors compared to non-survivors group. Pair-matching for MELD-Na was also associated with increased connectivity in survivors compared to non-survivors over 3 and 6 months follow up. Conclusion: This study demonstrates the application of a network approach in evaluating functional connectivity of organ systems in liver cirrhosis, demonstrating a

Prognosis and Survival Modelling in Cirrhosis Using Parenclitic Networks

“…Their results showed that in a cohort of critically ill patients, survivors consistently exhibited a higher number of edges and clusters compared to non-survivors in their organ connectivity network structures (Asada et al, 2016). In a recent report, we used a similar approach and showed that functional connectivity of organ systems is significantly disrupted in patients with cirrhosis who did not survive during 12-month follow up (Tan et al, 2020). However, the methodology of these studies is based on correlation analysis of a population of patients and cannot be used for mapping the network connectivity at the level of individual patients.…”

“…Seven standard clinical variables representing unique physiological functions or clinical feature were collected (serum albumin, ALB; total bilirubin, Bil; prothrombin time, PT; serum creatinine, Cr; ammonia, NH 4 ; serum sodium, Na; and hepatic encephalopathy, HE) based on a previous study (Tan et al, 2020). HE was classified as unimpaired, minimal and overt HE according to Montagnese et al (Montagnese et al, 2004;Vilstrup et al, 2014).…”

Health and Disease Are Dynamic Complex-Adaptive States Implications for Practice and Research

“…Alteration in the interactions within a layer will not only change the dynamics of that layer but also those of interacting layers, and thereby shift the integrated function of the system-as-a-whole ( 39 , 40 ). Physiological network properties have pragmatic clinical implications as exemplified in patients with liver failure ( 41 ) or severe critical illness ( 42 )—the breakdown/loss of network interconnection between the layers of system organization is associated with poor outcomes.…”

Network Physiology of Exercise: Vision and Perspectives

“…In recent years, the Network Physiology framework has been utilized in various fields of basic Physiology and Clinical Medicine, including multiple organ failure and sepsis in critically ill patients (Asada et al, 2016;Moorman et al, 2016), neonatal intensive care (Lavanga et al, 2020;Lucchini et al, 2020), liver disease (Tan et al, 2020), epilepsy and neurological disorders (Lin et al, 2020), diabetes and obesity (Podobnik et al, 2020;Prats-Puig et al, 2020), cancer (Liu et al, 2020), or psychiatry (Bolton et al, 2020), and has the potential for broad applications in the field of Exercise Physiology and Sports Medicine to uncover how the key physiological systems interact pairwise, that is, which links are the major mediators in a given network and how these links adjust their strength with accumulation of fatigue, after a training intervention, or in response to a certain pathological condition (e.g., musculoskeletal injury and neurodegenerative disease).…”