ArticleFrontiers in physiology2020
Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure.
Article in Frontiers in physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
- Interpreting peripheral oxygen saturation variability in critical illness: A directional framework adjusted for hypoxia severity.Experimental physiology · 2026Article
- Hepatoprotective Effect of Silymarin Herb in Prevention of Liver Dysfunction Using Pig as Animal Model.Nutrients · 2025Review
- Parenclitic network mapping predicts survival in critically ill patients with sepsis.Physiological reports · 2025Article
- Can physiological network mapping reveal pathophysiological insights into emerging diseases? Lessons from COVID-19.PloS one · 2025Article
- Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure.Scientific reports · 2024Article
- Physiological network approach to prognosis in cirrhosis: A shifting paradigm.Physiological reports · 2024Review
- Application of short-term analysis of skin temperature variability in prediction of survival in patients with cirrhosis.Frontiers in network physiology · 2023Article
- The mechanistic and prognostic implications of heart rate variability analysis in patients with cirrhosis.Physiological reports · 2022Review
- Prognosis and Survival Modelling in Cirrhosis Using Parenclitic Networks.Frontiers in network physiology · 2022Article
- A Sedentary and Unhealthy Lifestyle Fuels Chronic Disease Progression by Changing Interstitial Cell Behaviour: A Network Analysis.Frontiers in physiology · 2022Article
- The New Field of Network Physiology: Building the Human Physiolome.Frontiers in network physiology · 2021Article
- Health and Disease Are Dynamic Complex-Adaptive States Implications for Practice and Research.Frontiers in psychiatry · 2021Article
- Network Physiology of Exercise: Vision and Perspectives.Frontiers in physiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA 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.
methods201 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.
resultsThere 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.
conclusionThis study demonstrates the application of a network approach in evaluating functional connectivity of organ systems in liver cirrhosis, demonstrating a significant degree of network disruption in organ systems in non-survivors. Network analysis of organ systems may provide insight in developing novel prognostic models for organ allocation in patients with cirrhosis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.