Prognosis and Survival Modelling in Cirrhosis Using Parenclitic Networks

Abstract

Background: Liver cirrhosis involves multiple organ systems and has a high mortality. A network approach to complex diseases often reveals the collective system behaviours and intrinsic interactions between organ systems. However, mapping the functional connectivity for each individual patient has been challenging due to the lack of suitable analytical methods for assessment of physiological networks. In the present study we applied a parenclitic approach to assess the physiological network of each individual patient from routine clinical/laboratory data available. We aimed to assess the value of the parenclitic networks to predict survival in patients with cirrhosis.Methods: Parenclitic approach creates a network from the perspective of an individual subject in a population. In this study such an approach was used to measure the deviation of each individual patient from the existing network of physiological interactions in a reference population of patients with cirrhosis. 106 patients with cirrhosis were retrospectively enrolled and followed up for 12 months. Network construction and analysis were performed using data from seven clinical/laboratory variables (serum albumin, bilirubin, creatinine, ammonia, sodium, prothrombin time and hepatic encephalopathy) for calculation of parenclitic deviations. Cox regression was used for survival analysis.Result: Initial network analysis indicated that correlation between five clinical/laboratory variables can distinguish between survivors and non-survivors in this cohort. Parenclitic deviations along albumin-bilirubin (Hazard ratio = 1.063, p < 0.05) and albumin-prothrombin time (Hazard ratio = 1.138, p < 0.05) predicted 12-month survival independent of model for end-stage liver disease (MELD). Combination of MELD with the parenclitic measures could predict survival better than MELD alone.Conclusion: The parenclitic network approach can predict survival of patients with cirrhosis and provides pathophysiologic insight on network disruption in chronic liver disease.

Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure

“…Indeed, the results of this study corroborate previous research in the field, where changes in organ system connectivity measured using network analysis and other methods were shown to improve traditional scoring systems for predicting patient outcomes in patients with cirrhosis or even detecting subgroups of patients who may respond to therapy (e.g., targeted albumin therapy) [17,18,55,56]. Specifically, a previous study using parenclitic network analysis showed reduced organ system connectivity (based on population-level correlation network mapping) in patients with cirrhosis who did not survive compared with survivors [18].…”

“…We applied Parenclitic network mapping for network mapping based on patients’ clinical or biochemical biomarkers. Parenclitic network mapping is a novel approach for network analysis [17] that facilitates the mapping of individual data points within models constructed from a reference population (e.g., patients who survive ALF). Initially, the correlation between clinical/biochemical biomarkers was assessed in the reference population to determine the expected relationship between the pair of biomarkers.…”

Reduced oxygen saturation entropy is associated with poor prognosis in critically ill patients with sepsis

“…Reduced complexity of cardiac time‐series as well as body temperature have already been reported in non‐survivors with sepsis (de Castilho et al, 2018; Papaioannou et al, 2012, 2013). Future studies can extend this type of computational analysis by including network analysis of parallel physiological variables for providing pathophysiologic insight as well as personalized physio‐markers for prognostication in critically ill patients (Moorman et al, 2016; Zhang et al, 2022).…”

The mechanistic and prognostic implications of heart rate variability analysis in patients with cirrhosis

“…How to cite this article: Abid, N.-U., & Mani, A. R. (2022). The mechanistic and prognostic implications of heart rate variability analysis in patients with cirrhosis.…”

“…Due to the multisystem involvement in cirrhosis, a network physiology approach has been suggested for the optimum assessment of patients suffering from chronic liver failure (Tan et al, 2020 ; Zhang et al, 2022 ). Crucially, a network physiology approach may help develop physio‐markers to create novel prognostic indicators for patients with cirrhosis, which has been the subject of investigation by recent studies (Bhogal et al, 2019 ; Bottaro et al, 2020 ; Chan et al, 2017 ; Jansen et al, 2018 , 2019 ; Mani et al, 2009 ; Satti et al, 2019 ).…”