Reduced functional connectivity of physiological systems is associated with poor prognosis is critically ill patients. However, physiological network analysis is not commonly used in clinical practice and awaits quantitative evidence. Acute liver failure (ALF) is associated with multiorgan failure and mortality. Prognostication in ALF is highly important for clinical management but is currently dependent on models that do not consider the interaction between organ systems. This study is aimed to examine the impact of physiological network analysis, in prognostication of patients with ALF.

Data from 640 adult patients admitted to the ICU for paracetamol-induced ALF were extracted from the MIMIC-III database. Parenclitic network analysis was performed on the routine biomarkers and network clusters were identified using the k-clique percolation method.

Network analysis showed that the liver function biomarkers were more clustered in survivors than in non-survivors. Arterial pH was also found to cluster with serum creatinine and bicarbonate in survivors compared with non-survivors, where it clustered with respiratory nodes indicating physiologically distinctive compensatory mechanism. Deviation along the pH-bicarbonate and pH-creatinine axes could significantly predict mortality independent of current prognostic indicators. These results demonstrate that network analysis can provide pathophysiologic insight and predict survival in critically ill patients with ALF.

The authors have declared no competing interest.

The authors received no funding to conduct this study

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The data analysed in this study were sourced from the third version of the Medical Information Mart for Intensive Care (MIMIC-III) following training, application, and acquisition of the required permissions. MIMIC-III data has been deidentified in accordance with Health Insurance Portability and Accountability Act (HIPAA) standards and the project approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center and the MIT (IRB protocol nos. 2001P001699/14 and 0403000206, respectively). The authors involved in data extraction completed mandatory online ethics training at MIT and were credentialled (IDs 10304625 and 48067739).

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Data are available upon request from the corresponding author.