Background: Reduced functional connectivity of physiological systems is associated with poor prognosis in critically ill patients. However, physiological network analysis is not commonly used in clinical practice and needs further validation.  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 aims to examine the impact of physiological network analysis in prognostication of patients with ALF.

Methods: Data from 640 adult patients admitted to the intensive care unit (ICU) for paracetamol-induced ALF between 2001 and 2012 were extracted from the MIMIC-III database. Parenclitic network analysis was performed on patients’ routine biomarkers and network clusters were identified using the k-clique percolation method.  Statistical analysis was performed to predict the 28-day survival of patients independent of their sequential organ failure assessment (SOFA) score and King’s College Criteria.

Results: There is higher organ system disconnection in non-survivals compared with survivals with a shift toward acidosis. pH regulation was respectively directed toward renal function and respiratory functions in survivors and nonsurvivors (Figure 1). Parenclitic deviation along the blood pH and serum creatinine (HR(hazard ratio) = 187.08, p = 0.001), blood pH and bicarbonate (HR = 78.94, p < 0.001), lactate and glucose level (HR = 1.10, p < 0.001), lactate and heart rate (HR = 1.09, p < 0.001), as well as oxygen saturation (SpO2) and respiratory rate (HR = 1.13, p = 0.018) axes, predicted ICU mortality independent of SOFA. The addition of the parenclitic network indices significantly improved the prognostic value of SOFA.

Conclusion: These results demonstrate that network analysis can provide pathophysiologic insight and predict survival in critically ill patients with paracetamol-induced ALF