Can physiological network mapping reveal pathophysiological insights into emerging diseases? Lessons from COVID-19

Fig 1. Bonferroni-Corrected Correlation Network Maps Comparing Survivors (n = 148) and Non-Survivors (n = 54) of COVID-19 during the first wave of the pandemic. Each link shows a statistically significant Pearson correlation between two biomarkers after Bonferroni correlation for the total number of multiple comparisons. Abbreviations: O2Sat – capillary oxygen saturation; Hb – Haemoglobin; WBC – White Blood Cell count; Platelet counts – Platelet count; CRP – C-reactive protein; Cr – Serum creatinine; BUN – Blood urea nitrogen; AST – Aspartate aminotransferase; ALT – Alanine aminotransferase; ALP – Alkaline phosphatase; Na ⁺ – Sodium; K ⁺ – Potassium; INR – International normalized ratio; pH – Arterial hydrogen ion concentration; PCO2 – Arterial partial pressure of carbon dioxide; HCO3 – Arterial bicarbonate concentration.

Fig 2. Age-Matched, Bonferroni-Corrected Correlation Network Maps Comparing COVID-19 Survivors (n = 54) and Non-Survivors (n = 54). Age matching for the groups was performed using an automated algorithm to pair patients according to their age. Each link shows a statistically significant Pearson correlation between two biomarkers after Bonferroni correlation for the total number of multiple comparisons. Abbreviations: O2Sat – capillary oxygen saturation; Hb – Haemoglobin; WBC – White Blood Cell count; Platelet counts – Platelet count; CRP – C-reactive protein; Cr – Serum creatinine; BUN – Blood urea nitrogen; AST – Aspartate aminotransferase; ALT – Alanine aminotransferase; ALP – Alkaline phosphatase; Na ⁺ – Sodium; K ⁺ – Potassium; INR – International normalized ratio; pH – Arterial hydrogen ion concentration; PCO2 – Arterial partial pressure of carbon dioxide; HCO3 – Arterial bicarbonate concentration.