| Title: |
Parenclitic network mapping predicts survival in critically ill patients with sepsis |
| Authors: |
Ito, Emily; Oyelade, Tope; Wikner, Matthew; Liu, Jinyuan; Lilaonitkul, Watjana; Mani, Ali R |
| Source: |
Physiological Reports , 13 (11) , Article e70407. (2025) |
| Publication Year: |
2025 |
| Collection: |
University College London: UCL Discovery |
| Subject Terms: |
Intensive care; network; network physiology; parenclitic; sepsis; survival |
| Description: |
Sepsis is a complex disease involving multiple organ systems. A network physiology approach to sepsis may reveal collective system behaviors and intrinsic organ interactions. However, mapping functional connectivity for individual patients has been challenging due to the lack of analytical methods for evaluating physiological networks using routine clinical and laboratory data. This study explored the use of parenclitic network mapping to assess organ connectivity and predict sepsis outcomes based on routine laboratory data. Data from 162 sepsis patients meeting Sepsis-3 criteria were retrospectively analyzed from the MIMIC-III database. Fifteen physiological variables representing organ systems were used to construct organ network connectivity through correlation analysis. Correlation analysis identified 7 interactions linked to 30-day survival. Parenclitic network analysis was used to measure deviations in individual patients' correlations between organ systems from the reference physiological interactions observed in survivors. Parenclitic deviations in the pH-bicarbonate axis (hazard ratio = 2.081, p < 0.001) and pH-lactate axis (hazard ratio = 2.773, p = 0.024) significantly predicted 30-day mortality, independent of the Sequential Organ Failure Assessment (SOFA) score and ventilation status. This study highlights the potential of parenclitic network mapping to provide insights into sepsis pathophysiology and differences in organ system connectivity between survivors and non-survivors independent of sepsis severity and mechanical ventilation status. |
| Document Type: |
article in journal/newspaper |
| File Description: |
text |
| Language: |
English |
| Relation: |
https://discovery.ucl.ac.uk/id/eprint/10209499/ |
| Availability: |
https://discovery.ucl.ac.uk/id/eprint/10209499/1/Physiological%20Reports%20-%202025%20-%20Ito%20-%20Parenclitic%20network%20mapping%20predicts%20survival%20in%20critically%20ill%20patients%20with%20sepsis.pdf; https://discovery.ucl.ac.uk/id/eprint/10209499/ |
| Rights: |
open |
| Accession Number: |
edsbas.C10AF21B |
| Database: |
BASE |