Decreased cardio-respiratory information transfer is associated with deterioration and a poor prognosis in critically ill patients with sepsis.

Assessing illness severity in the ICU is crucial for early prediction of deterioration and prognosis. Traditional prognostic scores often treat organ systems separately, overlooking the body's interconnected nature. Network physiology offers a new approach to understanding these complex interactions. This study used the concept of transfer entropy (TE) to measure information flow between heart rate (HR), respiratory rate (RR), and capillary oxygen saturation (SpO²) in critically ill sepsis patients, hypothesizing that TE between these signals would correlate with disease outcome. The retrospective cohort study utilized the MIMIC III Clinical Database, including patients who met Sepsis-3 criteria on admission and had 30 minutes of continuous HR, RR, and SpO² data. TE between the signals was calculated to create physiological network maps. Cox regression assessed the 48 relationship between cardiorespiratory network indices and both deterioration (SOFA score increase of ≥2 points at 48 hours) and 30-day mortality. Among 164 patients, higher information flow from SpO² to HR [TE(SpO2→HR)] and reciprocal flow between HR and RR [TE(RR→HR) and TE(HR→RR)] were linked to reduced mortality, independent of age, mechanical ventilation, SOFA score, and comorbidity. Reductions in TE(HR → RR), TE(RR→HR), TE(SpO2→RR), and TE(SpO2→HR) were associated with increased risk of 48-hour deterioration. After adjustment for potential confounders, only TE(HR→RR) and TE(RR→HR) remained statistically significant. The study confirmed that physiological network mapping using routine signals in sepsis patients could indicate illness severity and that higher TE values were generally associated with improved outcomes. XXXX XXXX.