Abstract

Peripheral oxygen saturation ( S p O 2 ${S_{{\mathrm{p}}{{\mathrm{O}}_2}}}$ ) exhibits a complex pattern of fluctuations during hypoxia, which can be quantified using entropy measures. S p O 2 ${S_{{\mathrm{p}}{{\mathrm{O}}_2}}}$ entropy analysis provides insights into dynamic physiological regulation by non‐invasively reflecting the body's capacity to adapt to internal or external physiological challenges. However, the interpretation of S p O 2 ${S_{{\mathrm{p}}{{\mathrm{O}}_2}}}$ entropy alone is limited without contextualisation and the degree of physiological challenge encountered (e.g. the severity of hypoxia). This proof‐of‐concept retrospective study analysed continuous 1 Hz S p O 2 ${S_{{\mathrm{p}}{{\mathrm{O}}_2}}}$ recordings extracted from MIMIC‐III dataset's Intensive Care Unit ICU patients with sepsis (n = 164), chronic obstructive pulmonary disease (COPD) (n = 58), acute liver failure (ALF) (n = 59), or cirrhosis (n = 169). Sample entropy was computed directly from raw 20‐min S p O 2 ${S_{{\mathrm{p}}{{\mathrm{O}}_2}}}$ signals and normalised to mean S p O 2 ${S_{{\mathrm{p}}{{\mathrm{O}}_2}}}$ using directional parenclitic deviation (δ), derived from a healthy hypoxia‐exposure reference dataset. Cox‐regression models assessed 30‐day ICU mortality. In sepsis, δ was significantly higher in non‐survivors (hazard ratio (HR) = 2.20, P < 0.0001) and independently predicted 30‐day mortality (HR = 1.79, P < 0.0001). δ was not predictive in the COPD, ALF and cirrhosis cohorts. Unlike other patient groups, the cirrhosis group demonstrated unexpected mean negative δ values, suggesting aberrant regulatory engagement, potentially related to the pathophysiology of hepatopulmonary syndrome. These findings demonstrate that δ provides physiological contexts to entropy‐based S p O 2 ${S_{{\mathrm{p}}{{\mathrm{O}}_2}}}$ analysis. By linking variability to the severity of hypoxia, this framework enables a more interpretable and a potentially clinically applicable biomarker of systemic regulation in critical illnesses. Future validation across diverse cohorts could support its potential to aid in personalised care within intensive care settings.