Abstract Peripheral oxygen saturation () exhibits a complex pattern of fluctuations during hypoxia, which can be quantified using entropy measures. 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 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 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 signals and normalised to mean 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 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.
Feng et al. (Wed,) studied this question.