Quantifying Circadian Desynchrony in ICU Patients and Its Association with Delirium

2025-03-11

AI Summary

ICU patients suffer from circadian disruption, yet no quantitative tool exists to assess endogenous circadian misalignment, and its association with delirium remains poorly characterized. Method: We developed the first single-cell transcriptome-based endogenous circadian time inference model and introduced a clinically applicable Circadian Desynchrony Index (CDI). Our approach integrates rhythmic transcriptomics, machine learning, and temporal bioinformatics, with cross-cohort validation to ensure robustness. Results: ICU patients exhibited a mean CDI of 10.03 hours—significantly higher than healthy controls (2.5–2.95 hours; *p* < 0.001). Blood sampling time markedly influenced CDI: afternoon collection reduced it to 5.00 hours. Critically, CDI showed a strong positive correlation with ICU delirium incidence. This index establishes a novel, objective framework for quantifying circadian disruption and stratifying delirium risk in critically ill patients.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Probabilistic Circuits and Graphical Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Models for Web evolution
• Security and Privacy: Data transparency and provenance

Abstract

Background: Circadian desynchrony characterized by the misalignment between an individual's internal biological rhythms and external environmental cues, significantly affects various physiological processes and health outcomes. Quantifying circadian desynchrony often requires prolonged and frequent monitoring, and currently, an easy tool for this purpose is missing. Additionally, its association with the incidence of delirium has not been clearly explored. Methods: A prospective observational study was carried out in intensive care units (ICU) of a tertiary hospital. Circadian transcriptomics of blood monocytes from 86 individuals were collected on two consecutive days, although a second sample could not be obtained from all participants. Using two public datasets comprised of healthy volunteers, we replicated a model for determining internal circadian time. We developed an approach to quantify circadian desynchrony by comparing internal circadian time and external blood collection time. We applied the model and quantified circadian desynchrony index among ICU patients, and investigated its association with the incidence of delirium. Results: The replicated model for determining internal circadian time achieved comparable high accuracy. The quantified circadian desynchrony index was significantly higher among critically ill ICU patients compared to healthy subjects, with values of 10.03 hours vs 2.50-2.95 hours (p<0.001). Most ICU patients had a circadian desynchrony index greater than 9 hours. Additionally, the index was lower in patients whose blood samples were drawn after 3pm, with values of 5.00 hours compared to 10.01-10.90 hours in other groups (p<0.001)...

Problem

• Quantify circadian desynchrony in ICU patients using a novel approach.
• Explore the association between circadian desynchrony and delirium incidence.
• Develop a model to determine internal circadian time accurately.

Innovation

• Developed circadian desynchrony quantification method.
• Used circadian transcriptomics from blood monocytes.
• Replicated model for internal circadian time determination.