Development of a Novel Algorithm for Real-Time Bedside Assessment of Insulin Resistance in Critically Ill Patients Based on Continuous Glucose Monitoring Data: A Multicenter, Observational, Prospective Study
Development of a Novel Algorithm for Real-Time Bedside Assessment of Insulin Resistance in Critically Ill Patients Based on Continuous Glucose Monitoring Data: A Multicenter, Observational, Prospective Study
This study aims to develop and validate a novel algorithm for the real-time assessment of insulin resistance in critically ill patients using Continuous Glucose Monitoring (CGM). Current methods for assessing insulin resistance are often invasive or unfeasible in the intensive care setting. By analyzing the dynamic correlation between CGM readings and reference blood glucose fluctuations, the investigators seek to construct a new algorithmic metric. The study will further evaluate the association of this new metric with established insulin resistance indices, organ function, and patient clinical outcomes.
Insulin resistance (IR) is prevalent among critically ill patients, particularly those with sepsis, and is significantly associated with increased mortality, prolonged length of stay, and infectious complications. Consequently, real-time and accurate bedside monitoring of the degree of insulin resistance in this population is of paramount importance.
Current clinical modalities for assessing insulin resistance (IR) in critical illness remain insufficient. The "gold standard" hyperinsulinemic-euglycemic clamp (HEC) is restricted by invasiveness, complexity, and cost, while its artificial steady-state fails to reflect glucose dynamics under acute stress. Alternatively, HOMA-IR is a static, hepatic-focused measure; its reliance on fasting baselines is often unfeasible due to continuous nutrition, precluding accurate assessment of peripheral glucose disposal. Similarly, the Oral Glucose Tolerance Test (OGTT) is limited by gastrointestinal dysmotility and confounded by stress-induced insulin dysregulation. Consequently, a novel strategy for real-time, dynamic, and bedside IR assessment is urgently required to overcome these limitations.
This study aims to develop a novel algorithm for the real-time, bedside assessment of insulin resistance utilizing continuous glucose monitoring (CGM) data. A primary focus is the evaluation of the capability of CGM to capture dynamic glycemic fluctuations; specifically, the correlation between the magnitude of change in reference blood glucose between time points and the concurrent change in CGM readings will be analyzed. Based on the concordance of these dynamic variations, a new algorithmic metric is to be constructed. Subsequently, the association of this metric with established insulin resistance indices, organ function, and patient prognosis will be investigated to validate its clinical utility as a minimally invasive tool for monitoring metabolic status in critically ill patients.
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