Predictive Value of Gastrointestinal Blood Flow for Enteral Nutrition Intolerance in Critically Ill Patients

Overview

This study aims to explore the correlation between gastrointestinal blood flow and the incidence of enteral nutrition intolerance (ENI) and its symptoms in critically ill patients, construct and compare predictive models including blood flow parameters, and evaluate their incremental predictive value.

Enteral nutrition (EN) is the preferred route of nutritional support for critically ill patients. However, the occurrence of enteral nutrition intolerance (ENI) often limits its efficacy and interrupts nutritional supply. Current clinical assessment methods and existing predictive models for ENI mostly rely on subjective or delayed indicators. Normal gastrointestinal function is highly dependent on adequate blood perfusion and unobstructed venous return , but current research pays insufficient attention to the status of gastrointestinal blood flow. Point-of-care ultrasound (POCUS), due to its dynamic and visual nature, can be used to objectively evaluate these gastrointestinal indicators. This study is designed as a prospective observational cohort study involving Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine. Researchers will perform bedside ultrasound evaluations at four specific time points: upon ICU admission, and on Day 1, Day 4, and Day 7 of enteral nutrition. The ultrasound assessments will measure various hemodynamic parameters including diameter, time-averaged maximum velocity, blood flow, and VExUS scores of major vessels such as the celiac artery (CA), superior mesenteric artery (SMA), inferior vena cava (IVC), hepatic vein (HV), and portal vein (PV). The ultimate goal of this study is to employ machine learning algorithms to construct and compare three predictive models: a clinical indicator model, a blood flow parameter model, and a combined clinical-blood flow model. By doing so, the study will explore the independent predictive value of gastrointestinal blood flow for ENI and its symptoms in critically ill patients, evaluate the incremental value of adding blood flow parameters to the prediction models, and validate the models using an external dataset.

Conditions