Feeding Intolerance Risk Prediction Model in Patients With Enteral Nutrition Through Nasogastric Tube

Xiao Jie Chen

Status: Not yet enrolling

Conditions: Enteral Nutrition

Treatments: Behavioral: enteral nutrition

Study type: Observational

Funder types: Other

Identifiers

NCT05900167

IIT-2023-0054

Details and patient eligibility

About

To explore the risk factors of enteral feeding intolerance in critically ill patients, build a risk prediction model and verify it, in order to provide reference for early identification and screening of high-risk groups

Full description

Based on the previous literature study, the risk factors of enteral feeding intolerance in critically ill patients were obtained, and the general demographic, disease and treatment information of patients were collected. Four machine learning algorithms, namely traditional logistic regression, random forest, support vector machine and naive Bayes, were used to construct risk prediction models, and the optimal model was selected and verified by comparing the model performance

Enrollment: 442 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• The nutritional status of the patient is assessed by the doctor and the need for enteral nutrition is determined;Patients with first nasogastric tube insertion and successful enteral feeding;There was no gastrointestinal discomfort before enteral nutrition;Patients (or family members) authorized to participate in this study and signed informed consent

Exclusion criteria

• Previous gastrointestinal disease or gastrointestinal surgery;enteral nutrition through gastrostomy tube or jejunostomy tube;discontinuation or suspension of enteral nutrition for special reasons other than feeding intolerance;Patients with serious lack of clinical data

Trial design

442 participants in 1 patient group

enteral nutrition group

Treatment:

Behavioral: enteral nutrition