Observational Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Dec 27, 2022; 14(12): 1363-1374
Published online Dec 27, 2022. doi: 10.4240/wjgs.v14.i12.1363
Development of a prediction model for enteral feeding intolerance in intensive care unit patients: A prospective cohort study
Xue-Mei Lu, Deng-Shuai Jia, Rui Wang, Qing Yang, Shan-Shan Jin, Lan Chen
Xue-Mei Lu, Deng-Shuai Jia, School of Nursing, Shanghai Jiao Tong University, Shanghai 200025, China
Xue-Mei Lu, Lan Chen, Department of Nursing, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai 200080, China
Rui Wang, Qing Yang, Shan-Shan Jin, Department of Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai 200080, China
Author contributions: Lu XM contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing the original draft, and project administration; Jia DS contributed to conceptualization, methodology, investigation, and writing the original draft; Wang R and Yang Q contributed to methodology, investigation, and resources; Jin SS contributed to investigation and resources; Chen L contributed to conceptualization, methodology, resources, review and editing of the manuscript, supervision, and project administration; All authors read and approved the final manuscript.
Institutional review board statement: The study protocol was approved (numbered 2020KY230) by the appropriate ethics committee (Medical Ethics Committee of Shanghai General Hospital) on December 23, 2020.
Informed consent statement: Before we enrolled patients, informed consent was obtained from the patient or next of kin. Since we needed to complete daily ultrasonography of patients, we needed to obtain their informed consent.
Conflict-of-interest statement: The authors declare having no conflicts of interest.
Data sharing statement: If there is a need to get the dataset, please contact Xue-Mei Lu (lu_xm1118@163.com). The information of the patients in the dataset is anonymized.
STROBE statement: The authors have read the STROBE Statement–checklist of items, and the manuscript was prepared and revised according to the STROBE Statement–checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Lan Chen, PhD, Chief Nurse, Department of Nursing, Shanghai General Hospital, Shanghai Jiao Tong University, No. 86 Wu Jin Road, Shanghai 200080, China. 13636317690@126.com
Received: August 27, 2022
Peer-review started: August 27, 2022
First decision: September 25, 2022
Revised: October 15, 2022
Accepted: November 16, 2022
Article in press: November 16, 2022
Published online: December 27, 2022
Processing time: 122 Days and 2.6 Hours
ARTICLE HIGHLIGHTS
Research background

Enteral nutrition (EN) is essential for critically ill patients, but some patients develop enteral feeding intolerance (EFI). Intolerance can hinder a patient’s energy intake and recovery. Therefore, predicting EFI is of vital importance in clinical practice.

Research motivation

Determining which patients are at high risk of developing EFI based on their current physical condition and medical treatment will allow physicians and nurses to individualize medical care and begin EFI preventative measures for the high-risk patients.

Research objectives

To develop a clinical prediction model (CPM) to predict the risk of EFI in patients receiving EN in the intensive care unit (ICU). We currently know that many factors can influence the development of EFI.

Research methods

A prospective cohort study was performed, and we prospectively recorded enrolled patients’ data. Prospective cohort studies can more realistically document patient data and clinical responses, reducing human intervention. We used ultrasound measurement of the antrum cross-sectional area to measure gastric residual volume, which can effectively reduce the occurrence of complications and increase the efficiency of feeding.

Research results

We developed and internally validated a CPM for predicting the risk of EFI in patients receiving EN in the ICU. After univariate and multivariate analyses, five factors were used for the CPM, including age, gastrointestinal disease, early feeding, mechanical ventilation before EN started, and abnormal serum sodium when EN started.

Research conclusions

This model can help clinical workers to identify patients at high risk for EFI earlier, which will allow these patients to receive preventative measures in advance.

Research perspectives

In the future, an increased sample size and analyzing more variables will develop a more accurate clinical predictive model. Prospective cohort studies and randomized control studies are the best methods for the future research.