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

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

World J Gastrointest Surg. 2022 Dec 27;14(12):1363-1374. doi: 10.4240/wjgs.v14.i12.1363.

Authors

Xue-Mei Lu^{1

2}, Deng-Shuai Jia¹, Rui Wang³, Qing Yang³, Shan-Shan Jin³, Lan Chen⁴

Affiliations

¹ School of Nursing, Shanghai Jiao Tong University, Shanghai 200025, China.
² Department of Nursing, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai 200080, China.
³ Department of Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai 200080, China.
⁴ Department of Nursing, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai 200080, China. [email protected].

Abstract

Background: Enteral nutrition (EN) is essential for critically ill patients. However, some patients will have enteral feeding intolerance (EFI) in the process of EN.

Aim: To develop a clinical prediction model to predict the risk of EFI in patients receiving EN in the intensive care unit.

Methods: A prospective cohort study was performed. The enrolled patients' basic information, medical status, nutritional support, and gastrointestinal (GI) symptoms were recorded. The baseline data and influencing factors were compared. Logistic regression analysis was used to establish the model, and the bootstrap resampling method was used to conduct internal validation.

Results: The sample cohort included 203 patients, and 37.93% of the patients were diagnosed with EFI. After the final regression analysis, age, GI disease, early feeding, mechanical ventilation before EN started, and abnormal serum sodium were identified. In the internal validation, 500 bootstrap resample samples were performed, and the area under the curve was 0.70 (95%CI: 0.63-0.77).

Conclusion: This clinical prediction model can be applied to predict the risk of EFI.

Keywords: Clinical prediction model; Critical care medicine; Critical care nursing; Enteral feeding intolerance; Nutrition assessment; Nutritional support.