Development and validation of a predictive model for diarrhea in ICU patients with enteral nutrition

Author:

Chen Qiuchan1ORCID,Chen Yuzhen2,Wang Haiqin1,Huang Jing2,Ou Xiuli2,Hu Jieshan1,Yao Xiaohong1,Guan Lijun1

Affiliation:

1. Intensive Care Unit Jiangmen Central Hospital Jiangmen China

2. Nursing Department Jiangmen Central Hospital Jiangmen China

Abstract

AbstractBackgroundThe aim of this study was to build and validate a risk prediction model for diarrhea in patients in the intensive care unit (ICU) receiving enteral nutrition (EN) by identifying risk factors for diarrhea in these patients.MethodsThe risk factors for diarrhea were analyzed to build a prediction model for EN diarrhea in patients in the ICU based on the data collected from 302 patients receiving EN in the ICU. Subsequently, the model was validated by the area under the curve.ResultsIn this study, the collected data were divided into two groups: a derivation cohort and a validation cohort. The results showed that 54.03% (114) of patients had diarrhea in the derivation cohort and 56.04% (51) of patients had diarrhea in the validation cohort. Moreover, days of EN, high urea nitrogen levels, probiotics, respiratory system disease, and daily doses of nutrient solution were included as predictive factors for diarrhea in patients receiving EN in the ICU. The predictive power of the model was 0.81 (95% CI, 0.752~0.868) in the derivation cohort and 0.736 (95% CI, 0.634~0.837) in the validation cohort.ConclusionIn accordance with the predictive factors, the model, characterized by excellent discrimination and high accuracy, can be used to clinically identify patients in the ICU with a high risk of EN diarrhea.

Publisher

Wiley

Subject

Nutrition and Dietetics,Medicine (miscellaneous)

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Diarrhövorhersagemodell für enteral ernährte Intensivpatienten vorgestellt;Aktuelle Ernährungsmedizin;2023-10

2. The future of artificial intelligence in clinical nutrition;Current Opinion in Clinical Nutrition & Metabolic Care;2023-08-30

3. Gastrointestinal failure, big data and intensive care;Current Opinion in Clinical Nutrition & Metabolic Care;2023-06-20

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