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

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

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.

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).

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

Enteral nutrition (EN) is a preferred and cost-effective approach to nutritional support[1,2]. When EN is provided, nutrients in the gastrointestinal (GI) tract activate intestinal endocrine cells and lymphoid tissues, which positively contributes to GI function (e.g., movement, digestion, and immunity)[3,4]. However, during the provision of EN, many complications can develop that have an adverse impact on nutritional support[5]. Enteral feeding intolerance (EFI) is a common and primary manifestation among many GI complications.

EFI is the inability to deliver adequate energy or nutrients to patients due to GI symptoms in the absence of mechanical obstruction[6]. EFI develops in 2%-75% of enteral feeding patients in intensive care units (ICUs)[7]. When EFI occurs, prokinetic agents and post-pyloric feeding are recommended[8]. If EFI cannot be attenuated by medications or other feeding access, then EN is reduced or suspended[9,10]. This may result in an inability to attain nutritional goals or in malnutrition.

Therefore, distinguishing high-risk patients before EFI occurs is very important and has a guiding role in clinical practice. Many studies have explored the mechanics and causes for the development of EFI in clinical practice. A review summarized some of the main reasons: (1) Admission diagnosis of burns, head injuries, sepsis, and multi-trauma; (2) Premorbid conditions of disordered glucose metabolism, age, and sex; (3) Electrolyte disorders; and (4) Use of drugs such as sedatives, analgesics, and catecholamines[9]. A recent review of a multicenter and multiyear database indicated that EFI was more likely to occur in burn, cardiovascular/vascular disease, GI disease, and sepsis patients in the ICU[11]. However, in recent years, assessment of EFI at the bedside was driven by clinician opinion, which is still subjective to some extent. This may result in misjudgment of EFI occurrence and have an adverse effect on nutrition delivery and clinical recovery.

A clinical prediction model (CPM) is built upon the use of mathematical formulas to estimate the probability that a particular individual will have a disease or an outcome in the future[12,13]. CPM can assist clinicians in decision-making and developing therapy programs in complex clinical situations and may help patients have better outcomes. Many studies have identified variables associated with EFI, such as diabetes, abdominal surgery, and head injury. This study aimed to analyze different risk factors for EFI occurrence in the ICU and to construct a CPM that would screen high-risk ICU patients to implement early prevention and intervention methods.

We developed a novel practical prognostic instrument for predicting the risk of EFI in the ICU that may support clinicians when making treatment recommendations for patients receiving EN. Development of the model followed established recommendations. We identified three protective predictors, namely age, GI disease, and early feeding. Moreover, two risk factors were determined, namely mechanical ventilation before EN started and abnormal serum sodium. The internally validated area under the curve was 0.70 for the model to predict EFI outcomes.

We developed the CPM using an assembled population from three different ICU departments at one center. We made every effort to enroll patients with different diseases. Therefore, our model could apply to most situations in the ICU. To control for potential bias, the data of every patient were divided into three parts. The basic information was recorded by a researcher, the daily EN data were recorded by clinical nurses, and the ultrasonographic data were recorded by ICU doctors trained in performing ultrasonography. The researcher was unable to obtain the other data before the follow-up ended. In addition, we utilized the quantitative method of content analysis to guarantee the scientific rationality of our study.

Alternative predictors were found from the literature and clinical experts. When we performed univariate analysis, we included predictors with P values smaller than 0.15 with the aim that no possible significant factors were omitted. We determined the potential effective predictors based on the P value and by considering those predictors recommended by experts or that were highly suspected. These predictors were well-defined, easily measured, and routinely available. In internal validation, we used the bootstrap validation to assess discrimination and calibration and repeated the validation 500 times for accuracy.

In our study, we found that older patients were less likely to develop EFI. This result is similar to the results of existing studies[28,29] but contrary to conventional wisdom. After a literature review, expert consultation, and clinical observation, we identified some reasons that explain this counterintuitive result. Older patients are given less EN because of their energy requirements and physical condition. In our study, patients aged 60 years or older received on average less EN per day than younger patients [900 (500, 1200) vs 1000 (500, 1500), respectively]. Critically ill elderly patients have many chronic diseases, such as diabetes, chronic gastroenteritis, and hepatic dysfunction. To promote GI motility and regulate water balance, nutrition teams often use water or rice soup as the initial nutrition for the elderly. Water or rice soup is used for a period of time to facilitate a later transition to an EN emulsion, which may reduce stimulation of the GI tract in elderly patients[42]. The direct relationship between age and EFI requires further experimental analysis to completely understand the relationship.

Similarly, we found that ICU patients with GI disease (e.g., pancreatitis, post-gastrectomy, or upper GI hemorrhage) were less likely to experience EFI. In our study, patients with GI disease were likely given less feed to avoid worsening their health issues [patients with GI disease: 575 (275, 975) vs patients without GI disease: 1000 (725, 1500), P < 0.000]. In clinical practice, the intention of a small volume of EN is not to meet energy requirements but to maintain the structural and functional integrity of the GI tract[43]. Therefore, GI symptoms may be slight and difficult to observe in this circumstance. In addition, medical interventions (e.g., metoclopramide, probiotics, acupuncture, and enema) are administered to patients diagnosed with GI disease[44-46]. This advance treatment may lead to a decreased occurrence of EFI.

A previous attempt to develop a prediction model yielded promising results but had limited applicability because its target population was patients with gastrectomy for gastric cancer rather than ICU patients[47]. Some preventive measures have been implemented to reduce EFI occurrence in ICU patients, such as fat-modified enteral formula and bolus enteral feeding methods[48-50]. However, there is a gap in the knowledge of distinguishing patients at high risk of EFI. Medical workers can apply our model when it is recommended that a patient receives EN. By analyzing the conditions between the period of being admitted to the ICU and receiving EN, patients at high risk are determined and are given a set of preventive measures, which is an effective measure for reducing the occurrence of EFI. Notably, experienced clinical workers already have some knowledge of which patients will be high risk for EFI and have put protective measures into clinical practice. Based on the current nutritional management practices in our center, the predictive model should be used knowing that high-risk patients may have already received preventive measures.

There are potential limitations to our study. Because of time and manpower, we developed the model using a small sample size in a single center. The effect of sepsis, trauma, electrolytes could not be properly addressed because of the small sample size. The differences between these factors between the two groups may be overlooked. Due to the actual situation, the effect of various formula feeds could not be ascertained because of use of several feeding formulas. In addition, the representativeness and predictive performance of our model may have limitations. However, this limit may be slight because the final model includes only five variables. Moreover, the delivery strategy of intermittent or continuous feeding and the temperature of the nutrient solution contribute to EFI occurrence[51]. Our study did not consider these effects because all included patients received room temperature continuous feeding in our medical center. During the study, there may have been some confounding factors that we did not consider, including etiology, medications, and fluids. For future research, these factors should be considered, and we suggest external validation in different centers over additional time periods. In the future, we hope to be able to analyze the effect of individual factors on EFI on the basis of expanding the sample size. In addition, applying our prediction model to additional interventional studies as a tool to optimize clinical management is a long-term goal.

We have developed and internally validated a CPM for predicting the risk of EFI in patients receiving EN in the ICU. The developed nomogram is easy to use and might help clinicians make individualized predictions of each patient’s probability of experiencing EFI. Early identification of patients at high risk of EFI can greatly help doctors and nurses better manage clinical care. Clinical nurses can implement different nursing measures according to each patient’s risk. These measures will ultimately help ICU patients achieve better nutritional support and a quicker recovery.

We thank the patients whose anonymized data were used for this research. We acknowledge all collaborators in this study and all clinical workers in the intensive care units. We thank the nurses responsible for helping us collect data every day. We thank Professor Lu and Professor Fang for their guidance on the research project. We thank Mrs. Kang, the statistician, for her help in the data analysis stage.