To identify risk factors for ENFI in intensive care unit patients, this study conducted a systematic search of the databases PubMed, Web of Science, and China Knowledge Network (CNKI) from the inception of the databases to January 30, 2024, using the Medical Subject Headings (MeSH) and free-text terms. Two members of the research team independently screened the literature based on the Joanna Briggs Institute (JBI) checklist and the Johns Hopkins University Evidence Assessment Criteria, with disagreements adjudicated by a third party. The literature-specific screening process is shown in the PRISMA flowchart (Figure 2). Eighteen candidate risk factors were extracted from 18 selected high-quality articles. These risk factors were refined through two rounds of the expert meeting method, resulting in the identification of 26 potential risk factors for enteral nutrition intolerance. These factors included clinical baseline data, biochemical markers, and intervention-related variables, and the specific entries are detailed in Tables 1, 2. This multistage approach ensured the methodological rigor and clinical relevance of the study. To ensure the accuracy and stability of the model, the sample size was calculated based on the requirement that it should be at least 5–10 times the number of independent variables. Considering 26 influencing factors, an ENFI incidence rate of 38%, and a 10% sample attrition rate, at least 376 samples were required. In practice, this study included 487 samples, meeting the sample size requirement. We divided the samples into a training set and a test set in an 8:2 ratio.

According to the diagnostic criteria for enteral nutrition feeding intolerance (ENFI) established by the Abdominal Problems Working Group of the European Society of Intensive Care Medicine (15), combined with clinical practice, the diagnostic criteria for ENFI in ICU patients were defined as follows: (1) Failure to achieve the target caloric intake of at least 20 kcal/(kg·d) within 72 h after initiating enteral nutrition; (2) Suspension or discontinuation of enteral nutrition due to gastrointestinal symptoms including abdominal distension (IAP ≥ 12 mmHg), vomiting (expulsion of gastric contents through the mouth occurring once or more), or diarrhea (≥3 episodes of loose watery stools per 24-h period, with each stool volume >200 g); or (3) Gastric residual volume (GRV) monitoring every 6 h, with either a single GRV measurement ≥200 mL or cumulative GRV exceeding 500 mL within 24 h. In this study, GRV was established as a secondary observational indicator, which is not used alone to assess enteral nutrition intolerance, but needs to be used in conjunction with other outcome indicators. This indicator was retained based on institutional research protocols, but according to the American Society for Parenteral and Enteral Nutrition (ASPEN) recommendations, GRV needs to be interpreted with caution and weighted strictly in the assessment system to ensure the scientific validity of the study conclusions. The assessment, based on the medical records from the first 7 days of enteral feeding, was uniformly applied to both the training and validation sets. It was conducted independently by a nutrition nurse specialist, a critical care specialist, and the researcher, with the diagnosis of ENFI requiring agreement from at least two of the evaluators.

We used Python 3.9 to build and evaluate the prediction models based on the results of the influencing factor analysis. The first step in the machine learning process was to import the model-selection module and initialize the environment by defining the data frame, target variable (feeding tolerance/intolerance), training set (80%), and test set (20%). Bootstrap stability verification was performed on the included features. The stability of the selected frequency for each feature in the resampling is greater than 95%, indicating that the importance of the selected feature is highly confident. To ensure the generalization ability of the model and avoid data leakage, the hierarchical random segmentation strategy was used to divide the dataset to ensure that all preprocessing (e.g., imputation, normalization) was only fitted on the training set. We used grid search and cross-validation on the training set to enhance the model’s performance and lower the likelihood of overfitting. We divided the dataset into an 80% training set and a 20% test set. Models were built using three algorithms: LR, SVM, and RF.

Table 4 shows the performance metrics on the training set for the three models built using different methods. Among them, the AUC of the RF model is 0.9511, and the F1 score is 0.9446, achieving the highest scores among the three groups of models. The ROC curves for each model on the training set are shown in Figure 3. We evaluated the model’s calibration using the calibration curve Brier score. Figure 4 displays the results of the calibration curve, which visualizes the model’s calibrability. The Brier score (0.0463) is a direct measure of probabilistic prediction accuracy, indicating that the model is well calibrated overall.

We used resampling methods to validate the three models. The results of the test set showed that the RF model AUC of 0.982. Comprehensive analysis indicated that the RF model performed the best on the test set, as detailed in Table 5. The ROC curves for each model in the test set are shown in Figure 5.

The APACHE II score is an authoritative indicator for assessing the severity of illness and predicting prognosis in ICU patients. It is widely used in clinical practice, with higher scores indicating more severe illness and a worse prognosis (17). The more severe the patient’s condition, the more intense the systemic stress response, leading to pronounced vasoconstriction of splanchnic vessels, gastrointestinal mucosal ischemia, and even erosion, thereby impairing gastrointestinal function. On the other hand, the body enters a hypercatabolic state, triggering the breakdown, consumption, and loss of tissue proteins, resulting in hypoalbuminemia. This condition induces gastrointestinal mucosal edema, further exacerbating mucosal injury and ultimately reducing enteral nutrition tolerance in ICU patients (18). It was found that the intolerance group’s APACHE II score (33.29 ± 10.41) was significantly higher than the tolerance group’s (18.98 ± 6.41). Statistical analysis also confirmed that the APACHE II score is a separate risk factor for ENFI in ICU patients (p < 0.05), which is in line with what other research has found (2). Routine APACHE II scoring within 24 h of ICU admission can stratify high-risk patients, prompting closer EN monitoring and early interventions.

In this study, the IAP in the tolerance group was 7.29 ± 1.70 mmHg, while in the intolerance group, it was 11.02 ± 1.96 mmHg. In the intolerance group, 82 patients (46.86%) had intra-abdominal hypertension (IAP > 12 mmHg), but no cases of abdominal compartment syndrome (IAP > 20 mmHg) were observed. The fact that most patients suffered from respiratory or neurological diseases may explain this phenomenon. Compared to patients with gastrointestinal diseases, the increase in IAP in these patients was relatively mild, but the IAP in the intolerance group was still significantly higher than that in the tolerance group.

IAP (19) refers to the pressure within the abdominal cavity, and its increase can be attributed to factors such as increased organ volume, increased fluid volume, and the use of mechanical ventilation. The gastrointestinal tract is one of the most sensitive organs to increased IAP. As IAP rises, mesenteric blood flow decreases, and venous return is obstructed, leading to intestinal edema and impaired intestinal function, resulting in gastrointestinal adverse effects (20). Clinically, we often estimate IAP by measuring gastric, superior vena cava, inferior vena cava, or bladder pressure. Bladder pressure is considered the “gold standard” for IAP monitoring due to its simplicity, non-invasiveness, accuracy, and minimal influence by human factors or the disease itself (21). This study also used bladder pressure as a proxy for IAP. Healthcare providers should regularly monitor IAP in clinical practice and actively seek causes and interventions for patients with high IAP.

In this study, the blood glucose level in the intolerance group was 9.34 ± 3.50 mmol/L, higher than that in the tolerance group (7.34 ± 3.33 mmol/L). For critically ill patients, hyperglycemia may result not only from pre-existing diabetes but also from various other factors (22). Stress-induced hyperglycemia refers to elevated blood glucose levels in patients without a history of diabetes, occurring in response to severe trauma, shock, cardiovascular accidents, or other stressors (23). Elevated blood glucose levels can reflexively reduce the tension of the gastric antrum smooth muscle, leading to decreased gastric motility and symptoms such as gastric retention. Furthermore, high blood glucose can make the pylorus work harder, which can make the contractions of the stomach and duodenum not work together properly. Such conditions can cause problems with emptying the stomach and greatly raise the risk of ENFI (18, 28). Therefore, healthcare providers should pay close attention to blood glucose monitoring in critically ill ICU patients. When hyperglycemia occurs, appropriate measures should be taken to maintain blood glucose within a relatively stable range, which can help reduce the incidence of ENFI.

The results of this study indicate that ICU patients on mechanical ventilation are more likely to experience ENFI, and the use of mechanical ventilation is a risk factor for ENFI in ICU patients (p < 0.05). Mechanical ventilation is an artificial support system that controls or alters a patient’s spontaneous breathing movements. Its purpose is to maintain airway patency, improve ventilation and oxygenation, and prevent carbon dioxide retention and hypoxia. It is a common treatment method for critically ill patients in clinical practice (24). High levels of positive end-expiratory pressure (PEEP) can cause organs around the heart to have poor blood flow, lower cardiac output, and gastrointestinal ischemia. Such condition can slow the movement of food through the digestive tract or damage the mucosa, which can set off ENFI. On the other hand, mechanical ventilation can cause gas to enter the stomach or lead to bile reflux, further increasing IAP and affecting the patient’s tolerance to enteral nutrition. Therefore, in real life, IAP monitoring should be done regularly on patients on mechanical ventilation before and after they start enteral nutrition so that targeted measures can be taken in time if ENFI happens. Additionally, energy expenditure can be estimated based on carbon dioxide production calculated by the ventilator, and individualized feeding plans can be developed based on the patient’s energy expenditure to reduce the occurrence of ENFI (25).