Objective  To investigate and screen the risk factors and construct a risk prediction model for aspiration in enteral nutrition patients in ICU.

Methods  ICU patients who underwent enteral nutrition in the ICU of Qingdao Municipal Hospital were included from January 2022 to June 2023.  Independent risk factors for aspiration in enteral nutrition patients in the ICU were analyzed and predictive models were constructed using univariate analysis, Lasso regression, and multifactorial Logistic regression. The Random forest model ranked the importance of the independent factors, and the predictive models were visualized using Nomogram.

Results  A total of 500 patients were included and aspiration occurred in 285 patients, with the incidence of aspiration of 57% in enteral nutrition patients in the ICU. The independent risk factors were ranked in order of importance from highest to lowest as number of days of placement [OR=1.038, 95%CI(1.024, 1.052)], body position [OR=3.879, 95%CI(2.104, 7.152)], duration of daily enteral nutrition [OR=1.035, 95%CI(1.004, 1.067)], APACHE II score [OR=1.063, 95%CI(1.032, 1.095)], use of sedative and analgesic medication [OR=4.054, 95%CI(1.804, 9.108)], partial pressure of oxygen [OR=0.985, 95%CI(0.974, 0.997)]. The model in the training set had a prediction accuracy of 74.00%, a specificity of 69.48%, a sensitivity of 77.55%, and an area under curve (AUC) of ROC of 0.82[95%CI(0.78, 0.86)]. The model in the validation set had a prediction accuracy of 70.00%, a specificity of 68.85%, a sensitivity of 70.79%, and an AUC of 0.79[95% CI(0.72, 0.86)]. The calibration curve and decision curve showed that the model had good calibration and benefit.

Conclusion  The risk prediction model constructed in this study demonstrates strong predictive efficacy, offering a scientific and objective reference basis for clinical staff to assess the risk of aspiration, and facilitate the implementation of targeted preventive  measures.

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