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  • Title: Development and validation of a nomogram for predicting the placement of nasointestinal tubes in critically ill patients based on abdominal radiography: A single-center, retrospective study.
    Author: Zheng Z, Tang S, Shao Z, Cai H, Wang J, Lu L, Yang X, Liu J.
    Journal: Heliyon; 2024 Sep 15; 10(17):e37498. PubMed ID: 39296028.
    Abstract:
    BACKGROUND: Enteral nutrition administered via the nasointestinal tube (NET) is a prevalent nutritional modality among critically ill patients, and abdominal radiographs hold significant value in accurately ascertaining the precise positioning of the NET subsequent to its placement. Therefore, we propose an innovative approach to construct a clinical prediction model based on NET's configuration within the gastrointestinal tract in abdominal radiography. This model aims to enhance the accuracy of determining the position of NETs after their placement. METHODS: Patients admitted to the intensive care unit of Zhejiang Provincial People's Hospital between October 2017 and October 2021 were included to constitute the training cohort for retrospective analysis, and nomogram was constructed. Consecutively enrolled patients admitted to the same hospital from October 2021 to October 2023 were included as the validation cohort. The training cohort underwent a univariate analysis initially, followed by a multivariate logistic regression approach to analyze and identify the most appropriate model. Subsequently, nomogram was generated along with receiver operator characteristic curves, calibration curves, and decision curves for both the training and validation cohorts to evaluate the predictive performance of the model. RESULTS: The training and validation cohorts comprised 574 and 249 patients, respectively, with successful tube placement observed in 60.1 % and 76.3 % of patients, correspondingly. The predictors incorporated in the prediction maps encompass the "C-shape," the height of "inverse C-shape," showing the duodenojejunal flexure, and the location of the head end of the NET. The model demonstrated excellent predictive efficacy, achieving an AUC of 0.883 (95 % CI 0.855-0.911) and good calibration. Furthermore, when applied to the validation cohort, the nomogram exhibited strong discrimination with an AUC of 0.815 (95 % CI 0.750-0.880) and good calibration. CONCLUSION: The combination of abdominal radiography and NET's configuration within the gastrointestinal tract enables accurate determination of NET placement in critically ill patients.
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