4.6 Article

Nomogram to predict feeding intolerance in critically ill children

Journal

EUROPEAN JOURNAL OF PEDIATRICS
Volume -, Issue -, Pages -

Publisher

SPRINGER
DOI: 10.1007/s00431-023-05205-8

Keywords

Feed intolerance; Critically ill; Children; Nomogram; Predictors; Enteral nutrition

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This study aimed to understand the characteristics of children with feed intolerance (FI) and identify the factors predicting FI in critically ill children. The study found that higher PIM3 score, mechanical ventilation (MV), sepsis, hypokalemia, and lower PaO2 were independent risk factors for FI, whereas higher albumin was an independent protective factor for FI.
Feed intolerance (FI) is significantly associated with poor prognosis in critically ill patients. This study aimed to understand the characteristics of children with FI and identify the factors predicting FI in critically ill children. This retrospective cohort study was conducted between January 2017 and June 2022 in the Pediatric Intensive Care Unit of a specialized children's hospital. Eighteen factors, including age, body mass index for age z-score (BAZ) <-2, paediatric index of mortality (PIM)3 score, Glasgow coma scale score, mechanical ventilation (MV), enteral nutrition delay, vasoactive drugs, sedatives, sepsis, heart disease, neurological disease, hypokalemia, arterial PH < 7.35, arterial partial pressure of oxygen (PaO2), blood glucose, hemoglobin, total protein, and albumin, were retrieved to predict FI. The outcome was FI during PICU stay. During the study period, a total of 854 children were included, of which 215 children developed FI. Six predictors of FI were selected: PIM3 score, MV, sepsis, hypokalemia, albumin, and PaO2. Multivariate logistic regression analysis showed that higher PIM3 score, MV, sepsis, hypokalemia, and lower PaO2 were independent risk factors for FI, whereas higher albumin was an independent protective factor for FI. The C-index of the predictive nomogram of 0.943 was confirmed at internal validation to be 0.940, indicating a good predictive value of the model. Decision curve analysis shows good clinical applicability of the nomogram in predicting FI.Conclusion: The nomogram was verified to have a good prediction performance based on discrimination, calibration, and clinical decision analysis.

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