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[Risk factors and development of a prediction model of enteral feeding intolerance in critically ill children]
Xia Zhou1, Hong-Mei Gao1, Lin Huang
1Department of Clinical Nursing, Xiangya Hospital, Central South University, Changsha 410008, China.
Insights
Feeding intolerance (FI) in critically ill children receiving enteral nutrition (EN) is linked to longer bed rest, shock, and certain medications. A new nomogram model accurately predicts FI risk, aiding clinical decisions.
Area of Science:
- Pediatric critical care medicine
- Clinical nutrition
- Biostatistics
Context:
- Enteral nutrition (EN) is crucial for critically ill children.
- Feeding intolerance (FI) is a common complication impacting recovery.
- Identifying predictive factors for FI is essential for optimizing patient management.
Purpose:
- To identify independent risk factors for feeding intolerance (FI) in critically ill children receiving enteral nutrition (EN).
- To develop and validate a predictive nomogram model for FI in this patient population.
Summary:
- A retrospective study analyzed data from critically ill children, identifying duration of bed rest, shock, gastrointestinal decompression, non-steroidal anti-inflammatory drug use, and combined parenteral nutrition as independent risk factors for FI.
- A nomogram prediction model was constructed and validated, demonstrating strong discrimination (AUC 0.934 in training set, 0.852 in validation set) and good calibration.
Impact:
- The developed nomogram model provides a valuable tool for predicting FI in critically ill children receiving EN.
- This model can enhance clinical decision-making, potentially improving patient outcomes and resource allocation.
Objectives:
To explore the risk factors of feeding intolerance (FI) in critically ill children receiving enteral nutrition (EN) and to construct a prediction nomogram model for FI.
Methods:
A retrospective study was conducted to collect data from critically ill children admitted to the Pediatric Intensive Care Unit of Xiangya Hospital, Central South University, between January 2015 and October 2020. The children were randomly divided into a training set (346 cases) and a validation set (147 cases). The training set was further divided into a tolerance group (216 cases) and an intolerance group (130 cases). Multivariate logistic regression analysis was used to screen for risk factors for FI in critically ill children receiving EN. A nomogram was constructed using R language, which was then validated on the validation set. The model's discrimination, calibration, and clinical net benefit were evaluated using receiver operating characteristic curves, calibration curves, and decision curves.
Results:
Duration of bed rest, shock, gastrointestinal decompression, use of non-steroidal anti-inflammatory drugs, and combined parenteral nutrition were identified as independent risk factors for FI in critically ill children receiving EN (P<0.05). Based on these factors, a nomogram prediction model for FI in critically ill children receiving EN was developed. The area under the receiver operating characteristic curve for the training set and validation set was 0.934 (95%CI: 0.906-0.963) and 0.852 (95%CI: 0.787-0.917), respectively, indicating good discrimination of the model. The Hosmer-Lemeshow goodness-of-fit test showed that the model had a good fit (χ 2=12.559, P=0.128). Calibration curve and decision curve analyses suggested that the model has high predictive efficacy and clinical application value.
Conclusions:
Duration of bed rest, shock, gastrointestinal decompression, use of non-steroidal anti-inflammatory drugs, and combined parenteral nutrition are independent risk factors for FI in critically ill children receiving EN. The nomogram model developed based on these factors exhibits high predictive efficacy and clinical application value.
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