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Background <p>To develop and validate a nomogram model for predicting the risk of fulminant necrotizing enterocolitis (fNEC) in infants with NEC and to summarize the clinical features of fNEC.</p> Methods <p>Neonates admitted to Shengjing Hospital from 1st January 2013 to 31st December 2022 with the diagnosis of NEC were randomly divided into a training set or a validation set. Independent risk factors identified by univariate and multivariate logistic regression analyses were used to conduct a nomogram model for fNEC. The model was evaluated by the area under the curve (AUC), calibration curves and decision curve analysis (DCA) in both sets.</p> Results <p>A total of 315 neonates were included, comprising 70 cases of fNEC and 245 cases of non-fulminant NEC. Neonates with fNEC exhibited more severe symptoms, including acidosis, thrombocytopenia, absent bowel sounds, increased need for surgical intervention and significantly higher mortality. The nomogram was developed using four predictors: bloody stool, absence of bowel sounds, elevated lactate levels and pH values. The AUC of the training and validation sets were 0.939 and 0.975, respectively. Calibration curves and the DCA showed satisfactory performance.</p> Conclusions <p>Neonates with fNEC exhibited more severe symptoms. Our nomogram model shows promise in assisting clinicians in predicting fNEC.</p>

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Fulminant necrotizing enterocolitis: clinical features and a predictive model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月10日

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  • Xiaoyu Chen,
  • Yuqiao Li,
  • Yuhan Liu,
  • Tianjing Liu,
  • Yongyan Shi

摘要

Background

To develop and validate a nomogram model for predicting the risk of fulminant necrotizing enterocolitis (fNEC) in infants with NEC and to summarize the clinical features of fNEC.

Methods

Neonates admitted to Shengjing Hospital from 1st January 2013 to 31st December 2022 with the diagnosis of NEC were randomly divided into a training set or a validation set. Independent risk factors identified by univariate and multivariate logistic regression analyses were used to conduct a nomogram model for fNEC. The model was evaluated by the area under the curve (AUC), calibration curves and decision curve analysis (DCA) in both sets.

Results

A total of 315 neonates were included, comprising 70 cases of fNEC and 245 cases of non-fulminant NEC. Neonates with fNEC exhibited more severe symptoms, including acidosis, thrombocytopenia, absent bowel sounds, increased need for surgical intervention and significantly higher mortality. The nomogram was developed using four predictors: bloody stool, absence of bowel sounds, elevated lactate levels and pH values. The AUC of the training and validation sets were 0.939 and 0.975, respectively. Calibration curves and the DCA showed satisfactory performance.

Conclusions

Neonates with fNEC exhibited more severe symptoms. Our nomogram model shows promise in assisting clinicians in predicting fNEC.