Use of sildenafil and l-arginine in an experimental rat model for the prevention of neonatal necrotizing enterocolitis

Moreira, Gabriela Araujo; Dias, André; Cadena, Sílvia Maria Suter Correia; Corrêa-Ferreira, Marília Locatelli; Ioshii, Sérgio Ossamu; Fachin, Camila Girardi

doi:10.1038/s41598-022-10323-8

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…Formula feeding was also an independent risk factor for NEC in our study. This is consistent with previous research ( 35 ). Chen et al found that formula feeding was strongly associated with intestinal ischemia and hypoxia in human NEC ( 30 ).…”

Section: Discussionsupporting

confidence: 94%

“…These conditions can affect the growth and health of newborns. Previous studies have reported that hypothermia in low birth weight premature infants is significantly associated with increased in-hospital mortality and NEC (26)(27)(28)(29). Similarly, our study also found a negative correlation between the body temperature of infants entering the NICU within 24 h after birth and the incidence of NEC, that is, the lower the body temperature of the infant, the greater the probability of developing NEC.…”

Section: Discussionmentioning

confidence: 99%

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A prediction model for neonatal necrotizing enterocolitis in preterm and very low birth weight infants

Feng,

Zhang,

Wei

et al. 2023

Front. Pediatr.

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ObjectivesNeonatal necrotizing enterocolitis (NEC) is a severe gastrointestinal disease that primarily affects preterm and very low birth weight infants, with high morbidity and mortality. We aim to build a reliable prediction model to predict the risk of NEC in preterm and very low birth weight infants.MethodsWe conducted a retrospective analysis of medical data from infants (gestational age <32 weeks, birth weight <1,500 g) admitted to Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region. We collected clinical data, randomly dividing it into an 8:2 ratio for training and testing. Multivariate logistic regression was employed to identify significant predictors for NEC. Principal component analysis was used for dimensionality reduction of numerical variables. The prediction model was constructed through logistic regression, incorporating all relevant variables. Subsequently, we calculated performance evaluation metrics, including Receiver Operating Characteristic (ROC) curves and confusion matrices. Additionally, we conducted model performance comparisons with common machine learning models to establish its superiority.ResultsA total of 292 infants were included, with 20% (n = 58) randomly selected for external validation. Multivariate logistic regression revealed the significance of four predictors for NEC in preterm and very low birth weight infants: temperature (P = 0.003), Apgar score at 5 min (P = 0.004), formula feeding (P = 0.007), and gestational diabetes mellitus (GDM, P = 0.033). The model achieved an accuracy of 82.46% in the test set with an F1 score of 0.90, outperforming other machine learning models (support vector machine, random forest).ConclusionsOur logistic regression model effectively predicts NEC risk in preterm and very low birth weight infants, as confirmed by external validation. Key predictors include temperature, Apgar score at 5 min, formula feeding, and GDM. This study provides a vital tool for NEC risk assessment in this population, potentially improving early interventions and child survival. However, clinical validation and further research are necessary for practical application.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionmentioning

confidence: 99%