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Objectives: Neonatal 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. Methods: We conducted a retrospective analysis of medical data from infants (gestational age <32 weeks, birth weight <1,500â 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. Results: A 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 5â 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). Conclusions: Our 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 5â 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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OBJECTIVES: To investigate the difference in intestinal microbiota between preterm infants with neurodevelopmental impairment (NDI) and those without NDI. METHODS: In this prospective cohort study, the preterm infants who were admitted to the neonatal intensive care unit of Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region from September 1, 2019 to September 30, 2021 were enrolled as subjects. According to the assessment results of Gesell Developmental Scale at the corrected gestational age of 1.5-2 years, they were divided into two groups: normal (n=115) and NDI (n=100). Fecal samples were collected one day before discharge, one day before introducing solid food, and at the corrected gestational age of 1 year. High-throughput sequencing was used to compare the composition of intestinal microbiota between groups. RESULTS: Compared with the normal group, the NDI group had a significantly higher Shannon diversity index at the corrected gestational age of 1 year (P<0.05). The principal coordinate analysis showed a significant difference in the composition of intestinal microbiota between the two groups one day before introducing solid food and at the corrected gestational age of 1 year (P<0.05). Compared with the normal group, the NDI group had a significantly higher abundance of Bifidobacterium in the intestine at all three time points, a significantly higher abundance of Enterococcus one day before introducing solid food and at the corrected gestational age of 1 year, and a significantly lower abundance of Akkermansia one day before introducing solid food (P<0.05). CONCLUSIONS: There are significant differences in the composition of intestinal microbiota between preterm infants with NDI and those without NDI. This study enriches the data on the characteristics of intestinal microbiota in preterm infants with NDI and provides reference for the microbiota therapy and intervention for NDI in preterm infants.
Subject(s)
Gastrointestinal Microbiome , Infant, Premature, Diseases , Infant , Child , Infant, Newborn , Humans , Child, Preschool , Infant, Premature , Prospective Studies , China , Gestational AgeABSTRACT
Introduction: Early identification and intervention of neurodevelopmental impairment in preterm infants may significantly improve their outcomes. This study aimed to build a prediction model for short-term neurodevelopmental impairment in preterm infants using machine learning method. Methods: Preterm infants with gestational age < 32 weeks who were hospitalized in The Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, and were followed-up to 18 months corrected age were included to build the prediction model. The training set and test set are divided according to 8:2 randomly by Microsoft Excel. We firstly established a logistic regression model to screen out the indicators that have a significant effect on predicting neurodevelopmental impairment. The normalized weights of each indicator were obtained by building a Support Vector Machine, in order to measure the importance of each predictor, then the dimension of the indicators was further reduced by principal component analysis methods. Both discrimination and calibration were assessed with a bootstrap of 505 resamples. Results: In total, 387 eligible cases were collected, 78 were randomly selected for external validation. Multivariate logistic regression demonstrated that gestational age(p = 0.0004), extrauterine growth restriction (p = 0.0367), vaginal delivery (p = 0.0009), and hyperbilirubinemia (0.0015) were more important to predict the occurrence of neurodevelopmental impairment in preterm infants. The Support Vector Machine had an area under the curve of 0.9800 on the training set. The results of the model were exported based on 10-fold cross-validation. In addition, the area under the curve on the test set is 0.70. The external validation proves the reliability of the prediction model. Conclusion: A support vector machine based on perinatal factors was developed to predict the occurrence of neurodevelopmental impairment in preterm infants with gestational age < 32 weeks. The prediction model provides clinicians with an accurate and effective tool for the prevention and early intervention of neurodevelopmental impairment in this population.
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BACKGROUND: In developing countries, infant survival rate and long-term outcomes of extremely preterm infants(EPIs) have significantly improved due to advances in perinatal care. The striking gap in the treatment outcome of EPIs between China and the other developed countries was a major concern. To assess treatment outcomes and associated factors among EPIs in Nanning, China. METHODS: This was a perspective study consisting of eligible cases with gestational age between 22 and 28 weeks and infants were followed to 18-24 months of age. Data including clinical characteristics, perinatal factors and after-birth conditions were collected from the neonatal intensive care unit (NICU) in a major women's and children's health hospital in Guangxi Province from January 1st 2010 to February 1st 2015. RESULTS: During that period 79 EPIs were born in the hospital. Twenty-eight infants died in hospital after their parents decided to withdraw clinical treatment. Of the 51 surviving infants, 5 infants were lost to follow-up. Eleven of the 46 infants were evaluated at 18-24 months of age and were diagnosed with neurodevelopmental disability and 35 infants showed normal motor language development. The incidence of intrauterine infection and intraventricular hemorrhage (IVH) grade III were both higher in the group of infants who were diagnosed neurodevelopmental disability than in the group of infants with normal motor language development (p < 0.05). Logistic regression analysis showed that intrauterine infection (OR = 33.290, 95%CI = 2.180-508.351) and IVH grade III (OR = 26.814, 95%CI = 3.631-197.989) were the major risk factors for neurodevelopmental disability in EPIs. CONCLUSIONS: Intrauterine infection and IVH grade III were associated with the neurodevelopmental disability in EPIs.
