Evaluation of Time to Resolution of Medical Necrotizing Enterocolitis Using Severity-Guided Management in a Neonatal Intensive Care Unit

Surgical prediction of neonatal necrotizing enterocolitis based on radiomics and clinical information

PURPOSE

To assess the predictive value of radiomics for surgical decision-making in neonatal necrotizing enterocolitis (NEC) when abdominal radiographs (ARs) do not suggest an absolute surgical indication for free pneumoperitoneum.

METHODS

In this retrospective study, we finally included 171 newborns with NEC and obtained their ARs and clinical data. The dataset was randomly divided into a training set (70%) and a test set (30%). We developed machine learning models for predicting surgical treatment using clinical features and radiomic features, respectively, and combined these features to build joint models. We assessed predictive performance of the different models by receiver operating characteristic curve (ROC) analysis and compared area under curve (AUC) using the Delong test. Decision curve analysis (DCA) was used to assess the potential clinical benefit of the models to patients.

RESULTS

There was no significant difference in AUC between the clinical model and the four radiomic models (P > 0.05). The XGBoost joint model had better predictive efficacy and stability (AUC, training set: 0.988, test set: 0.959). Its AUC in the test set was significantly higher than that of the clinical model (P < 0.05). DCA showed that the XGBoost joint model achieved higher net clinical benefit compared to the clinical model in the threshold probability range (0.2-0.6).

CONCLUSION

Radiomic features based on AR are objective and reproducible. The joint model combining radiomic features and clinical signs has good surgical predictive efficacy and may be an important method to help primary neonatal surgeons assess the surgical risk of NEC neonates.

Effective alternatives for dietary interventions for necrotizing enterocolitis: a systematic review of in vivo studies

Necrotizing enterocolitis (NEC) is a significant cause of morbidity and mortality among neonates and low birth weight children in the United States. Current treatment options, such as antibiotics and intestinal resections, often result in complications related to pediatric nutrition and development. This systematic review aimed to identify alternative dietary bioactive compounds that have shown promising outcomes in ameliorating NEC in vivo studies conducted within the past six years. Following PRISMA guidelines and registering in PROSPERO (CRD42023330617), we conducted a comprehensive search of PubMed, Scopus, and Web of Science. Our analysis included 19 studies, predominantly involving in vivo models of rats (Rattus norvegicus) and mice (Mus musculus). The findings revealed that various types of compounds have demonstrated successful amelioration of NEC symptoms. Specifically, six studies employed plant phenolics, seven utilized plant metabolites/cytotoxic chemicals, three explored the efficacy of vitamins, and three investigated the potential of whole food extracts. Importantly, all administered compounds exhibited positive effects in mitigating the disease. These results highlight the potential of natural cytotoxic chemicals derived from medicinal plants in identifying and implementing powerful alternative drugs and therapies for NEC. Such approaches have the capacity to impact multiple pathways involved in the development and progression of NEC symptoms.

An improved joint non-negative matrix factorization for identifying surgical treatment timing of neonatal necrotizing enterocolitis

Neonatal necrotizing enterocolitis is a severe neonatal intestinal disease. Timely identification of surgical indications is essential for newborns in order to seek the best time for treatment and improve prognosis. This paper attempts to establish an algorithm model based on multimodal clinical data to determine the features of surgical indications and construct an auxiliary diagnosis model. The proposed algorithm adds hypergraph constraints on the two modal data based on Joint Nonnegative Matrix Factorization (JNMF), aiming to mine the higher-order correlations of the two data features. In addition, the adjacency matrix of the two kinds of data is used as a network regularization constraint to prevent overfitting. Orthogonal and L1-norm regulations were introduced to avoid feature redundancy and perform feature selection, respectively, and confirmed 14 clinical features. Finally, we used three classifiers, random forest, support vector machine, and logistic regression, to perform binary classification of patients requiring surgery. The results show that when the features selected by the proposed algorithm model are classified by random forest, the area under the ROC curve is 0.8, which has high prediction accuracy.