This study aimed to analyze the differences in microbial composition between the proximal and distal intestinal segments of newborns with intestinal atresia, speculating about the mechanism underlying the initial establishment of neonatal intestinal flora. Additionally, differential metabolic pathways were explored to predict their potential effects on gravidas and fetuses. The microbial characteristics of intestinal flora were assessed using 16SrRNA sequencing. The alpha and beta-diversity indices were calculated to compare the microbial composition among three groups. Principal Coordinates Analysis (PCoA) was employed to identify and quantify differences in microbial communities. Furthermore, PICRUSt software was utilized to predict the possible functional impacts of differential metabolic pathways by comparing them with public databases. Samples were collected from 23 neonates with intestinal atresia (proximal and distal segments) and 25 healthy neonates (first meconium) based on predefined selection criteria. No significant differences in baseline characteristics were observed between the control and intestinal atresia groups (P > 0.05). Alpha-Diversity analysis revealed that the distal intestinal group exhibited greater microbial species richness. Beta-Diversity analysis indicated significant differences in bacterial composition between the control group and the distal intestinal group (P < 0.05), with the distal group showing a more pronounced divergence compared to the proximal group. Functional prediction analysis suggested that the differential metabolic pathways might protect the intestinal mucosal barrier. However, they could also negatively impact blood glucose regulation and lipid transport in gravidas and fetuses, potentially contributing to adverse emotional states in pregnant women. The distinct microbial profiles observed among the three groups suggest that the establishment of neonatal intestinal flora may result from a combination of placental transmission and digestive tract colonization. Functional pathway analysis suggested these microbial metabolic differences may exert pleiotropic effects, demonstrating both protective roles in intestinal barrier function and potentially detrimental impacts on emotional modulation and glucose/lipid dysregulation.

The aim of this study was to develop and validate an intestinal stenosis prediction model for postoperative newborns with neonatal necrotizing enterocolitis (NEC). Clinical information was collected on neonates who had undergone anastomosis or enterostomy because of NEC. The least absolute shrinkage and selection operator regression was applied to identify risk factors included in the model for postoperative intestinal stenosis. Multivariate logistic regression analysis was used to develop a predicting model regression based on the selected variables. Then internal validation was assessed using the bootstrapping validation. The accuracy and applicability of the model are assessed by C-index, calibration and decision curve. Predictors incorporated into the model were a weight on admission, hematochezia, duration of abnormal C-reactive protein, lactate, intestinal peristalsis vanish, operation methods and duration of surgery. The regression equation was logit (P) = −0.001X1 + 1.566X2 + 0.185X3 + 0.304X4 + 1.34X5 − 2.932X6 + 0.015X7 − 3.193, where X1 was weight on admission (g), X2 was hematochezia (yes = 1, no = 0), X3 was duration of abnormal C-reactive protein (days), X4 was lactate (mmol/L), X5 was intestinal peristalsis vanish (yes = 1, no = 0), X6 was primary anastomosis (yes = 1, no = 0), X7 was duration of surgery (min). The model displayed good discrimination with a C-index of 0.879 (0.827,0.932) by random sampling for 1000 times. The calibration curve excluded the overfitting performance, and the decision curve confirmed the clinical application capacity of the model. This nomogram of intestinal stenosis incorporating the use of weight on admission, hematochezia, duration of abnormal C-reactive protein, lactate, intestinal peristalsis vanish, operation methods and duration of surgery could be conveniently used to facilitate the intestinal stenosis risk prediction in postoperative-NEC-patients.

PURPOSE:To investigate the correlation between postoperative feeding intolerance and defecation, with a view to carrying out prognostic assessment and timely intervention for the recovery of postoperative gastrointestinal function. METHODS:The 114 neonates with congenital intestinal atresia who underwent primary anastomosis admitted to Shenzhen Children's Hospital from January 2014 to December 2022 were studied, and the patients' basic information, intraoperative conditions, postoperative feeding and defecation, and hospitalization time were retrospectively analyzed. RESULTS:The risk factors for feeding intolerance after primary anastomosis for intestinal atresia are the gestational days, the time of the first postoperative defecations, the number of defecations on the previous day and the average number of defecations before feeding. CONCLUSION:The incidence of postoperative feeding intolerance is higher in preterm infants, and pediatricians can decide the timing of breastfeeding on the basis of the patients' defecation. The focus on accurate defecation may be more meaningful in determining and predicting postoperative feeding intolerance in the infants.

Objective Enteral feeding after intestinal atresia has always been a concern for clinicians. But the present studies mainly focused on single factors. This research aimed to comprehensively analyze the multiple factors on complete enteral nutrition after primary anastomosis, and establish the convenient prediction model. Methods We retrospectively collected reliable information in neonates with intestinal atresia form January 2010 to June 2022. The cox regression analysis was performed to select independent risk factors and develop nomogram. Subsequently, ROC curve, calibration curve and decision curve were drawn to thoroughly evaluate the accuracy and applicability of the model. Results The predictors finally included in the model were gestational age, meconium peritonitis, distance from the anastomosis to the ileocecal region, diameter ratio of proximal to distal bowels, and time of initial feeding. The nomogram of predicting the probability of week 2, week 3 and week 4 was drawn and their area under the curve were 0.765, 0.785 and 0.747, respectively. Similarly, calibration and decision curve indicated that the prediction model had a great prediction performance. Conclusion The clinical value of predictive models can be recognized. The hope is that the predictive model can help pediatricians reduce hospital costs and parental anxiety.

Objective:The purpose of this study was to establish a predictive model of postoperative fever in children with acute appendicitis through retrospective analysis, and the prediction ability of the model is demonstrated by model evaluation and external validation.Methods:Medical records information on children undergoing surgery for acute appendicitis within 2 years were retrospectively collected, prospective collection was performed for external validation in the next 3 months. The patients were divided into two groups according to whether the postoperative body temperature exceeded 38.5°C. Multivariate logistic regression analysis was used to determine independent risk factors and develop regression equations and nomogram. ROC curve, calibration curve and decision curve were made for model evaluation. Finally, the clinical implication of the prediction model was clarified by associating postoperative fever with prognosis.Results:High risk factors of postoperative fever included in the prediction model were onset time (X1), preoperative temperature (X2), leukocyte count (X3), C-reactive protein (X4) and operation time (X5). The regression equation is logit (P) = 0.005X1+0.166X2+0.056X3+0.004X4+0.005X5-9.042. ROC curve showed that the area under the curve (AUC) of the training set was 0.660 (0.621, 0.699), and the AUC of the verification set was 0.712 (0.639, 0.784). The calibration curve suggested that the prediction probability was close to the actual probability. Decision curve analysis (DCA) showed that patients could benefit from clinician's judgment. Furthermore, prognostic analysis showed children presenting with postoperative fever had the more duration of postoperative fever, hospitalization stays and cost, except for rehospitalization.Conclusion:All the results revealed that the model had good predictive ability. Pediatricians can calculate the probability of postoperative fever and make timely interventions to reduce pain for children and parents.