Correlation analysis of feeding intolerance and defecation after primary anastomosis for neonatal intestinal atresia.

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.

The multivariate cox regression model for complete enteral nutrition after primary anastomosis in neonates with intestinal atresia.

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.

Prediction nomogram for evaluating the probability of postoperative fever in children with acute appendicitis.

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.