Predicting surgical NEC in neonates: risk factors and model development.

Background: Neonatal necrotizing enterocolitis (NEC) is a severe gastrointestinal emergency associated with high morbidity and mortality in neonates. Early identification of cases requiring surgical intervention is critical to improving outcomes.

Objective: To identify independent risk factors associated with surgical treatment in neonates with NEC and to develop a predictive model that supports timely surgical decision-making and enhances prognosis.

Methods: This retrospective study included 188 neonates diagnosed with NEC (Bell stage II or higher) at Liuzhou Hospital, Guangzhou Women and Children's Medical Center between December 2018 and December 2024. Patients were categorized into a surgical treatment group (n = 70) and a conservative treatment group (n = 118). Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression were used to identify independent risk factors. A nomogram was developed based on these factors. Model performance was assessed using receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA).

Results: Among the 188 neonates, 70 (37.2%) underwent surgery. The mortality rate was significantly higher in the surgical group compared to the conservative group (17.1% vs. 1.1%). Multivariable analysis identified C-reactive protein (CRP), serum lactate, portal venous gas, reduced intestinal motility, white blood cell count, and absolute lymphocyte count as independent predictors of surgical intervention. The nomogram model demonstrated excellent discrimination (AUC = 0.946, 95% CI: 0.911-0.981), with a sensitivity of 0.914 and specificity of 0.890. Calibration and DCA confirmed strong clinical utility.

Conclusion: This study established a nomogram-based predictive model for surgical treatment in NEC, utilizing six indicators: CRP, serum lactate, portal venous gas, reduced intestinal motility, white blood cell count, and absolute lymphocyte count. The model shows high accuracy and clinical utility. It can assist clinicians in predicting the need for surgery at the early stages of NEC, optimizing treatment decisions, reducing mortality, and improving the prognosis of affected neonates.