Development and validation of a nomogram for predicting necrotizing enterocolitis in premature infants with early-onset sepsis.

This study aimed to develop an effective individualized predictive nomogram for the occurrence of necrotizing enterocolitis (NEC) in premature infants with early-onset sepsis (EOS). A total of 238 premature infants meeting the inclusion criteria of gestational age < 37 weeks and EOS diagnosis, including 71 with NEC and 167 without NEC (NEC incidence: 29.8%), treated at the First Hospital Affiliated to Army Medical University from January, 2016, to September, 2024 were retrospectively enrolled as a modeling cohort. Additionally, 205 preterm with EOS (53 with NEC and 152 with non-NEC, NEC incidence: 25.9%), who were treated at Liaocheng People's Hospital from January, 2014, to September, 2024 were retrospectively enrolled as a validation cohort to assess the predictive efficacy of the model. LASSO-Logistic regression analysis were applied to screen independent predictors, which were subsequently incorporated into a nomogram constructed using R software. Model performance was assessed through receiver operating characteristic analysis, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC). Lasso-logistic regression identified four independent predictors of NEC in premature infants with EOS: chorioamnionitis (OR = 3.07, 95% CI: 1.26-7.48, p = 0.013), neonatal respiratory distress syndrome (OR = 2.20, 95% CI: 1.10-4.41, p = 0.027), lactate level (OR = 1.96, 95% CI: 1.48-2.58, p < 0.001), and white blood cell (WBC) count (OR = 0.89, 95% CI: 0.83-0.95, p < 0.001). These factors were integrated into the nomogram. The nomogram demonstrated excellent discriminative ability with the area under the receiver operating curve of 0.848 (95% CI: 0.793-0.903, sensitivity: 0.820, specificity: 0.761) in the modeling cohort and 0.825 (95% CI: 0.764-0.887, sensitivity: 0.750, specificity: 0.755) in the validation cohort, enabling early risk stratification for targeted clinical monitoring. Calibration curves confirmed good agreement between predicted and observed NEC probabilities (modeling cohort: χ2 = 3.539, df = 8, p = 0.896; validation cohort: χ2 = 12.769, df = 8, p = 0.120). DCA and CIC further verified the nomogram's high net clinical benefit, confirming its utility in guiding clinical decision-making. This study establishes a nomogram based on four readily accessible variables to predict NEC in premature infants with EOS. With robust predictive performance, this tool enables early risk stratification of high-risk infants, facilitating timely and targeted monitoring and intervention.