Development and validation of a nomogram for predicting necrotizing enterocolitis in premature infants with...

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.