ArticleEuropean journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology2026
Sepsis in neonates with necrotizing enterocolitis: outcomes and predictive model.
Article in European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Sepsis after neonatal necrotizing enterocolitis (NEC) onset was associated with substantially adverse outcomes. However, effective tools for predicting sepsis risk in this special population remain lacking. This study aimed to investigate the influence of sepsis after NEC onset on the outcomes and develop a prediction model for early identification of sepsis. Retrospective review of 530 patients diagnosed with NEC was reviewed with clinical data. In this study, the overall proportion of sepsis was 56.2% (298/530). The rates of surgical intervention (63.09% vs. 18.10%) and mortality (17.11% vs. 3.88%) were higher in patients with sepsis than in those without (P < 0.05). The entire cohort was randomly assigned to a training set (n = 371) or testing set (n = 159) at a 7:3 ratio. LASSO-Logistic regression analysis showed that gestational age, age at NEC onset, C-reactive protein level, ventilator support, and hypoproteinemia were independently associated with sepsis (all P < 0.05), and a Nomogram model was developed based on these determinants. The area under the receiver operator characteristic curve of the training set was 0.937 (95% confidence interval [CI]: 0.813-0.960) and that of the testing set was 0.917 (95% CI: 0.876-0.957). The H-L goodness-of-fit test and calibration curve showed the forecast was in good agreement with the actual situation (both P > 0.05), whereas the decision curve analysis indicated the model's practical utility for clinical decision-making with a probability threshold of 0.03. This Nomogram model for predicting sepsis demonstrated desirable discriminative performance, aiding clinicians in enhancing risk assessment and facilitating early preventive interventions.
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