RESUMO
OBJECTIVES: To identify risk factors associated with early-onset sepsis (EOS) in very preterm infants and develop a nomogram model for predicting the risk of EOS. METHODS: A retrospective analysis was conducted on 344 very preterm infants delivered at the First Affiliated Hospital of Zhengzhou University and admitted to the Department of Neonatology between January 2020 and December 2022. These infants were randomly divided into a training set (241 infants) and a validating set (103 infants) in a 7:3 ratio. The training set was further divided into two groups based on the presence or absence of EOS: EOS (n=64) and non-EOS (n=177). Multivariate logistic regression analysis was performed to identify risk factors for EOS in the very preterm infants. The nomogram model was developed using R language and validated using the validating set. The discriminative ability, calibration, and clinical utility of the model were assessed using receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis, respectively. RESULTS: The multivariate logistic regression analysis revealed that gestational age, need for tracheal intubation in the delivery room, meconium-stained amniotic fluid, serum albumin level on the first day of life, and chorioamnionitis were risk factors for EOS in very preterm infants (P<0.05). The area under the ROC curve for the training set was 0.925 (95%CI: 0.888-0.963), and that for the validating set was 0.796 (95%CI: 0.694-0.898), confirming the model's good discrimination. The Hosmer-Lemeshow goodness-of-fit test suggested that the model was well-fitting (P=0.621). The calibration curve analysis and decision curve analysis demonstrated that the model had high predictive efficacy and clinical applicability. CONCLUSIONS: Gestational age, need for tracheal intubation in the delivery room, meconium-stained amniotic fluid, serum albumin level on the first day of life, and chorioamnionitis are significantly associated with the development of EOS in very preterm infants.The nomogram model for predicting the risk of EOS in very preterm infants, constructed based on these factors, has high predictive efficacy and clinical applicability.