Factors associated with survival of extremely preterm infants and development of a nomogram: a 9-year single-center...

To investigate the clinical characteristics and factors associated with outcomes in extremely preterm infants with a gestational age < 28 weeks, and to develop a nomogram-based prediction model to support clinical decision-making in neonatal intensive care. We retrospectively collected clinical data from 722 extremely preterm infants admitted to a neonatal intensive care unit between January 2016 and December 2024. The cohort was randomly divided into a training set and a testing set at a ratio of 7:3. Univariate and multivariate logistic regression analyses were performed to identify independent factors associated with in-hospital mortality. A nomogram was constructed based on the multivariate model. The predictive performance of the model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). This study included a total of 722 extremely preterm infants, of whom 390 survived to discharge and 332 died. Variables with statistical significance in univariate analyses were entered into multivariate logistic regression. Gestational age, a 1-minute Apgar score of 0-3, used of pulmonary surfactant, used of vasoactive drugs, and pulmonary hemorrhage were identified as independent factors associated with in-hospital mortality. The nomogram demonstrated excellent discrimination, with areas under the ROC curve (AUCs) of 0.969 and 0.953 in the training and testing sets, respectively. The sensitivity and specificity were 0.914 and 0.974 in the training set, and 0.838 and 0.966 in the testing set. The Hosmer-Lemeshow test indicated good calibration (χ² = 8.481, P = 0.388 in the training set; χ² = 10.011, P = 0.264 in the testing set). Decision curve analysis showed that the nomogram provided a favorable net benefit across a wide range of threshold probabilities. The nomogram developed in this study demonstrated good discrimination, calibration, and clinical utility for predicting mortality in extremely preterm infants. This model may serve as a practical tool for individualized risk assessment and early clinical intervention.