Abstract
BACKGROUND: Identification of factors associated with extubation failure (EF) may contribute to the optimization of the timing of extubation, prevention of reintubation, and enhancement of clinical outcomes in preterm infants. This study aimed to analyze the risk factors for EF in preterm infants born before 32 gestational weeks and develop a predictive nomogram for EF. METHODS: This retrospective study was based on data of preterm infants born before 32 gestational weeks between January 2020 and December 2024 who received mechanical ventilation within 24 hours after birth. These infants were divided into a training set and a validation set according to the time of birth. Risk factors were screened using univariable analysis, least absolute shrinkage and selection operator regression was used for variable screening, a predictive model was built using binary logistic analysis, and a nomogram was constructed. Calibration curve, the area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA) were applied to assess the discrimination, accuracy, and clinical practicability of the nomogram, respectively. RESULTS: A total of 178 infants were included in the study and EF rate was 30.9%. Postmenstrual age at extubation, fraction of inspired oxygen before extubation and hemodynamically significant patent ductus arteriosus before extubation were identified as independent factors for predicting EF. A nomogram constructed based on these independent factors can be used for predicting EF. The AUC values of the training set and the validation set were 0.834 and 0.851. Calibration curves revealed significant agreement between the nomogram model and actual observations. The results of the DCA analysis indicated that this model offered good clinical benefits. CONCLUSIONS: The prediction model can accurately estimate the risk of EF in preterm infants born before 32 gestational weeks.