关键词: 早产儿, 呼吸窘迫综合征, 列线图, 高海拔地区, 多因素 logistic 回归, 预测模型

Abstract:

Objective To develop a nomogram-based predictive model for assessing the risk of respiratory distress syndrome （RDS） in premature infants in the high-altitude region of Dali. The predictive performance and clinical applicability of the model will be systematically evaluated to provide evidence-based guidance for the early diagnosis and clinical management of respiratory distress in premature infants. Methods A total of 680 preterm infants admitted to the Dali Maternal and Child Health Hospital between January 2020 and December 2024 were enrolled in the study and randomly divided into a training set （n = 476） and a validation set （n = 204） at a ratio of 7∶3. Independent predictors were identified through univariate logistic regression and multivariate stepwise regression analyses， and a nomogram model was subsequently developed using R software. The performance of the model， including its discrimination， calibration， stability， and clinical applicability， was evaluated using the receiver operating characteristic curve （ROC）， Hosmer-Lemeshow goodness-of-fit test， bootstrap resampling method， and decision curve analysis （DCA）. Results The final model incorporated seven independent variables： gestational age， birth weight， Apgar score， blood oxygen saturation， gestational hyperglycemia， prenatal glucocorticoid therapy， and maternal history of infection. The areas under the curve （AUCs） for the training and validation sets were 0.88 （95%CI： 0.84 ~ 0.92） and 0.83 （95%CI： 0.76 ~ 0.89）， respectively， with all Hosmer-Lemeshow test p-values exceeding 0.05. The bootstrap-corrected AUC was 0.85 （95%CI： 0.81 ~ 0.89）. DCA indicated that the model achieved the highest net benefit at a risk threshold range of 10% to 35%. Conclusions This model integrates multiple risk factors associated with the occurrence of RDS in plateau environments， demonstrating robust predictive performance for RDS in preterm infants residing in high-altitude areas such as Dali. It can serve as a valuable tool for risk stratification and clinical decision-making， and may also provide a reference for future multicenter prospective studies.

Key words: preterm infants, respiratory distress syndrome, nomogram, high-altitude areas, multivariate logistic regression, prediction model