目的 构建适用于大理高海拔地区早产儿呼吸窘迫综合征（RDS）发生风险的列线图预测模型，并评估其预测效能与临床实用性，指导临床工作中对于早产儿呼吸窘迫的诊治。 方法 纳入2020年1月至2024年12月大理州妇幼保健院收治的680例早产儿，按7：3分为建模集（n = 476）与验证集（n = 204）。通过单因素logistic回归与多因素逐步回归筛选独立预测因子，利用R语言构建列线图模型。采用受试者工作特征曲线（ROC）、Hosmer-Lemeshow检验、Bootstrap重抽样及决策曲线分析（DCA）评估模型区分度、校准度、稳定性与临床价值。 结果 最终模型纳入胎龄、出生体质量、1 min Apgar评分、血氧饱和度、孕期高血糖、产前糖皮质激素治疗及孕期感染史7个独立变量。建模集与验证集AUC分别为0.88（95%CI： 0.84 ~ 0.92）与0.83（95%CI： 0.76 ~ 0.89），Hosmer-Lemeshow检验均P > 0.05，Bootstrap校正后AUC为0.85（95%CI： 0.81 ~ 0.89），DCA显示模型在风险阈值10% ~ 35%时净收益最优。 结论 本模型整合了高原环境下RDS发生的多种危险因素，对大理高海拔地区早产儿RDS具有良好预测效能，可用于风险分层与临床决策，也为今后开展多中心前瞻性研究提供了参照。

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.