摘要 目的 探讨早产儿发生有血流动力学改变的动脉导管未闭（hemodynamically significant patent ductus arteriosus, hsPDA）的预测因素，并构建早产儿发生hsPDA的列线图预测模型。 方法 回顾性分析2020年1月—2022年12月在南京市妇幼保健院分娩的胎龄<32周且确诊为动脉导管未闭（patent ductus arteriosus, PDA）的早产儿的临床资料，根据是否存在hsPDA分为hsPDA组（52例）和非hsPDA组（176例），对两组早产儿的出生一般信息、母亲孕产期相关情况、住院期间相关指标进行单因素分析及多因素logistic回归分析以筛选预测变量，使用R软件构建早产儿hsPDA发生的列线图预测模型，并采用Bootstrap法进行内部验证。最后，对预测模型进行校准度、鉴别能力和临床适用度的评价。 结果 多因素回归分析显示，左房与主动脉根部内径比值（ratio of the left atrium to aorta diameter, LA/AO）、分娩方式为阴道顺产、机械通气时长是早产儿hsPDA发生的独立预测因素（P<0.05）。根据单因素及多因素分析结果，筛选出了用于构建早产儿hsPDA发生风险的预测模型的变量，并据此进行了列线图模型构建。这些变量包括：LA/AO、分娩方式为阴道顺产、机械通气时长、5 min Apgar评分，以及合并需要肺表面活性物质治疗的新生儿呼吸窘迫综合征。该模型的受试者操作特征曲线的曲线下面积为0.876（95%CI：0.824~0.927），校准后曲线接近理想参考线，提示该模型具有良好的校准度，且Hosmer?Lemeshow检验表明该模型拟合良好，临床决策曲线高于极端曲线。 结论 基于LA/AO、分娩方式为阴道顺产、机械通气时长、5 min Apgar评分、合并需要肺表面活性物质治疗的新生儿呼吸窘迫综合征5个变量建立的列线图预测模型对预测早产儿hsPDA的发生具有参考意义，对临床早期识别hsPDA的发生有指导价值。
Abstract：Objective To explore the predictive factors for hemodynamically significant patent ductus arteriosus (hsPDA) in preterm infants and to construct a nomogram prediction model for hsPDA occurrence in this population. Methods A retrospective analysis was conducted on the clinical data of preterm infants with gestational age <32 weeks diagnosed with patent ductus arteriosus (PDA) who were delivered at Nanjing Women and Children's Healthcare Hospital from January 2020 to December 2022. The subjects were divided into an hsPDA group (52 cases) and a non-hsPDA group (176 cases) based on the presence of hsPDA. Univariate analysis and multivariate logistic regression analysis were performed to screen predictive variables regarding the general information of the infants at birth, maternal pregnancy and delivery conditions, and relevant indicators during hospitalization. A nomogram prediction model for hsPDA occurrence was constructed using R software in preterm infants. Internal validation was performed using the Bootstrap method. Finally, the predictive model was evaluated for calibration, discrimination ability, and clinical utility. Results Multivariate regression analysis showed that the ratio of the left atrium to aorta diameter (LA/AO), mode of delivery (vaginal), and duration of mechanical ventilation were independent predictive factors for hsPDA in preterm infants (P<0.05). Based on the results of univariate analysis and multivariate logistic regression analysis, variables used to construct the nomogram prediction model for hsPDA risk included: LA/AO ratio, mode of delivery (vaginal), duration of mechanical ventilation, 5-minute Apgar score, and the presence of neonatal respiratory distress syndrome requiring surfactant therapy. The area under the receiver operating characteristic curve for this model was 0.876 (95%CI: 0.824-0.927), and the calibrated curve was close to the ideal reference line, indicating good calibration. The Hosmer-Lemeshow test demonstrated that the model fit well, and the clinical decision curve was above the extreme curves. Conclusions The nomogram prediction model, constructed using five variables (LA/AO ratio, vaginal delivery, duration of mechanical ventilation, 5-minute Apgar score, and the presence of neonatal respiratory distress syndrome requiring surfactant therapy), has reference significance for predicting the occurrence of hsPDA in preterm infants and provides valuable guidance for the early clinical identification of hsPDA.
MU Jun,LI Shu-Shu,SU Ai-Ling et al. Predictive factors for hemodynamically significant patent ductus arteriosus in preterm infants and the construction of a nomogram prediction model[J]. CJCP, 2025, 27(3): 279-285.
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