A nomogram to predict extubation failure in infants born before 32 gestational weeks: a single-center retrospective study.

IF 1.5 4区医学 Q2 PEDIATRICS

Translational pediatrics Pub Date : 2025-05-30 Epub Date: 2025-05-27 DOI:10.21037/tp-2025-43

Han Zhang, Yunjie Zhang, Fangfei Tao, Yongfeng Wu, Zhou Jiang

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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.

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预测32孕周前出生婴儿拔管失败的nomogram：一项单中心回顾性研究

背景：识别与拔管失败（EF）相关的因素可能有助于优化拔管时机，预防再插管，并提高早产儿的临床结果。本研究旨在分析32孕周前出生的早产儿EF的危险因素，并建立EF的预测图。方法：本回顾性研究基于2020年1月至2024年12月期间32孕周前出生并在出生后24小时内接受机械通气的早产儿数据。这些婴儿根据出生时间分为训练集和验证集。采用单变量分析筛选危险因素，采用最小绝对收缩和选择算子回归进行变量筛选，采用二元logistic分析建立预测模型，并构建方差图。采用校准曲线、受试者工作特征曲线下面积（AUC）和决策曲线分析（DCA）分别评价nomogram辨别性、准确性和临床实用性。结果：共纳入178例患儿，EF发生率为30.9%。拔管时经后年龄、拔管前吸氧分数和拔管前血流动力学显著性动脉导管未闭被确定为预测EF的独立因素。基于这些独立因素构建的模态图可用于预测EF。训练集和验证集的AUC值分别为0.834和0.851。校正曲线显示nomogram模型与实际观测值之间有显著的一致性。DCA分析结果表明该模型具有良好的临床疗效。结论：该预测模型可准确预测32孕周前早产儿EF的发生风险。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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