早产儿支气管肺发育不良的高危因素临床分析及预测模型构建

doi:10.7499/j.issn.1008-8830.2404065

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Abstract Objective To investigate the risk factors for bronchopulmonary dysplasia (BPD) in preterm infants, and to establish a risk prediction model. Methods A total of 120 preterm infants who were admitted to the neonatal intensive care unit of Shanghai Children's Hospital from January to December 2022 were included. According to the diagnostic criteria for BPD released by the National Institute of Child Health and Human Development in 2018, they were divided into a non-BPD group (84 infants) and a BPD group (36 infants). The clinical data of the infants and their mothers were compared between the two groups. The univariate analysis and the stepwise multivariate regression analysis were used to identify the risk factors for BPD and establish a risk prediction model. Results The results showed that a gestational age of <28 weeks, duration of noninvasive respiratory support, comorbidity with infectious pneumonia, and chorioamnionitis in the mother were independent risk factors for BPD in preterm infants (P<0.05). A nomogram model for predicting the development of BPD was established based on the risk factors, with an area under the receiver operating characteristic curve of 0.93, and the calibration curve of this nomogram had a slope of about 1. The goodness-of-fit test indicated the model fitted well (χ2=8.287, P=0.406). Conclusions A gestational age of <28 weeks, duration of noninvasive respiratory support, comorbidity with infectious pneumonia, and chorioamnionitis in the mother are independent risk factors for BPD in preterm infants.

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	LIU Yi-Bo
	YAN Chong-Bing
	ZHANG Yuan-Yang
	WENG Bo-Wen
	CAI Cheng

Key words： Bronchopulmonary dysplasia Risk factor Risk prediction model Preterm infant

Received: 10 April 2024

Corresponding Authors: Cai C , Email:caicheng2004@163.com。 E-mail: caicheng2004@163.com

Cite this article:

LIU Yi-Bo,YAN Chong-Bing,ZHANG Yuan-Yang et al. Risk factors for bronchopulmonary dysplasia in preterm infants and establishment of a prediction model[J]. CJCP, 2024, 26(11): 1148-1154.

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http://www.zgddek.com/EN/10.7499/j.issn.1008-8830.2404065 OR http://www.zgddek.com/EN/Y2024/V26/I11/1148

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