医学
支气管肺发育不良
列线图
呼吸窘迫
儿科
坏死性小肠结肠炎
前瞻性队列研究
队列
重症监护
人口
逻辑回归
新生儿重症监护室
队列研究
出生体重
胎龄
重症监护医学
内科学
怀孕
外科
遗传学
生物
环境卫生
作者
Chenhong Wang,Zheng Chen,Guannan Bai,Lizhong Du,Yuan Shi,Liping Shi
摘要
Moderate-to-severe bronchopulmonary dysplasia (msBPD) is a prevalent and severe condition in very preterm infants, contributing to long-term respiratory issues. Developing a predictive model specific to the Chinese population could support early intervention. This research focused on determining risk factors and developing a predictive model for msBPD in very preterm infants with respiratory distress, following the 2018 National Institute of Child Health and Human Development criteria. A prospective multicenter study was conducted across 26 tertiary neonatal intensive care units (NICUs) in China from March 2020 to March 2022. Infants born at < 32 weeks' gestation, admitted within 72 h of birth with a respiratory score ≥ 5, were enrolled. Infants were assigned at random to either a training cohort (800 infants from 18 NICUs) or an external validation cohort (397 infants from 8 NICUs). Predictive factors were identified using multivariate logistic regression and Lasso regression, leading to the development of a prediction nomogram. Key independent predictors included birth weight, surfactant use, early pulmonary hypertension, duration of invasive mechanical ventilation, and necrotizing enterocolitis (Stages II-III). The model demonstrated high accuracy in the training cohort (AUC = 0.844) with good calibration (Hosmer-Lemeshow test, p = 0.247). Independent validation showed consistent discrimination (AUC = 0.849) and calibration (Hosmer-Lemeshow test, p = 0.333), while decision curve analysis confirmed clinical benefit. The developed nomogram-based model offers reliable early msBPD risk assessment in very preterm infants, supporting timely clinical interventions within Chinese NICUs.
科研通智能强力驱动
Strongly Powered by AbleSci AI