Liu Hui, Li Jing, Guo Jingyu, Shi Yuan, Wang Li
Department of Pediatrics, University-Town Hospital of Chongqing Medical University, Chongqing, 401331, China.
Department of Pediatrics, Daping Hospital, Army Medical University, Chongqing, 400042, China.
EClinicalMedicine. 2022 Jun 25;50:101523. doi: 10.1016/j.eclinm.2022.101523. eCollection 2022 Aug.
Neonatal acute respiratory distress syndrome (ARDS) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of neonatal ARDS is critical. This study aimed to build a perinatal prediction nomogram for early prediction of neonatal ARDS.
A prediction model was built including 243 late-preterm and full-term infants from Daping Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and Dec 31, 2019. 80 patients from the Children's Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and June 30, 2018 were considered for external validation. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of neonatal ARDS. Both discrimination and calibration were assessed by bootstrapping with 1000 resamples.
Multivariate logistic regression demonstrated that mother's education level (odds ratio [OR] 0·478, 95% confidence interval [CI] 0·324-0·704), premature rupture of membrane (OR 0·296, 95% CI 0·133-0·655), infectious disease within 7 days before delivery (OR 0·275, 95% CI 0·083-0·909), hospital level (OR 2·479, 95% CI 1·260-4·877), and Apgar 5-min score (OR 0·717, 95% CI 0·563-0·913) were independent predictors for neonatal ARDS in late-preterm and full-term infants, who experienced dyspnoea within 24 h after birth and required mechanical ventilation. The area under the curve and concordance index of the nomogram constructed from the above five factors were 0·760 and 0·757, respectively. The Hosmer-Lemeshow test showed that the model was a good fit ( = 0.320). The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the reliability of the prediction nomogram.
A nomogram based on perinatal factors was developed to predict the occurrence of neonatal ARDS in late-preterm and full-term infants who experienced dyspnoea within 24 h after birth and required mechanical ventilation. It provided clinicians with an accurate and effective tool for the early prediction and timely management of neonatal ARDS.
No funding was associated with this study.
新生儿急性呼吸窘迫综合征（ARDS）是一种临床危重症，致残率和死亡率高。早期识别和治疗新生儿ARDS至关重要。本研究旨在构建围产期预测列线图，用于早期预测新生儿ARDS。
构建一个预测模型，纳入2018年1月1日至2019年12月31日在中国重庆大坪医院住院的243例晚期早产儿和足月儿。将2018年1月1日至2018年6月30日在中国重庆儿童医院住院的80例患者作为外部验证对象。进行多因素逻辑回归分析以识别独立预测因素，并建立列线图来预测新生儿ARDS的发生。通过1000次重复抽样的自举法评估区分度和校准度。
多因素逻辑回归分析显示，母亲教育程度（比值比[OR]0.478，95%置信区间[CI]0.324 - 0.704）、胎膜早破（OR 0.296，95% CI 0.133 - 0.655）、分娩前7天内的传染病（OR 0.275，95% CI 0.083 - 0.909）、医院级别（OR 2.479，95% CI 1.260 - 4.877）和阿氏5分钟评分（OR 0.717，95% CI 0.563 - 0.913）是晚期早产儿和足月儿发生新生儿ARDS的独立预测因素，这些婴儿在出生后２４小时内出现呼吸困难并需要机械通气。由上述五个因素构建的列线图的曲线下面积和一致性指数分别为0.760和0.757。Hosmer-Lemeshow检验显示模型拟合良好（P = 0.320）。列线图的校准曲线接近理想对角线。此外，决策曲线分析表明该模型的净效益显著更好。外部验证证明了预测列线图的可靠性。
开发了一种基于围产期因素的列线图，用于预测出生后24小时内出现呼吸困难并需要机械通气的晚期早产儿和足月儿发生新生儿ARDS的情况。它为临床医生提供了一种准确有效的工具，用于早期预测和及时管理新生儿ARDS。
本研究无相关资金资助。
