Jin Fang, Chen Yu, Liu Yi-Xun, Wu Su-Ying, Fang Chao-Ce, Zhang Yong-Fang, Zheng Lu, Zhang Li-Fang, Song Xiao-Dong, Xia Hong, Chen Er-Ming, Rao Xiao-Qin, Chen Guang-Quan, Yi Qiong, Hu Yan, Jiang Lang, Li Jing, Pang Qing-Wei, You Chong, Cheng Bi-Xia, Tan Zhang-Hua, Tan Ya-Juan, Zhang Ding, Yu Tie-Sheng, Rao Jian, Liang Yi-Dan, Xia Shi-Wen
Department of Neonatology, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430070, China.
Zhongguo Dang Dai Er Ke Za Zhi. 2023 Jul 15;25(7):697-704. doi: 10.7499/j.issn.1008-8830.2301047.
To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia.
A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively.
Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia.
The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.
探讨湖北恩施土家族苗族自治州新生儿窒息的危险因素，并建立预测新生儿窒息风险的列线图模型。
进行一项回顾性研究，将2019年1月至12月在恩施土家族苗族自治州20家合作医院治疗的613例新生儿窒息病例作为窒息组，同期在这些医院新生儿科随机选取988例出生且未发生窒息的新生儿作为对照组。采用单因素和多因素分析确定新生儿窒息的危险因素。使用R软件（4.2.2）建立列线图模型。分别采用受试者工作特征曲线、校准曲线和决策曲线分析评估该模型预测新生儿窒息风险的区分度、校准度和临床实用性。
多因素logistic回归分析显示，少数民族（土家族）、男性、早产、先天性畸形、胎位异常、宫内窘迫、母亲职业为农民、高中以下文化程度、产前检查少于9次、先兆流产、脐带异常、羊水异常、前置胎盘、胎盘早剥、急诊剖宫产和助产是新生儿窒息的独立危险因素（P<0.05）。基于这些危险因素预测新生儿窒息风险的模型曲线下面积为0.748（95%：0.723 - 0.772）。校准曲线表明该模型预测新生儿窒息风险的准确性较高。决策曲线分析表明该模型可为有窒息风险的新生儿提供更高的净效益。
湖北恩施土家族苗族自治州新生儿窒息的危险因素是多方面的，基于这些因素的列线图模型在预测新生儿窒息风险方面具有良好价值，可帮助临床医生早期识别窒息高危新生儿，降低新生儿窒息发生率。
