Risk factors for neonatal asphyxia and establishment of a nomogram model for predicting neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture: a multicenter study
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
Abstract Objective 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. Methods 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. Results 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 (P<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%CI: 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. Conclusions 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.
JIN Fang,CHEN Yu,LIU Yi-Xun et al. Risk factors for neonatal asphyxia and establishment of a nomogram model for predicting neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture: a multicenter study[J]. CJCP, 2023, 25(7): 697-704.
JIN Fang,CHEN Yu,LIU Yi-Xun et al. Risk factors for neonatal asphyxia and establishment of a nomogram model for predicting neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture: a multicenter study[J]. CJCP, 2023, 25(7): 697-704.
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