Analysis of influencing factors on brain injury after neonatal asphyxia resuscitation
10.3760/cma.j.cn115455-20240624-00531
- VernacularTitle:新生儿窒息复苏后合并脑损伤的影响因素分析
- Author:
Ru WANG
1
;
Huiling KANG
1
;
Yanchao LI
1
Author Information
1. 石家庄市妇幼保健院新生儿科，石家庄 050000
- Publication Type:Journal Article
- Keywords:
Asphyxia neonatorum;
Brain injuries;
Root cause analysis;
Nomograms
- From:
Chinese Journal of Postgraduates of Medicine
2025;48(3):250-256
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the influencing factors of brain injury in children with neonatal asphyxia after resuscitation.Methods:The clinical data of 180 children with neonatal asphyxia from January 2017 to January 2024 in Shijiazhuang Maternal and Child Health Hospital were retrospectively analyzed, and all children were received resuscitation treatment. The children were divided into modeling group (126 cases) and validation group (54 cases) in a 7∶3 ratio. Among the children in modeling group, 51 children combined brain injury (brain injury subgroup), and 75 children did not combine brain injury (non-brain injury subgroup). The general data were recorded, and the continuous variables were determined by the receiver operating characteristic (ROC) curve to determine the optimal cut-off value. Multivariate Logistic regression analysis was used to analyze the independent risk factors of brain injury in children with neonatal asphyxia after resuscitation. The R language software "rms" package was used to construct a nomogram model for predicting brain injury in children with neonatal asphyxia after resuscitation. The nomogram model was internally verified by the calibration curve, and the prediction efficiency of the nomogram model was evaluated by the decision curve and ROC curve.Results:There was no statistical difference in general data between modeling group and validation group ( P>0.05). The gestation age<37 weeks proportion, severe asphyxia proportion, Ⅱ to Ⅲ grade amniotic fluid contamination proportion, intrauterine distress proportion and blood lactate in brain injury subgroup were significantly higher than those in non-brain injury subgroup: 60.78% (31/51) vs. 38.67% (29/75), 37.25% (19/51) vs. 17.33% (13/75), 27.45% (14/51) vs. 10.67% (8/75), 47.06% (24/51) vs. 26.67% (20/75) and (2.64 ± 0.61) mmol/L vs. (2.21 ± 0.56) mmol/L, and there were statistical differences ( P<0.05 or <0.01); there were no statistical differences in gender composition, birth weight, maternal age, maternal history of adverse pregnancy and childbirth, mode of delivery, parity, abnormal amniotic fluid volume, abnormal fetal position, abnormal umbilical cord, abnormal placenta, systolic blood pressure, diastolic blood pressure, body temperature and blood glucose between the two groups ( P>0.05). ROC curve analysis result showed that the optimal cutoff value of blood lactate was 2.59 mmol/L. Multivariate Logistic regression analysis result showed that the young gestation age, severe asphyxia, Ⅱ to Ⅲ grade amniotic fluid contamination, intrauterine distress and high blood lactate were independent risk factors of brain injury in children with neonatal asphyxia after resuscitation ( OR = 2.854, 3.428, 3.405, 3.427 and 7.844; 95% CI 1.166 to 6.983, 1.263 to 9.305, 1.076 to 10.768, 1.358 to 8.645 and 3.080 to 19.978; P<0.05 or <0.01). The gestation age, degree of asphyxia, amniotic fluid contamination, intrauterine distress and blood lactate were used as predictors to construct a nomogram model for predicting brain injury in children with neonatal asphyxia after resuscitation. The calibration curve analysis result showed that the calibration curve of the nomogram model for predicting brain injury in children with neonatal asphyxia after resuscitation tended towards the ideal curve ( C- index = 0.824, 95% CI 0.745 to 0.903). The decision curve analysis result showed that, when the risk threshold was greater than 0.18, the clinical net benefits provided by the nomogram model were higher than those of a single independent risk factor, and it could provide significant additional clinical net benefits in predicting the high risk of brain injury in children with neonatal asphyxia after resuscitation. ROC curve of internal validation analysis result that the curve (AUC) of the nomogram model for predicting brain injury in children with neonatal asphyxia after resuscitation was 0.824 (95% CI 0.744 to 0.903). ROC curve of external validation result showed that the AUC of the nomogram model for predicting brain injury in children with neonatal asphyxia after resuscitation was 0.838 (95% CI 0.714 to 0.962). Conclusions:The gestation age, degree of asphyxia, amniotic fluid contamination, intrauterine distress and blood lactate are independent risk factors for brain injury in children with neonatal asphyxia after resuscitation. The nomogram model constructed based on these factors has a high clinical benefit in predicting brain injury in children with neonatal asphyxia after resuscitation.
