关键词:

新生儿, 高胆红素血症, Lasso回归模型, 母婴结局, 预防对策

Abstract:

Objective　To investigate the relationships between maternal-infant factors and neonatal hyperbilirubinemia by Lasso regression model, and explore its preventive measures. Methods　A retrospective analysis was conducted on the medical records of 200 pregnant women and their newborns in Yangling Demonstration Zone Hospital from January to December 2023. Among the pregnant women, there were 113 primiparas and 87 multiparas, the age ranged from 23 to 40 (31.42±5.64) years old. Among the newborns, there were 105 boys and 95 girls; the birth weight ranged from 1 511 to 4 035 (2 506.50±1 019) g. All newborns were divided into an occurrence group (108 cases) and a non-occurrence group (92 cases) by the results of hyperbilirubinemia. The baseline data of pregnant women and newborns (such as number of deliveries, delivery methods, gestational age, birth weight, etc.) of two groups were collected. Lasso regression was used to select variables, and then logistic regression analysis was used to identify the risk factors influencing the occurrence of hyperbilirubinemia in newborns. R language was used to establish a nomogram prediction model and the efficiency of the prediction model was analyzed. Statistical analysis was conducted using the χ² test and independent sample t test. Results　After conducting a single-factor screening of the 22 most representative risk factors that affected the occurrence of hyperbilirubinemia in newborns, and analyzing 22 variables using Lasso regression and completing ten-fold cross-validation, it was found that a total of 11 variables were selected when λ = 0.035 090. The obtained variables were included in the multivariate logistic regression analysis. The results showed that cesarean section (OR = 2.217), blood type incompatibility of mother and infant (OR = 2.863), gestational age < 34 weeks (OR = 1.842), and onset time of neonatal jaundice < 24 h (OR = 1.799) were risk factors for neonatal hyperbilirubinemia (all P<0.05). In addition, Bootstrap method (B=1000) was used to verify the model. The area under the curve (AUC) of the predicted model was 0.833, 95%CI 0.779 - 0.887. The mean absolute error and mean square error of the calibration curve were 0.033 and 0.001 42. There was no statistically significant difference between the actual and predicted values (χ²= 5.407, P=0.713). The net benefit rate of the decision curve analysis (DCA) curve was good. Conclusions　Maternal and infant factors such as delivery modes, incompatibility of blood group, gestational age, and onset time of neonatal jaundice may increase the risk of neonatal hyperbilirubinemia. Clinical intervention can be strengthened to reduce the risk of neonatal hyperbilirubinemia.

Key words:

Newborns, Hyperbilirubinemia, Lasso regression model, Maternal and infant outcomes, Preventive countermeasures