Abstract
Objective:Severe preeclampsia is a pregnancy complication that poses a serious threat to maternal and perinatal health.Early identification of high-risk patients is crucial for improving prognosis.This study aims to investigate the effects of changes in serum uric acid(UA),liver function indicators,and placental growth factor(PLGF)levels on maternal and neonatal outcomes in patients with early-onset severe preeclampsia,and to establish a multi-index combined risk prediction model to facilitate early clinical recognition of high-risk preeclampsia patients.
Methods:A retrospective analysis was conducted on 246 patients with early-onset severe preeclampsia admitted to Dengzhou Central Hospital between January 2023 and December 2024.According to maternal and neonatal outcomes,patients were divided into a good-outcome group(n=166)and a poor-outcome group(n=80).General clinical data and laboratory indicators were collected.Univariate analysis and multivariate Logistic regression were used to identify independent risk factors for adverse maternal and neonatal outcomes.Based on the Logistic regression results,a combined predictive model was constructed,and receiver operating characteristic(ROC)curves were plotted to evaluate its predictive performance.
Results:Compared with the good-outcome group,patients in the poor-outcome group had significantly higher body mass index(BMI),a higher proportion of obesity,and elevated levels of serum UA,albumin(ALB),alanine aminotransferase(ALT),and aspartate aminotransferase(AST),while their gestational age at delivery and PLGF levels were significantly lower(all P<0.05).Neonatal indicators,including birth weight,Apgar score,and the proportion of neonates admitted to the neonatal intensive care unit(NICU)for>24 hours,also differed significantly between the 2 groups(all P<0.05).Logistic regression analysis identified elevated UA,ALB,ALT,and AST levels,earlier gestational age at delivery,obesity,and lower PLGF levels as independent risk factors for poor maternal and neonatal outcomes in patients with early-onset severe preeclampsia(all P<0.05).Pearson correlation analysis revealed that BMI classification during pregnancy was significantly correlated with UA(r=0.42,P<0.01)and ALT(r=0.38,P<0.01);thus,BMI classification was excluded from the final model to avoid multicollinearity and ensure balanced weighting of other indicators.The combined predictive model was constructed using 6 variables,gestational age at delivery,UA,ALB,ALT,AST,and PLGF.ROC curve analysis showed that the area under the curve(AUC)values for predicting poor maternal and neonatal outcomes using gestational age,UA,ALB,ALT,AST,PLGF individually,and the combination model were 0.666,0.762,0.747,0.768,0.828,0.747,and 0.989 respectively.The sensitivities were 70.0%,92.5%,100.0%,87.5%,71.3%,100.0%,and 90.0%,and the specificities were 57.8%,55.4%,45.8%,54.2%,83.1%,50.0%,and 99.4%respectively.The combined model demonstrated significantly higher predictive efficiency than any single indicator(all P<0.001).
Conclusion:The combined prediction model provides significantly better predictive performance for adverse maternal and neonatal outcomes in patients with early-onset severe preeclampsia than single indicators,and shows high potential clinical application value.关键词
子痫前期/母婴结局/血尿酸/胎盘生长因子/分娩孕周/白蛋白/丙氨酸氨基转移酶/天门冬氨酸氨基转移酶Key words
early-onset severe preeclampsia/maternal and neonatal outcomes/serum uric acid/placental growth factor/gestational age at delivery/albumin/alanine aminotransferase/aspartate aminotransferase
