BackgroundThe aim of this study was to analyze amplitude-integrated electroencephalography (aEEG) in early diagnosis and prognosis of hypoxic encephalopathy (HE) in premature infants.Material/MethodsThirty-six premature infants with HE who were treated in Linyi Central Hospital were enrolled into the study group, while 40 premature infants without HE were assigned into the control group. aEEG was conducted within 6 h after delivery to compare aEEG continuity, mature sleep-wake cycle, and maximum and minimum voltage in the 2 groups. Correlations between aEEG abnormalities and clinical grading, neurological prognosis, Apgar score, and blood gas were also analyzed among the premature infants with HE.ResultsCompared with the control group, there were reductions in the continuous rate of aEEG, mature sleep-wake cycle, and the minimum voltage, and an increase in the maximum voltage in the study group (all P<0.05). The study group had a higher abnormal rate of aEEG and a lower normal rate of aEEG than in the control group (both P<0.05). Spearman’s rank correlation coefficients for abnormal aEEG and clinical grade and poor neurological prognosis were 0.758 and 0.799, respectively. The sensitivity of abnormal aEEG in predicting severity of clinical grading was 100% with a specificity of 82.5%. The sensitivity of abnormal aEEG in predicting neurological prognosis was 100% with a specificity of 90.3%. The Apgar scores and blood glass pH of the infants with various abnormal rates of aEEG were significantly different at 1 min, 5 min, and 10 min after delivery (all P<0.05).ConclusionsHE in premature infants has specific aEEG characteristics, which can be used to predict the severity and prognosis of HE.
Backgrounds and AimsBronchopulmonary dysplasia (BPD) has serious immediate and long-term sequelae as well as morbidity and mortality. The objective of this study is to develop a predictive model of BPD for premature infants using clinical maternal and neonatal parameters.MethodsThis single-center retrospective study enrolled 237 cases of premature infants with gestational age less than 32 weeks. The research collected demographic, clinical and laboratory parameters. Univariate logistic regression analysis was carried out to screen the potential risk factors of BPD. Multivariate and LASSO logistic regression analysis was performed to further select variables for the establishment of nomogram models. The discrimination of the model was assessed by C-index. The Hosmer-Lemeshow test was used to assess the calibration of the model.ResultsMultivariate analysis identified maternal age, delivery option, neonatal weight and age, invasive ventilation, and hemoglobin as risk predictors. LASSO analysis selected delivery option, neonatal weight and age, invasive ventilation, hemoglobin and albumin as the risk predictors. Both multivariate (AUC = 0.9051; HL P = 0.6920; C-index = 0.910) and LASSO (AUC = 0.8935; HL P = 0.7796; C-index = 0.899) - based nomograms exhibited ideal discrimination and calibration as confirmed by validation dataset.ConclusionsThe probability of BPD in a premature infant could be effectively predicted by the nomogram model based on the clinical maternal and neonatal parameters. However, the model required external validation using larger samples from multiple medical centers.
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