Amplitude-integrated electroencephalography improves the predictive ability of acute bilirubin encephalopathy

Abstract

Background: To establish a clinical prediction model of acute bilirubin encephalopathy (ABE) using amplitude-integrated electroencephalography (aEEG). Methods: A total of 114 neonatal hyperbilirubinemia patients in the Beijing Chaoyang hospital from August 2015 to October 2018 were enrolled in this study. There were 62 (54.38%) males, and the age of patients undergoing aEEG examination was 2-23 days, with an average of 7.61±4.08 days. Participant clinical information, peak bilirubin value, albumin value, hyperbilirubinemia, and the graphic indicators of aEEG were extracted from medical records, and ABE was diagnosed according to a bilirubin-induced neurological dysfunction (BIND) score >0. Multivariable logistic regression was used to establish a clinical prediction model of ABE. Furthermore, decision curve analysis (DCA) was performed to evaluate the model's predictive value. Results: According to the BIND score, there were a total of 23 (20.18%) ABE cases. The multivariable logistic regression analysis showed that the value of bilirubin/albumin (B/A), presence of hyperbilirubinemia risk factors, number of sleep-wake cycling (SWC) within 3 hours, widest bandwidth, duration of SWC, and type of SWC were significantly associated with ABE. A clinical prediction model was developed as: p=ex/ (1+ex), X=0.278+0.713*B/A+2.602*with risk factors (with risk factors equals 1) − 1.500*SWC number within 3 hours + 0.219*the widest bandwidth-0.065*the duration of one SWC + 1.491* SWC (mature SWC equals 0, immature SWC equals 1). The area under the curve (AUC) was 0.85 [95% confidence interval (CI): 0.75-0.94], which was significantly higher than the AUC only based on conventional clinical information of B/A (AUC: 0.58, 95% CI: 0.45-0.72). The DCA also showed good predictive ability compared to B/A. Conclusions: A clinical prediction model can be established based on the patients' B/A, presence of risk factors for hyperbilirubinemia, number of SWC within 3 hours, widest bandwidth, duration of 1 SWC, and the type of SWC. It has good predictive ability and may improve the diagnostic accuracy of ABE.

Establishment and Evaluation of Influencing Factors and Risk Prediction Model of Severe Neonatal Hyperbilirubinemia Complicated with Acute Bilirubin Encephalopathy

“…At the same time, factors other than bilirubin, such as gestational age, hemolytic disease, infection, mode of delivery, feeding method, etc., may also have an impact on children with severe hyperbilirubinemia complicated by ABE [7,8]. In addition, multiple studies have shown that there is often an interaction between multiple factors that lead to the occurrence and development of the disease [9][10][11].…”

The development and validation of a predictive model for neonatal phototherapy outcome using admission indicators

“…Chang et al developed a clinical prediction model for ABE using the bilirubin/albumin (B/A) ratio, risk factors for hyperbilirubinemia, and amplitude-integrated electroencephalography (aEEG) data. The model showed reasonable predicting power and was believed to improve accuracy of diagnosing ABE ( 13 ) but lacked specific evaluation indicators and failed to yield adequate evidence for assessing probability of ABE based solely on aEEG data. Bhutani et al created the bilirubin-induced-neurologic dysfunction (BIND) score for early identification of BE ( 14 ).…”