Amplitude-Integrated Electroencephalography for Early Diagnosis and Prognostic Prediction of Hypoxic Encephalopathy in Preterm Infants

Prolonged Background Suppression Induced by Anticonvulsants Misleading Amplitude-Integrated Encephalography's Interpretation: A Single Case Report

Prognostic role of the amplitude-integrated encephalography (aEEG) is undeniable, but few works focused on the contribution of medications in misleading its interpretation. We report the case of an asphyxiated newborn enrolled for therapeutic hypothermia (TH) that required the simultaneous use of three anticonvulsants, whose administration resulted in a comatose state and in a switch to a flat trace on the aEEG. The prolonged flat trace on the aEEG, pointing toward a state of irreversible brain damage, led to early stop of TH to prevent therapeutic obstinacy; unexpectedly, once Midazolam was weaned off, the aEEG recovered to a discontinuous pattern. The case emphasizes the aEEG's noninfallibility and advises clinicians to consider the potential misleading effects of depressant medications on its interpretation in asphyxiated newborns undergoing TH.

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

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.