Objective The aim of the study is to model amplitude-integrated electroencephalography (aEEG) utility to diagnose seizures in common clinical scenarios.
Study Design Using reported neonatal seizure prevalence and aEEG sensitivities and specificities, likelihood ratios (LRs) and post-test probabilities were calculated to quantify aEEG utility to diagnose seizures in three typical clinical scenarios.
Results Prevalence data supported pretest probabilities for neonatal seizures of 0.4 in neonatal hypoxic ischemic encephalopathy (HIE), 0.27 in bacterial meningitis, and 0.05 in extreme prematurity. Reported sensitivity of 85% and specificity of 90% for seizures with expert aEEG interpretation yielded a positive likelihood ratio (LR+) of 8.7 and a negative likelihood ratio (LR−) of 0.17. Reported sensitivity of 65% and specificity of 70% with intermediate interpretation yielded LR+ 2.17 and LR− 0.5. Reported sensitivity of 40% and sensitivity of 50% with inexperienced interpretation gave LR+ 0.8 and LR− 1.2. These translate the ability to move pretest to post-test probability highly dependent on user expertise. For HIE, a pretest probability of seizure of 0.4 moves to a post-test probability of 0.85 when aEEG is positive for seizures by expert interpretation, and down to 0.1 when aEEG is negative. In contrast, no useful information was gained between pretest and post-test probability by aEEG interpreted as negative or positive for seizure at the inexperienced user level. Similarly, in the models of meningitis or extreme prematurity, incremental information gained from aEEG ranged widely based on interpreter experience.
Conclusion aEEG is most useful to screen for neonatal seizures when used in conditions with high seizure prevalence, and when interpretation has a sensitivity and specificity as reported for expert users. In contrast, aEEG can become negligible in providing meaningful clinical information when applied in conditions having lower seizure prevalence or when interpretation has low accuracy. Appropriate patient selection and high quality interpretation are essential for aEEG utility in neonatal seizure detection.