Time‐dependent risk of seizures in critically ill patients on continuous electroencephalogram
Systematic detection of electrographic seizures with CEEG requires time from electroencephalographic (EEG) technologists and clinical neurophysiologists as well as financial support from payers.13 This constitutes a barrier to the development of CEEG monitoring programs, especially in resource‐limited settings.
Prior studies sought to identify means to reduce the burden of CEEG. A first line of studies investigated quantitative EEG analysis and time‐compressed EEG displays to decrease reviewing time.15 Although successful and already implemented in some institutions, such approaches do not reduce the need for personnel and technical supply. A second group of studies aimed to identify a subgroup of high‐risk patients on whom to focus CEEG efforts. These studies relied on clinical factors, such as a history of epilepsy, coma, and clinical seizures prior to monitoring,1 early EEG findings that are detected prior to electrographic seizures, or a combination of both.7 A first issue with these studies is that they did not consider all the known EEG risk factors for electrographic seizures.1 Some were performed in highly selected populations.19 Another limitation in most prior studies is the failure to account for subject dropout, which might underestimate the risk of further seizures, especially in those considered to be at low risk clinically (because they receive shorter monitoring). In practice, if epileptiform discharges are noted during CEEG, many interpreting physicians tend to continue monitoring longer. A longer duration of monitoring increases the chance of capturing a seizure. This is true even if the epileptiform discharges do not modify the risk of seizures at all—leading to false correlations and self‐fulfilling prophecies. A somewhat different but related error is the overestimation of incidence of seizures at later time points. Patients with a high clinical suspicion of seizures are on CEEG longer than those with a lower risk, independent of EEG findings. This bias will increase the proportion of high‐risk patients at later time points and result in an overestimation of the risk of seizures at later time points.
A principled approach to address the problem of subject dropout as described above is survival analysis. Survival analysis has been applied to this problem on 2 previous occasions.17 In both studies, it was shown that the risk for seizures decays more rapidly than previously suspected, giving credence to the hypothesis of overestimation of seizure risk with long monitoring. However, their analyses did not account for the emergence of EEG risk factors during monitoring and how this affected the subsequent risk of seizures.
The purpose of this study was to develop models for the time‐dependent risk of electrographic seizures in critically ill patients as a function of baseline clinical risk factors and of abnormalities that may emerge during EEG monitoring. Such models provide a way to personalize the duration of EEG monitoring based on a patient's specific baseline risk factors and EEG findings. Multistate survival analysis provides a principled framework for our analysis.