The Utility of the Hypsarrhythmia Paroxysm Index and Sleep Spindles in EEG for Predicting Cognitive Outcomes in a Case Series of Infantile Spasms

Abstract

Objectives: Among patients with West Syndrome (WS), the underlying brain dysfunction is considered to be the major determinant of cognitive outcome. However, regardless of whether the WS type is symptomatic or cryptogenic, patients with infantile spasms may have a favourable or grim prognosis. The purpose of this study was to utilize electroencephalography (EEG) to guide treatment, and to assess cognitive outcome. Methods: We retrospectively reviewed the clinical and sleep EEG data in 16 patients who received adrenocorticotropic hormonal therapy between 2002 and 2014. The hypsarrhythmia paroxysm index (HPI), the frequency or duration of hypsarrhythmia paroxysms in non-rapid eye movement (NREM) sleep, and sleep spindles were evaluated in groups constituted according to etiology. Results: Cryptogenic patients whose onset of spasms began after 3 months of age and who experienced a shorter treatment delay had preserved spindles and favourable cognitive outcomes. Sleep spindles disappeared in cryptogenic patients with an early onset of spasms, long treatment delay, and high HPI at referral; their neurodevelopmental prognosis was unfavourable. In symptomatic cases in which the age of onset of spasms was earlier than 3 months and there had been a short treatment delay, sleep spindles never fully evolved, and the prognosis was poor. Patients with corpus callosum hypogenesis, a relatively mild malformation, had high HPI values at referral. While two of these patients did not display spindles during the follow-up, three had reappearing spindles as HPI values diminished, and the outcome was less severe in these patients. Conclusion: This study highlights the significance of EEG in studies of the prognosis of infantile spasms and demonstrates an objective approach that utilizes concurrent evaluation of sleep spindles and HPI.

Strength and stability of EEG functional connectivity predict treatment response in infants with epileptic spasms

“…Second, the features of hypsarrhythmic EEG vary with stage of sleep (Kellaway, 1985), necessitating accurate sleep staging prior to quantitative analysis. However, ES and hypsarrhythmia are associated with altered structure and progression of sleep stages, with notably diminished/absent REM sleep (Hrachovy et al, 1981) and sleep spindles in stage 2 sleep (Altunel A. et al, 2015), and EEG characteristics change during the course of treatment, making it infeasible to employ standardized sleep staging procedures to ensure the data were analyzed consistently.…”

Does the absence or presence of sleep spindles on EEG have prognostic value for cognitive outcome in children with infantile epileptic spasms syndrome? A systematic literature review

“…The literature screening by two researchers ended up with five published studies that covered the three required aspects: IESS, sleep spindles and cognitive outcome 20,22,23,25,26 (Figure 1). The contents of the papers in order of publication year, their details and PPV of the lack of sleep spindles for the risk of poor cognitive outcome are listed in Table 1 and Appendix S1.…”

“…4. Altunel et al 20 used in their retrospective study the Hypsarrhythmia Paroxysm Index 22 and sleep spindles to study 16 IESS patients treated with ACTH and to prevent relapses with small doses of ACTH when necessary. Cognitive development was measured at the median age of 5 years.…”

“…1. 4 | Rationale for the present study Different scoring systems [18][19][20][21] have been suggested as tools in the treatment of IESS in order to decide about type and duration of treatment or to predict seizure outcome or development. 22 Their use has, however, not become a widespread clinical guideline.…”

Automated detection of ripple oscillations in long-term scalp EEG from patients with infantile spasms

“…The fact that the IS cohort contained many patients that did not respond to treatment hindered our ability to analyze the effect of ACTH on HFO rate. Also, we were not able to perform manual sleep staging for the IS subjects (unlike for the control subjects), due to the disruption in typical sleep patterns [48][49][50][51]. However, we expect our automated wake/sleep classifier to have performed sufficiently for the IS subjects' datasets, given its 86% mean cross-validation accuracy for the control subjects and the assumption that sleep-wake features of EOG, EMG, ECG are consistent between control and IS subjects.…”