PhD Defence Astrid Glimmerveen | From Coma to Cognitive Recovery - Predicting Long-term Outcomes after Cardiac Arrest

From Coma to Cognitive Recovery - Predicting Long-term Outcomes after Cardiac Arrest

Astrid Glimmerveen is a PhD student in the Department of Clinical Neurophysiology. (Co)Promotors are prof.dr. J. Hofmeijer from the Faculty of Science & Technology and dr. H.M. Keijzer from Rijnstate Hospital.

Brain injury after cardiac arrest remains a major challenge. While survival rates have improved, recovery is far from guaranteed: many patients never regain consciousness, and those who do often face lasting cognitive or emotional difficulties. Reliable prognostication and early identification of rehabilitation needs are therefore crucial to improve both treatment decisions and long-term outcomes.

The first part of this thesis focused on comatose patients. We showed that somatosensory evoked potentials (SSEP) and electroencephalography (EEG) provide complementary prognostic information. Absent or very low SSEP amplitudes, as well as malignant EEG patterns, were invariably associated with poor outcome. Their combination increased sensitivity without loss of specificity, supporting their role in multimodal prognostication.

The second part examined predictors of long-term outcome. Simple bedside measures such as the Montreal Cognitive Assessment (MoCA) and Hospital Anxiety and Depression Scale (HADS) during hospital admission were already associated with cognition and emotional wellbeing at one year. Quantitative EEG features, including peak frequency and alpha-to-theta ratio, also showed associations with later memory performance, though further validation is needed before clinical use.

Finally, our sleep study revealed a high prevalence of disorders, such as obstructive sleep apnea, that were linked to cognitive impairment but often underreported in questionnaires. This highlights sleep as a potential target for intervention in survivors.

This thesis contributes to the field by improving acute-phase prognostication and exploring potential predictors of long-term recovery after cardiac arrest. By integrating EEG, SSEP, bedside screening, and sleep assessment, it provides a foundation for more personalized prognostication and care, aiming to shift the focus from survival alone to meaningful recovery.