Cureus · 2025
Continuous Electroencephalogram Monitoring in the Intensive Care Unit.
СтатьяНазвание и аннотация: Перевод в очереди
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Papaioannou Maria, Vasileiadou Georgia, Soulountsi Vasiliki, Dimaki Anastasia, Bikouli Anastasia, Lavrentieva Athina
Аннотация
OBJECTIVE: In recent years, continuous electroencephalogram (cEEG) monitoring has become a noninvasive tool for detecting and evaluating brain activity in critical care patients. The study aimed to evaluate the impact of cEEG on the management of comatose intensive care unit (ICU) patients and its association with disease prognosis.
METHODS: This observational, prospective study included adult patients in a single general ICU. The study analyzed patients' demographic characteristics, illness severity using the APACHE II (acute physiology and chronic health evaluation) and SOFA (sequential organ failure assessment) scores, comorbidities, reasons for undertaking EEG, EEG patterns, and therapeutic strategies used. Continuous EEG patterns were correlated with patients' outcomes.
RESULTS: Data from 55 patients were analyzed, with a median age of 61 years (range 19-86), a median APACHE II score of 22 (range 5-38), and a median SOFA score of 9 (range 4-16) on admission. The median duration of mechanical ventilation was 35 days, and the median ICU length of stay (LOS) was 42 days. The majority of cEEGs (61.1%) were performed due to medical reasons (ischemic stroke, septic shock, status epilepticus); 25.9% of cEEGs were conducted in neurosurgical patients with post-traumatic acute brain injury and malignancy, whereas 13% were carried out in post-anoxic comatose patients. Patients who started antiepileptic therapy after EEG examination had a higher mortality rate than patients who had already received antiepileptic drugs (AED) (x2=7.9565, p=0.004). In more than half of the patients, an encephalopathic EEG pattern was observed in comparison with a lower percentage of patients who had lateralized periodic discharges (LPDs); one patient had burst suppression, and one patient had electrocerebral inactivity (ECI) or silence (ECS). Patients with epileptic disorders had a higher mortality rate (p=0.018) in comparison with the other categories of patients. Taking their medical comorbidities into consideration, patients diagnosed with diabetes mellitus were more likely to have higher mortality (x2=5.115, p=0.024).
CONCLUSION: The management of critically ill patients is influenced by cEEG, which could modify therapeutic strategies and appears to be a useful prognostic tool in critical care patients.
METHODS: This observational, prospective study included adult patients in a single general ICU. The study analyzed patients' demographic characteristics, illness severity using the APACHE II (acute physiology and chronic health evaluation) and SOFA (sequential organ failure assessment) scores, comorbidities, reasons for undertaking EEG, EEG patterns, and therapeutic strategies used. Continuous EEG patterns were correlated with patients' outcomes.
RESULTS: Data from 55 patients were analyzed, with a median age of 61 years (range 19-86), a median APACHE II score of 22 (range 5-38), and a median SOFA score of 9 (range 4-16) on admission. The median duration of mechanical ventilation was 35 days, and the median ICU length of stay (LOS) was 42 days. The majority of cEEGs (61.1%) were performed due to medical reasons (ischemic stroke, septic shock, status epilepticus); 25.9% of cEEGs were conducted in neurosurgical patients with post-traumatic acute brain injury and malignancy, whereas 13% were carried out in post-anoxic comatose patients. Patients who started antiepileptic therapy after EEG examination had a higher mortality rate than patients who had already received antiepileptic drugs (AED) (x2=7.9565, p=0.004). In more than half of the patients, an encephalopathic EEG pattern was observed in comparison with a lower percentage of patients who had lateralized periodic discharges (LPDs); one patient had burst suppression, and one patient had electrocerebral inactivity (ECI) or silence (ECS). Patients with epileptic disorders had a higher mortality rate (p=0.018) in comparison with the other categories of patients. Taking their medical comorbidities into consideration, patients diagnosed with diabetes mellitus were more likely to have higher mortality (x2=5.115, p=0.024).
CONCLUSION: The management of critically ill patients is influenced by cEEG, which could modify therapeutic strategies and appears to be a useful prognostic tool in critical care patients.