Clinical Correlates of Electroencephalographic Patterns in Critically Ill Patients

Clinical and Radiological Parameters Affecting the Yield of Routine Electroencephalography in Various Indications

OBJECTIVES

To highlight the significance of various clinical and radiological parameters in association with specific electroencephalographic (EEG) patterns in order to prioritize EEG referrals.

METHOD

This retrospective, cross-sectional study was conducted in the neurology department of King Fahad University Hospital, Alkhobar, and involved a review and analysis of EEG and medical records pertaining to 604 patients referred for routine EEG. The data were analyzed using SPSS version 22. An association between various parameters and EEG yield was established.

RESULTS

Factors associated with the yield of abnormal EEG patterns were diverse, like generalized tonic-clonic seizures (GTCs) (P =.05), status epilepticus (SE) (P =.05), altered level of consciousness (ALC) (P =.00), abnormal movement (P =.00), cardiac arrest (P =.00), prior history of epilepsy (P =.04), chronic renal disease (CRD) (P =.03), abnormal neurological exam (P =.00), and cortical lesions on brain imaging (P =.00). Among the abnormal EEG patterns, epileptiform activity (EA) in EEG was associated with focal seizures (P =.03), GTCs (P =.00), falls (P =.05), cardiac arrest (P =.00), a history of epilepsy (P =.00), and hypoxic ischemic injury (P =.03). Encephalopathy in EEG was also associated with focal sz (P =.02), GTCs (P =.00), SE (P =.01), ALC (P =.00), cardiac arrest (P =.00), history of stroke (P =.01), and epilepsy (P =.00).

CONCLUSION

Among the studied parameters, patient level of consciousness, neurological exam findings, and neuroimaging findings, with some discrepancies, were found to be the most consistent in predicting the EEG yield. The study demonstrated the value of a proper neurological exam and careful selection of patients to gain the optimum benefit from the routine EEG.

The Relative Impact of Clinical and Investigational Factors to Predict the Outcome in Stroke Patients

OBJECTIVE

As stroke is still considered a significant cause of mortality and morbidity, it is crucial to find the factors affecting the outcome in these patients. We aimed to interpret the various clinical and investigational parameters and establish their association with the outcome in stroke patients.

MATERIALS AND METHODS

This is a retrospective, cross-sectional study, conducted in the Department of Neurology between June 2019 to November 2021. The study involved the review and analysis of medical records pertaining to 264 patients, admitted with the diagnosis of stroke. Various clinical, radiological, and electroencephalographic (EEG) patterns in stroke patients were analyzed and their association with outcome was established. The association between the studied variables was performed by the logistic regression (LR) and presented as odds ratio (OR) and 95% confidence interval (CI).

RESULTS

The study sample consisted of 264 patients. Males comprised 165 (62.5%) with the mean participant age of 57.17 ± 18.7 3 years (range: 18-94). Patients younger than 50 years had a better likelihood of a good outcome in comparison to patients older than 50. The admission location was the most significant factor in predicting the outcome ( P = 0.00) in favor of inpatient department and outpatient department (OPD), in contrast to patients admitted directly to intensive care unit (ICU). Normal EEG was associated with good outcome ( P = 0.04; OR, 3.3; CI, 1.01-10.88) even after adjustment of the confounders, whereas patients having marked EEG slowing had a poor outcome ( P = 0.05; OR, 2.4; CI, 0.65-8.79). Among the clinical parameters, hemiparesis ( P = 0.03), trauma ( P = 0.01), generalized tonic-clonic seizures (GTC) ( P = 0.00), and National Institutes of Health Stroke Scale of more than 4 were more likely associated with a poor outcome as well as the presence of intracranial hemorrhage (ICH) or infarction in the cortical and cortical/subcortical locations were associated with poor outcomes. After adjustment of confounders, the factors found to have prognostic significance in favor of good outcomes were inpatients or OPD referrals and normal EEG while direct admission to ICU, marked slowing on EEG, and presence of ICH were found to be associated with poor outcome.

CONCLUSION

Certain patterns are predictive of good or worse outcomes in stroke patients. Early identification of these factors can lead to early intervention, which in turn might help in a better outcome. The results of the study, therefore, have some prognostic significance.

The predictive value of EEG reactivity by electrical stimulation and quantitative analysis in critically ill patients with large hemispheric infarction

PURPOSE

The intensive care of critically ill patients with large hemispheric infarction improves the survival rate. However, established prognostic markers for neurological outcome show variable accuracy. We aimed to assess the value of electrical stimulation and quantitative analysis of EEG reactivity for early prognostication in this critically ill population.

MATERIALS AND METHODS

We prospectively enrolled consecutive patients between January 2018 and December 2021. EEG reactivity was randomly performed by pain or electrical stimulation via visual and quantitative analysis. Neurological outcome within 6-month was dichotomized as good (modified Rankin Scale, mRS 0-3) or poor (mRS 4-6).

RESULTS

Ninety-four patients were admitted, and 56 were included in the final analysis. EEG reactivity using electrical stimulation was superior to pain stimulation for good outcome prediction (visual analysis: AUC 0.825 vs. 0.763, P = 0.143; quantitative analysis: AUC 0.931 vs. 0.844, P = 0.058). The AUC of EEG reactivity by pain stimulation with visual analysis was 0.763, which increased to 0.931 by electrical stimulation with quantitative analysis (P = 0.006). When using quantitative analysis, the AUC of EEG reactivity increased (pain stimulation 0.763 vs. 0.844, P = 0.118; electrical stimulation 0.825 vs. 0.931, P = 0.041).

CONCLUSION

EEG reactivity by electrical stimulation and quantitative analysis seems a promising prognostic factor in these critical patients.