Babak Razavi

All Publications

• Comparative neuropsychological effects of carbamazepine and eslicarbazepine acetate. Epilepsy & behavior : E&B Meador, K. J., Seliger, J., Boyd, A., Razavi, B., Falco-Walter, J., Le, S., Loring, D. W. 2019; 94: 151–57

  Abstract

  People with epilepsy are at increased risk for neuropsychological dysfunction due to multiple factors, of which the most amendable are antiseizure medications (ASMs). Antiseizure medication effectiveness is frequently determined by tolerability. In this study, we compared the neuropsychological effects of eslicarbazepine acetate (ESL) and carbamazepine immediate-release (CBZ) using a randomized, double-blind, crossover design in healthy volunteers with a 2-week titration and 4-week maintenance phase in each treatment arm (CBZ = 400 mg BID and ESL = 800 mg qAM). Neuropsychological testing was performed at the initial visit, repeated at 1st baseline nondrug condition, end treatment #1, 2nd nondrug condition one month after treatment #1, end treatment #2, and 3rd nondrug condition one month after treatment #2. Neuropsychological testing was conducted 2 h after morning dose and included computer (i.e., dual task test, selective attention test, symbol digit, verbal memory, visuospatial memory, and 1- & 2-back continuous performance) and noncomputer tasks (i.e., Medical College of Georgia (MCG) paragraph memory, Stroop, Symbol Digit Modalities Test, Profile of Mood States). z-Scores calculated from nondrug conditions were used to compare ESL and CBZ for the 23 completers. Follow-up analyses included individual test scores and distribution of individual raw means. Mean blood levels on test day were CBZ = 8.9 mug/ml and ESL = 15.3 mug/ml. Omnibus z-score was significantly better for ESL (p = .0001). For individual measures, executive function and selective attention tests were statistically significantly better for ESL. Individual test raw means favored ESL over CBZ on 22 of 30 measures (p = .016, 2-tailed sign test). Eslicarbazepine acetate demonstrated less adverse neuropsychological effects than CBZ.

  View details for DOI 10.1016/j.yebeh.2019.02.034

  View details for PubMedID 30939410

• Reply to "Syncope is associated with electroencephalography changes" and to "Video-EEG during tilt-table testing is an invaluable aid for understanding syncope". Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Muppidi, S., Miglis, M. G., Razavi, B. 2018; 129 (7): 1500–1501

  View details for DOI 10.1016/j.clinph.2018.04.601

  View details for PubMedID 29729888

• The clinical utility of qualitative electroencephalography during tilt table testing - A retrospective study CLINICAL NEUROPHYSIOLOGY Muppidi, S., Razavi, B., Miglis, M. G., Jaradeh, S. 2018; 129 (4): 783–86

  Abstract

  To assess electroencephalography (EEG) changes during tilt table testing in syncope and other orthostatic syndromes.We retrospectively reviewed consecutive tilt table studies with simultaneous EEG from April 2014 to May 2016 at our center. All patients had video EEG during tilt table. All patients had at least 10 min of head up tilt unless they had syncope or did not tolerate the study. Video EEG was interpreted by epileptologists.Eighty-seven patients met the inclusion criteria. Mean age was 45 years, and 55 were women. Seven patients (∼8%) had syncope during tilt table, 11 patients (∼12%) had significant neurogenic orthostatic hypotension and a separate group of 11 patients (∼12%) had significant orthostatic tachycardia. Valsalva responses were abnormal in 7 of the 11 patients with orthostatic hypotension, suggesting an underlying neurogenic orthostatic hypotension. Visually discernable EEG changes were seen in only 3 patients (∼43%) who had syncope and in 1 patient (∼9%) with orthostatic tachycardia.Qualitative EEG analysis based on visual inspection during tilt table study revealed abnormalities in less than half the patients with syncope and a very small fraction with orthostatic tachycardia.Routine qualitative EEG recording might not be clinically useful during tilt table studies.

  View details for DOI 10.1016/j.clinph.2018.01.058

  View details for Web of Science ID 000427485900010

  View details for PubMedID 29448152

• Quantitative EEG Metrics Differ Between Outcome Groups and Change Over the First 72 h in Comatose Cardiac Arrest Patients NEUROCRITICAL CARE Wiley, S., Razavi, B., Krishnamohan, P., Mlynash, M., Eyngorn, I., Meador, K. J., Hirsch, K. G. 2018; 28 (1): 51–59

  Abstract

  Forty to sixty-six percent of patients resuscitated from cardiac arrest remain comatose, and historic outcome predictors are unreliable. Quantitative spectral analysis of continuous electroencephalography (cEEG) may differ between patients with good and poor outcomes.Consecutive patients with post-cardiac arrest hypoxic-ischemic coma undergoing cEEG were enrolled. Spectral analysis was conducted on artifact-free contiguous 5-min cEEG epochs from each hour. Whole band (1-30 Hz), delta (δ, 1-4 Hz), theta (θ, 4-8 Hz), alpha (α, 8-13 Hz), beta (β, 13-30 Hz), α/δ power ratio, percent suppression, and variability were calculated and correlated with outcome. Graphical patterns of quantitative EEG (qEEG) were described and categorized as correlating with outcome. Clinical outcome was dichotomized, with good neurologic outcome being consciousness recovery.Ten subjects with a mean age = 50 yrs (range = 18-65) were analyzed. There were significant differences in total power (3.50 [3.30-4.06] vs. 0.68 [0.52-1.02], p = 0.01), alpha power (1.39 [0.66-1.79] vs 0.27 [0.17-0.48], p < 0.05), delta power (2.78 [2.21-3.01] vs 0.55 [0.38-0.83], p = 0.01), percent suppression (0.66 [0.02-2.42] vs 73.4 [48.0-97.5], p = 0.01), and multiple measures of variability between good and poor outcome patients (all values median [IQR], good vs. poor). qEEG patterns with high or increasing power or large power variability were associated with good outcome (n = 6). Patterns with consistently low or decreasing power or minimal power variability were associated with poor outcome (n = 4).These preliminary results suggest qEEG metrics correlate with outcome. In some patients, qEEG patterns change over the first three days post-arrest.

  View details for DOI 10.1007/s12028-017-0419-2

  View details for Web of Science ID 000425631100008

  View details for PubMedID 28646267

• Detecting silent seizures by their sound. Epilepsia Parvizi, J., Gururangan, K., Razavi, B., Chafe, C. 2018

  Abstract

  The traditional approach to interpreting electroencephalograms (EEGs) requires physicians with formal training to visually assess the waveforms. This approach can be less practical in critical settings where a trained EEG specialist is not readily available to review the EEG and diagnose ongoing subclinical seizures, such as nonconvulsive status epilepticus.We have developed a novel method by which EEG data are converted to sound in real time by letting the underlying electrophysiological signal modulate a voice tone that is in the audible range. Here, we explored whether individuals without any prior EEG training could listen to 15-second sonified EEG and determine whether the EEG represents seizures or nonseizure conditions. We selected 84 EEG samples to represent seizures (n = 7), seizure-like activity (n = 25), or nonperiodic, nonrhythmic activity (normal or focal/generalized slowing, n = 52). EEGs from single channels in the left and right hemispheres were then converted to sound files. After a 4-minute training video, medical students (n = 34) and nurses (n = 30) were asked to designate each audio sample as "seizure" or "nonseizure." We then compared their performance with that of EEG-trained neurologists (n = 12) and medical students (n = 29) who also diagnosed the same EEGs on visual display.Nonexperts listening to single-channel sonified EEGs detected seizures with remarkable sensitivity (students, 98% ± 5%; nurses, 95% ± 14%) compared to experts or nonexperts reviewing the same EEGs on visual display (neurologists, 88% ± 11%; students, 76% ± 19%). If the EEGs contained seizures or seizure-like activity, nonexperts listening to sonified EEGs rated them as seizures with high specificity (students, 85% ± 9%; nurses, 82% ± 12%) compared to experts or nonexperts viewing the EEGs visually (neurologists, 90% ± 7%; students, 65% ± 20%).Our study confirms that individuals without EEG training can detect ongoing seizures or seizure-like rhythmic periodic patterns by listening to sonified EEG. Although sonification of EEG cannot replace the traditional approaches to EEG interpretation, it provides a meaningful triage tool for fast assessment of patients with suspected subclinical seizures.

  View details for PubMedID 29558565

• Diagnostic utility of eight-channel EEG for detecting generalized seizures Clinical Neurophysiology Practice Gururangan, K., Razavi, B., Parvizi, J. 2018; 3

  View details for DOI 10.1016/j.cnp.2018.03.001

• Detecting silent seizures by their sound Epilepsia Parvizi, J., Gururangan, K., Razavi, B., Chafe, C. 2018; 59 (4): 877-884

  Abstract

  The traditional approach to interpreting electroencephalograms (EEGs) requires physicians with formal training to visually assess the waveforms. This approach can be less practical in critical settings where a trained EEG specialist is not readily available to review the EEG and diagnose ongoing subclinical seizures, such as nonconvulsive status epilepticus.We have developed a novel method by which EEG data are converted to sound in real time by letting the underlying electrophysiological signal modulate a voice tone that is in the audible range. Here, we explored whether individuals without any prior EEG training could listen to 15-second sonified EEG and determine whether the EEG represents seizures or nonseizure conditions. We selected 84 EEG samples to represent seizures (n = 7), seizure-like activity (n = 25), or nonperiodic, nonrhythmic activity (normal or focal/generalized slowing, n = 52). EEGs from single channels in the left and right hemispheres were then converted to sound files. After a 4-minute training video, medical students (n = 34) and nurses (n = 30) were asked to designate each audio sample as "seizure" or "nonseizure." We then compared their performance with that of EEG-trained neurologists (n = 12) and medical students (n = 29) who also diagnosed the same EEGs on visual display.Nonexperts listening to single-channel sonified EEGs detected seizures with remarkable sensitivity (students, 98% ± 5%; nurses, 95% ± 14%) compared to experts or nonexperts reviewing the same EEGs on visual display (neurologists, 88% ± 11%; students, 76% ± 19%). If the EEGs contained seizures or seizure-like activity, nonexperts listening to sonified EEGs rated them as seizures with high specificity (students, 85% ± 9%; nurses, 82% ± 12%) compared to experts or nonexperts viewing the EEGs visually (neurologists, 90% ± 7%; students, 65% ± 20%).Our study confirms that individuals without EEG training can detect ongoing seizures or seizure-like rhythmic periodic patterns by listening to sonified EEG. Although sonification of EEG cannot replace the traditional approaches to EEG interpretation, it provides a meaningful triage tool for fast assessment of patients with suspected subclinical seizures.

  View details for DOI 10.1111/epi.14043

• Diagnostic utility of eight-channel EEG for detecting generalized or hemispheric seizures and rhythmic periodic patterns. Clinical neurophysiology practice Gururangan, K., Razavi, B., Parvizi, J. 2018; 3: 65–73

  Abstract

  To compare the diagnostic utility of electroencephalography (EEG) using reduced, 8-channel montage (rm-EEG) to full, 18-channel montage (fm-EEG) for detection of generalized or hemispheric seizures and rhythmic periodic patterns (RPPs) by neurologists with extensive EEG training, neurology residents with minimal EEG exposure, and medical students without EEG experience.We presented EEG samples in both fm-EEG (bipolar montage) and rm-EEG (lateral leads of bipolar montage) to 20 neurologists, 20 residents, and 42 medical students. Unanimous agreement of three senior epileptologists defined samples as seizures (n = 7), RPPs (n = 10), and normal or slowing (n = 20). Differences in median accuracy, sensitivity, and specificity were assessed using Wilcoxon signed-rank tests.Full and reduced EEG demonstrated similar accuracy when read by neurologists (fm-EEG: 95%, rm-EEG: 95%, p = 0.29), residents (fm-EEG: 80%, rm-EEG: 80%, p = 0.05), and students (fm-EEG: 60%, rm-EEG: 51%, p = 0.68). Moreover, neurologists' sensitivity for detecting seizure activity was comparable between fm-EEG (100%) and rm-EEG (98%) (p = 0.17). Furthermore, the specificity of rm-EEG for seizures and RPP (neurologists: 100%, residents: 90%, students: 86%) was significantly greater than that of fm-EEG (neurologists: 93%, p = 0.03; residents: 80%, p = 0.01; students: 69%, p < 0.001).The reduction of the number of EEG channels from 18 to 8 does not compromise neurologists' sensitivity for detecting seizures that are often a core reason for performing urgent EEG. It may also increase their specificity for detecting rhythmic and periodic patterns, and thereby providing important diagnostic information to guide patient's management.Our study is the first to document the utility of a reduced channel EEG above the hairline compared to full montage EEG in aiding medical staff with varying degrees of EEG training to detect generalized or hemispheric seizures.

  View details for PubMedID 30215011

  View details for PubMedCentralID PMC6133909

• EEG with Fewer Electrodes Is More Specific for Detecting Seizures and Seizure-Like Activity Gururangan, K., Razavi, B., Parvizi, J. WILEY. 2017: S158

  View details for Web of Science ID 000413198700326

• Utility of electroencephalography: Experience from a U.S. tertiary care medical center. Clinical neurophysiology Gururangan, K., Razavi, B., Parvizi, J. 2016; 127 (10): 3335-3340

  Abstract

  To investigate the utility of electroencephalography (EEG) for evaluation of patients with altered mental status (AMS).We retrospectively reviewed 200 continuous EEGs (cEEGs) obtained in ICU and non-ICU wards and 100 spot EEGs (sEEGs) obtained from the emergency department (ED) of a large tertiary medical center. Main outcomes were access time (from study request to hookup), and diagnostic yield (percentage of studies revealing significant abnormality).Access time, mean±SD (maximum), was 3.5±3.2 (20.8) hours in ICU, 4.8±5.0 (25.6) hours in non-ICU, and 2.7±3.6 (23.9) hours in ED. Access time was not significantly different for stat requests or EEGs with seizure activity. While the primary indication for EEG monitoring was to evaluate for seizures as the cause of AMS, only 8% of cEEGs and 1% of sEEGs revealed seizures. Epileptiform discharges were detected in 45% of ICU, 24% of non-ICU, and 9% of ED cases, while 2% of ICU, 15% of non-ICU, and 45% of ED cases were normal.Access to EEG is hampered by significant delays, and in emergency settings, the conventional EEG system detects seizures only in a minority of cases.Our findings underscore the inefficiencies of current EEG infrastructure for accessing diagnostically important information, as well as the need for more prospective data describing the relationship between EEG access time and EEG findings, clinical outcomes, and cost considerations.

  View details for DOI 10.1016/j.clinph.2016.08.013

  View details for PubMedID 27611442

• Dynamics of Quantitative EEG Changes During Cerebral Hypoperfusion Razavi, B., Meador, K. LIPPINCOTT WILLIAMS & WILKINS. 2016

  View details for Web of Science ID 000411328606016

• Quantitative (Spectral) Analysis of Continuous EEG for Prognostication in Post Cardiac Arrest Coma Wiley, S., Razavi, B., Hirsch, K., Meador, K. LIPPINCOTT WILLIAMS & WILKINS. 2016

  View details for Web of Science ID 000411328602100

• Quantitative (Spectral) Analysis of Continuous EEG for Prognostication in Post Cardiac Arrest Coma Wiley, S., Razavi, B., Hirsch, K., Meador, K. LIPPINCOTT WILLIAMS & WILKINS. 2016

  View details for Web of Science ID 000411328602190

• Dynamics of Quantitative EEG Changes During Cerebral Hypoperfusion Razavi, B., Meador, K. LIPPINCOTT WILLIAMS & WILKINS. 2016

  View details for Web of Science ID 000411279000024