Predictors of follow-up care for critically-ill patients with seizures and epileptiform abnormalities on EEG monitoring

OBJECTIVE

Post-hospitalization follow-up visits are crucial for preventing long-term complications. Patients with electrographic epileptiform abnormalities (EA) including seizures and periodic and rhythmic patterns are especially in need of follow-up for long-term seizure risk stratification and medication management. We sought to identify predictors of follow-up.

METHODS

This is a retrospective cohort study of all patients (age ≥ 18 years) admitted to intensive care units that underwent continuous EEG (cEEG) monitoring at a single center between 01/2016-12/2019. Patients with EAs were included. Clinical and demographic variables were recorded. Follow-up status was determined using visit records 6-month post discharge, and visits were stratified as outpatient follow-up, neurology follow-up, and inpatient readmission. Lasso feature selection analysis was performed.

RESULTS

723 patients (53 % female, mean (std) age of 62.3 (16.4) years) were identified from cEEG records with 575 (79 %) surviving to discharge. Of those discharged, 450 (78 %) had outpatient follow-up, 316 (55 %) had a neurology follow-up, and 288 (50 %) were readmitted during the 6-month period. Discharge on antiseizure medications (ASM), younger age, admission to neurosurgery, and proximity to the hospital were predictors of neurology follow-up visits. Discharge on ASMs, along with longer length of stay, younger age, emergency admissions, and higher illness severity were predictors of readmission.

SIGNIFICANCE

ASMs at discharge, demographics (age, address), hospital care teams, and illness severity determine probability of follow-up. Parameters identified in this study may help healthcare systems develop interventions to improve care transitions for critically-ill patients with seizures and other EA.

Real-World Continuous EEG Utilization and Outcomes in Hospitalized Patients With Acute Cerebrovascular Diseases

PURPOSE

Continuous electroencephalography (cEEG) is recommended for hospitalized patients with cerebrovascular diseases and suspected seizures or unexplained neurologic decline. We sought to (1) identify areas of practice variation in cEEG utilization, (2) determine predictors of cEEG utilization, (3) evaluate whether cEEG utilization is associated with outcomes in patients with cerebrovascular diseases.

METHODS

This cohort study of the Premier Healthcare Database (2014-2020), included hospitalized patients age >18 years with cerebrovascular diseases (identified by ICD codes). Continuous electroencephalography was identified by International Classification of Diseases (ICD)/Current Procedural Terminology (CPT) codes. Multivariable lasso logistic regression was used to identify predictors of cEEG utilization and in-hospital mortality. Propensity score-matched analysis was performed to determine the relation between cEEG use and mortality.

RESULTS

1,179,471 admissions were included; 16,777 (1.4%) underwent cEEG. Total number of cEEGs increased by 364% over 5 years (average 32%/year). On multivariable analysis, top five predictors of cEEG use included seizure diagnosis, hospitals with >500 beds, regions Northeast and South, and anesthetic use. Top predictors of mortality included use of mechanical ventilation, vasopressors, anesthetics, antiseizure medications, and age. Propensity analysis showed that cEEG was associated with lower in-hospital mortality (Average Treatment Effect -0.015 [95% confidence interval -0.028 to -0.003], Odds ratio 0.746 [95% confidence interval, 0.618-0.900]).

CONCLUSIONS

There has been a national increase in cEEG utilization for hospitalized patients with cerebrovascular diseases, with practice variation. cEEG utilization was associated with lower in-hospital mortality. Larger comparative studies of cEEG-guided treatments are indicated to inform best practices, guide policy changes for increased access, and create guidelines on triaging and transferring patients to centers with cEEG capability.

Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs

BACKGROUND AND OBJECTIVES

The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as "ictal-interictal-injury continuum" (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC.

METHODS

This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as "seizure (SZ)," "lateralized periodic discharges (LPDs)," "generalized periodic discharges (GPDs)," "lateralized rhythmic delta activity (LRDA)," "generalized rhythmic delta activity (GRDA)," or "other." EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability.

RESULTS

Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained ([Formula: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Formula: see text]: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds.

DISCUSSION

Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.

Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation

BACKGROUND AND OBJECTIVES

Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns.

METHODS

We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes.

RESULTS

SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively.

DISCUSSION

SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

Atmospheric carcinogens in Rio de Janeiro during the summer of 1998/99: benzo[a]pyrene and benzene

Rio de Janeiro, the second largest city in Brazil, is affected by severe pollution episodes and presents a high respiratory cancer incidence in comparison with the rest of the country. To monitor atmospheric pollution during the summer of 1998/1999 and to estimate the impact of organic pollution on public health, we determined the levels of two carcinogenic organic chemicals, benzo[a]pyrene and benzene, in four distinct sites throughout the city. A review of the levels recorded in other urban areas worldwide during the last ten years indicates that the benzo[a]pyrene (< or = 0.70 ng/m3) and benzene (< or = 11 micrograms/m3) concentrations found in Rio are relatively low. The highest levels were generally recorded in developing Asian countries, whereas the lowest values were found in North America. Unlike urban areas in temperate zones, pollution derived from domestic heating is minor in Rio de Janeiro, where most of the benzo[a]pyrene and benzene pollution originates from vehicular traffic. The quite distinct fuels used in light-duty vehicles in Brazil, combined with strong light incidence and increased rainfall during the summer, also contribute to diminish the levels of such pollutants.

Sedimentary 4-desmethyl sterols and n-alkanols in an eutrophic urban estuary, Capibaribe River, Brazil

Sterols, n-alkanols, organic carbon (OC), C/N ratios and carbon isotope data (delta 13C) were investigated in sediments of the urban Capibaribe River estuary, NE Brazil, in order to assess allochthonous and autochthonous sources of organic matter (OM). Sedimentary OC values are high, but C/N ratios and delta 13C data generally fall within the range of values reported in other riverine systems, and suggest mixed inputs from aquatic and terrestrial matter. Mean values for total 4-desmethyl sterols and high molecular weight (HMW) n-alkanols are 11.0 micrograms/g and 2.8 micrograms/g, respectively. Sterols are found at highest levels in areas of enhanced urban outfalls. They can be related to major planktonic species growing in riverine waters. Stanol/stenol ratios suggest a high degree of alteration of the autochthonous OM as a result of elevated temperatures and microbiological proliferation. Even though sterols suggest the importance of autochthonous inputs to the river, HMW n-alkanols indicate major terrigenous accumulation at the mouth and 10 km upriver. Coprostanol and epicoprostanol levels are comparable to other sewage contaminated hydrosystems, but not as high as expected given the importance of sewage outfalls and low riverine water discharge. However, high (coprostanol)/(coprostanol + cholestanol) ratio values indicate that fecal contamination is significant.