Centerscope

Centerscope, formerly Scope, was published by the Boston University Medical Center "to communicate the concern of the Medical Center for the development and maintenance of improved health care in contemporary society.

Front and Center

Newsletter providing "a lighter, human interest side of the news" from the Boston University Medical Campus

Hospital EEG Capability and Associations With Interhospital Transfer in Status Epilepticus.

BACKGROUND AND OBJECTIVES: EEG is widely recommended for status epilepticus (SE) management. However, EEG access and use across the United States is poorly characterized. We aimed to evaluate changes in inpatient EEG access over time and whether availability of EEG is associated with interhospital transfers for patients hospitalized with SE. METHODS: We performed a cross-sectional study using data available in the National Inpatient Sample data set from 2012 to 2018. We identified hospitals that used continuous or routine EEG during at least 1 seizure-related hospitalization in a given year using ICD-9 and ICD-10 procedure codes and defined these hospitals as EEG capable. We examined annual change in the proportion of hospitals that were EEG capable during the study period, compared characteristics of hospitals that were EEG capable with those that were not, and fit multivariable logistic regression models to determine whether hospital EEG capability was associated with likelihood of interhospital transfer. RESULTS: Among 4,550 hospitals in 2018, 1,241 (27.3%) were EEG capable. Of these, 1,188 hospitals (95.7%) were in urban settings. From 2012 to 2018, the proportion of hospitals that were EEG capable increased in urban settings (30.5%-41.1%, Mann-Kendall [M-K] test p < 0.001) and decreased in rural settings (4.0%-3.2%, M-K p = 0.026). Among 130,580 patients hospitalized with SE, 80,725 (61.8%) presented directly to an EEG-capable hospital. However, EEG use during hospitalization varied from 8% to 98%. Initial admission to a hospital without EEG capability was associated with 22% increased likelihood of interhospital transfer (adjusted RR 1.22, [95% CI, 1.09-1.37]; p < 0.01). Among those hospitalized at an EEG-capable hospital, patients admitted to hospitals in the lowest quintile of EEG volume were more than 2 times more likely to undergo interhospital transfer (adjusted RR 2.22, [95% CI 1.65-2.93]; p < 0.001). DISCUSSION: A minority of hospitals are EEG capable yet care for most patients with SE. Inpatient EEG use, however, varies widely among EEG-capable hospitals, and lack of inpatient EEG access is associated with interhospital transfer. Given the high incidence and cost of SE, there is a need to better understand the importance and use of EEG in this patient population to further organize inpatient epilepsy systems of care to optimize outcomes

Choice of reading comprehension test influences the outcomes of genetic analyses

Does the choice of test for assessing reading comprehension influence the outcome of genetic analyses? A twin design compared two types of reading comprehension tests classified as primarily associated with word decoding (RC-D) or listening comprehension (RC-LC). For both types of tests, the overall genetic influence is high and nearly identical. However, the tests differed significantly in how they covary with the genes associated with decoding and listening comprehension. Although Cholesky decomposition showed that both types of comprehension tests shared significant genetic influence with both decoding and listening comprehension, RC-D tests shared most genetic variance with decoding, and RC-LC tests shared most with listening comprehension. Thus, different tests used to measure the same construct may manifest very different patterns of genetic covariation. These results suggest that the apparent discrepancies among the findings of previous twin studies of reading comprehension could be due at least in part to test differences. © 2011 Society for the Scientific Study of Reading

Polypharmacy in patients with epilepsy: A nationally representative cross-sectional study.

OBJECTIVE The objective of the study was to characterize the prevalence of polypharmacy and central nervous system (CNS)-acting medications in patients with epilepsy, and particular types of medications. METHODS This was a retrospective cross-sectional study using data from the nationally representative National Health and Nutrition Examination Survey (NHANES). We included patients who reported taking at least one prescription medication in order to treat seizures or epilepsy during NHANES survey years 2013-2016. We assessed the number and types of drugs and predictors of total number of medications using a negative binomial regression. We then assessed prevalence of polypharmacy (≥5 medications), CNS polypharmacy (≥3 CNS-acting medications) and additional CNS-acting medications, and drugs that lower the seizure threshold (i.e., bupropion and tramadol), and extrapolated prevalence to estimated affected US population. RESULTS The NHANES contained 20,146 participants, of whom 135 reported taking ≥1 antiseizure medication (ASM) for seizures or epilepsy representing 2,399,520 US citizens using NHANES's sampling frame. Patients reported taking a mean 5.3 (95% confidence interval (CI): 4.3-6.3) prescription medications. Adjusting for race, sex, and uninsurance, both age and number of chronic conditions predicted increased number of medications (incident rate ratio (IRR) per decade: 1.16, 95% CI: 1.04-1.28; IRR per chronic condition: 1.19, 95% CI: 1.11-1.27). Polypharmacy was reported by 47% (95% CI: 38%-57%) of patients, CNS polypharmacy by 34% (23%-47%), benzodiazepine use by 21% (14%-30%), opioid use by 16% (11%-24%), benzodiazepine plus opioid use by 6% (3%-14%), and 6% (2%-15%) reported a drug that lowers the seizure threshold. Twelve percent (7%-20%) took an opioid with either a benzodiazepine or gabapentinoid. CONCLUSIONS Polypharmacy is common in patients with epilepsy. Patients taking ASMs frequently reported also taking other CNS-acting medications (i.e., opioids, benzodiazepines, seizure threshold-lowering medications), and medication combinations with black box warnings. Central nervous system polypharmacy poses health risks. Future research is needed to explore drivers of polypharmacy and strategies to help mitigate potentially harmful prescription use in this high-risk population

Institutional Factors Contribute to Variation in Intubation Rates in Status Epilepticus

BackgroundTo explore intubation rates among patients with status epilepticus (SE) and the degree of institutional variation.MethodsSerial cross-sectional study of SE-related hospitalizations from 2004 to 2013 using data from the National Inpatient Sample. The primary outcome was intubation of patients with SE. Multivariable models identified predictors of intubation, institutional variation in intubation rates, and the proportion of variance attributable to individual hospitals. This analysis was repeated using data from 5 states in the State Inpatient Databases (SID).ResultsThere were 119 337 SE hospitalizations. The overall intubation rate was 32.7% (95% confidence interval [CI]: 32.2%-33.3%). There was marked variation in estimated intubation rates, ranging from 2% to 80% in the lowest and highest quintile after adjustment. There was somewhat less variability in the SID cohort where quintiles ranged from 10% to 54%. Those undergoing intubation were more often men and presenting with stroke, intracerebral hemorrhage, central nervous system infection, hyponatremia, and alcohol withdrawal. Urban location (odds ratio [OR]: 3.8, 95% CI: 2.7-5.5) and hospitalization at a teaching institution (OR: 3.9, 95% CI: 1.2-12.6) were even stronger predictors of intubation after adjustment for clinical factors. A regression including both patient- and hospital-level variables to predict intubation also performed better than a regression including patient factors alone (C statistic 0.81 vs 0.59, respectively).ConclusionsThere is considerable institutional variation in intubation rates for SE independent of patient characteristics suggesting that decisions around intubation rest heavily on where one is hospitalized. Further work is needed to clarify how this variation influences outcomes

Hospital admission and readmission among homeless patients with neurologic disease.

ObjectiveTo characterize the most common neurologic diagnoses leading to hospitalization for homeless compared to housed individuals and to assess whether homelessness is an independent risk factor for 30-day readmission after an admission for a neurologic illness.MethodsWe performed a retrospective serial cross-sectional study using data from the Healthcare Cost and Utilization Project California State Inpatient Database from 2006 to 2011. Adult patients with a primary neurologic discharge diagnosis were included. The primary outcome was 30-day readmission. We used multilevel logistic regression to examine the association between homelessness and readmission after adjustment for patient factors.ResultsWe identified 1,082,347 patients with a neurologic primary diagnosis. The rate of homelessness was 0.37%. The most common indications for hospitalization among homeless patients were seizure and traumatic brain injury, both of which were more common in the homeless compared to housed population (19.3% vs 8.1% and 31.9% vs 9.2%, respectively, p < 0.001). A multilevel mixed-effects model controlling for patient age, sex, race, insurance type, comorbid conditions, and clustering on the hospital level found that homelessness was associated with increased 30-day readmission (odds ratio 1.5, 95% confidence interval 1.4-1.6, p < 0.001). This association persisted after this analysis was repeated within specific diagnoses (patients with epilepsy, trauma, encephalopathy, and neuromuscular disease).ConclusionThe most common neurologic reasons for admission among homeless patients are seizure and traumatic brain injury; these patients are at high risk for readmission. Future interventions should target the drivers of readmissions in this vulnerable population

Yield of Emergent CT in Patients With Epilepsy Presenting With a Seizure

BackgroundStudies of emergent neuroimaging in the management of patients presenting with a breakthrough seizure are lacking. We sought to determine how often emergent computed tomography (CT) scans are obtained in patients with known epilepsy presenting with a seizure and how often acute abnormalities are found.MethodsThis multicenter retrospective cohort study was performed in the emergency department at 2 academic medical centers. The primary outcomes were percentage of visits where a CT scan was obtained, whether CT findings represented acute abnormalities, and whether these findings changed acute management.ResultsOf the 396 visits included, CT scans were obtained in 39%, and 8% of these scans demonstrated acute abnormalities. Patients who were older, had status epilepticus, a brain tumor, head trauma, or an abnormal examination were all significantly more likely to undergo acute neuroimaging (P < .05). In the multivariable model, only history of brain tumor (odds ratio [OR] 5.88, 95% confidence interval [CI], 1.33-26.1) and head trauma as a result of seizure (OR 3.92, 95% CI, 1.01-15.2) reached statistical significance in predicting an acutely abnormal scan. The likelihood of an acute imaging abnormality in visits for patients without a history of brain tumor or head trauma as a result of the seizure was 2.7% (2 visits). Both of these patients had abnormal neurological examinations.ConclusionObtaining an emergent CT scan for patients with epilepsy presenting with a seizure may be avoidable in most cases, but might be indicated for patients with a history of brain tumor or head trauma as a result of seizure

Factors associated with 30-day readmission for patients hospitalized for seizures.

BackgroundWe sought to determine the cumulative incidence of readmissions after a seizure-related hospitalization and identify risk factors and readmission diagnoses.MethodsWe performed a retrospective cohort study of adult patients hospitalized with a primary discharge diagnosis of seizure (International Classification of Diseases, Ninth Edition, Clinical Modification codes 345.xx and 780.3x) using the State Inpatient Databases across 11 states from 2009 to 2012. Hospital and community characteristics were obtained from the American Hospital Association and Robert Wood Johnson Foundation. We performed logistic regressions to explore effects of patient, hospital, and community factors on readmissions within 30 days of discharge.ResultsOf 98,712 patients, 13,929 (14%) were readmitted within 30 days. Reasons for readmission included epilepsy/convulsions (30% of readmitted patients), mood disorders (5%), schizophrenia (4%), and septicemia (4%). The strongest predictors of readmission were diagnoses of CNS tumor (odds ratio [OR] 2.1, 95% confidence interval [CI] 1.9-2.4) or psychosis (OR 1.8, 95% CI 1.7-1.8), urgent index admission (OR 2.0, 95% CI 1.8-2.2), transfer to nonacute facilities (OR 1.7, 95% CI 1.6-1.8), long length of stay (OR 1.7, 95% CI 1.6-1.8), and for-profit hospitals (OR 1.7, 95% CI 1.6-1.8). Our main model's c-statistic was 0.66. Predictors of readmission for status epilepticus included index admission for status epilepticus (OR 3.5, 95% CI 2.6-4.7), low hospital epilepsy volume (OR 0.4, 95% CI 0.3-0.7), and rural hospitals (OR 4.8, 95% CI 2.1-10.9).ConclusionReadmission is common after hospitalization for seizures. Prevention strategies should focus on recurrent seizures, the most common readmission diagnosis. Many factors were associated with readmission, although readmissions remain challenging to predict