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Abstract Psychological assessment measures are frequently used to evaluate patients in epilepsy monitoring units. One goal of that assessment is to contribute information that may help with differential diagnosis between epilepsy and psychogenic nonepileptic seizures (PNES). The Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) is one such measure. Del Bene et al. (2017) recently published an analysis that was the first to compare MMPI-2-RF scale elevations between diagnostic groups stratified by sex. The purpose of the present study was to replicate that analysis in a larger sample. Similar to previous work, we found that both men and women with PNES were more likely than men and women with epilepsy to report high levels of somatic complaints (2 to 5 times greater odds of somatic symptom reporting) and a variety of types of complaints. Mood disturbance scales were not significantly elevated in our PNES sample. Results contribute to the small body of research on sex differences in patients with PNES and suggest that somatization is a key characterization across sexes. [ABSTRACT FROM AUTHOR]

Background The diagnosis of epilepsy is at times elusive for both neurologists and nonneurologists, resulting in delays in diagnosis and therapy. The development of screening methods has been identified as a priority in response to this diagnostic and therapeutic gap. EpiFinder is a novel clinical decision support tool designed to enhance the process of information gathering and integration of patient/proxy respondent data. It is designed specifically to take key terms from a patient's history and incorporate them into a heuristic algorithm that dynamically produces differential diagnoses of epilepsy syndromes. Objective The objective of this study was to test the usability and diagnostic accuracy of the clinical decision support application EpiFinder in an adult population. Methods Fifty-seven patients were prospectively identified upon admission to the Epilepsy Monitoring Unit (EMU) for episode classification from January through June of 2017. Based on semiologic input, the application generates a list of epilepsy syndromes. The EpiFinder-generated diagnosis for each subject was compared to the final diagnosis obtained via continuous video electroencephalogram (cVEEG) monitoring. Results Fifty-three patients had habitual events recorded during their EMU stay. A diagnosis of epilepsy was confirmed (with cVEEG monitoring) in 26 patients while 27 patients were found to have a diagnosis other than epilepsy. The algorithm appropriately predicted differentiation between the presence of an epilepsy syndrome and an alternative diagnosis with 86.8% (46/53 participants) accuracy. EpiFinder correctly identified the presence of epilepsy with a sensitivity of 86.4% (95% confidence interval [CI]: 65.0–97.1) and specificity of 85.1% (95% CI: 70.2–96.4). Conclusion The initial testing of the EpiFinder algorithm suggests possible utility in differentiating between an epilepsy syndrome and an alternative diagnosis in adult patients. [ABSTRACT FROM AUTHOR]

Abstract: Eyewitnesses frequently perceive seizures as life threatening. If an event occurs on the hospital premises, a “code blue” can be called which consumes considerable resources. The purpose of this study was to determine the frequency and characteristics of code blue calls for seizures and seizure mimickers. A retrospective review of a code blue log from 2001 through 2008 identified 50 seizure-like events, representing 5.3% of all codes. Twenty-eight (54%) occurred in inpatients; the other 22 (44%) events involved visitors or employees on the hospital premises. Eighty-six percent of the events were epileptic seizures. Seizure mimickers, particularly psychogenic nonepileptic seizures, were more common in the nonhospitalized group. Only five (17.9%) inpatients had a known diagnosis of epilepsy, compared with 17 (77.3%) of the nonhospitalized patients. This retrospective survey provides insights into how code blues are called on hospitalized versus nonhospitalized patients for seizure-like events. [Copyright &y& Elsevier]

Falls are one of the most common adverse events occurring in the epilepsy monitoring unit (EMU) and can result in significant injury. Protocols and procedures to reduce falls vary significantly between institutions as it is not yet known what interventions are effective in the EMU setting. This study retrospectively examined the frequency of falls and the impact of serial changes in fall prevention strategies utilized in the EMU between 2001 and 2014 at a single institution. Overall fall rate was 2.81 per 1000 patient days and varied annually from 0 to 9.02 per 1000 patient days. Both seizures and psychogenic nonepileptic events occurring in the bathroom were more likely to result in falls compared with events occurring elsewhere in the room. With initiation of increased patient education, hourly nurse rounding, nocturnal bed alarms, having two persons assisting for high fall risk patients when out of bed, and immediate postfall team review between 2001 and 2013, there was a trend of decreasing fall frequency; however, no specific intervention could be identified as having a particular high impact. In late 2013, a ceiling lift system extending into the bathroom was put in place for use in all EMU patients when out of bed. In the subsequent 15 months, there have been zero falls. The results reinforce both the need for diligent safety standards to prevent falls in the EMU as well as the challenges in identifying the most effective practices to achieve this goal. [ABSTRACT FROM AUTHOR]

Patients with recurrent, stereotyped neurological events of unclear etiology often warrant admission for continuous video-EEG monitoring in an epilepsy monitoring unit (EMU) for diagnosis. Epilepsy monitoring unit admission duration has been reported to range from days to weeks. To date, there are limited data on the average duration of admission for patients admitted to the EMU for spell classification. Many EMUs are forced to limit the overall duration of admission for numerous reasons including limited resources. It is unclear if a time-limited EMU stay reduces the event capture rate and, therefore, diagnostic yield of event classification admissions. The goal of this study was to determine how a time-limited length of stay strategy impacted event capture in patients admitted for spell classification. A retrospective chart review was performed at two comparable adult epilepsy monitoring units, Mayo Clinic Hospital (MCH) in Phoenix, Arizona, and Banner Good Samaritan Medical Center (BGSMC) in Phoenix, Arizona. Banner Good Samaritan Medical Center is only staffed Monday through Friday, thereby limiting the total possible duration of admission to five days. The goal was to determine if the rate of event capture differed between two institutions employing a time-limited EMU admission (BGSMC) when compared with the nonlimited admission (MCH). A total of 300 patient admissions at MCH and 260 patient admissions at BGSMC were reviewed over a comparable time period. The event capture rates at MCH and BGSMC were 74% and 72%, respectively. There was a greater percentage of patients with nonepileptic events (NEEs) at MCH than at BGSMC (62.7% vs. 47.3%). The mean duration until first event was 31 h at MCH and 38 h at BGSMC. The mean length of stay was greater at MCH (4.5 days) when compared with BGSMC (3.3 days). The overall diagnostic yield of a time-limited EMU admission was similar to that of a nonlimited admission for the purpose of spell classification. There was a statistically significant difference when comparing the time until first event at both institutions; however, this still fell within the 5-day duration that the time-restricted admission was limited to. These results may be important in optimizing an EMU practice in patients requiring admission for spell classification. [ABSTRACT FROM AUTHOR]

Abstract: The two most common personality measures used in evaluation of patients on epilepsy monitoring units (EMUs) are the Personality Assessment Inventory (PAI) and the Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Both have been evaluated separately for their ability to distinguish patients with epilepsy from patients with psychogenic events, but they have never been compared directly. The primary aim of this study was to provide comparison data in an EMU population between the PAI, MMPI-2, and the MMPI-2-RF (MMPI-2 Restructured Form). Results show that the PAI Somatic Complaints (SOM) scale and the Conversion subscale (SOM-C), with classification rates of 79%, outperform other indicators from the PAI and indicators from the MMPI-2 and the MMPI-2-RF. Given its other strengths combined with better diagnostic validity performance, the PAI may be the better personality assessment measure for use in distinguishing patients with epilepsy from those with psychogenic seizures in the EMU. [Copyright &y& Elsevier]

Abstract: The Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF) is a restructuring of the MMPI-2 that has improved the psychometric characteristics of the test. The primary aim of this study was to provide diagnostic utility data on the MMPI-2-RF in an epilepsy monitoring unit population (N =429). Mean comparisons revealed group differences on Validity Scales Fs and FBS-r; Restructured Clinical Scales RC1 and RC3; and Somatic Scales MLS, GIC, HPC, and NUC. Diagnostic utility data are provided for those scales with the largest effect sizes: RC1, FBS-r, and NUC. On RC1, sensitivity was 76% and specificity was 60%, similar to values found when applying published decision rules to the MMPI-2. RC1 explains unique variance in diagnosis beyond that explained by demographic or medical history risk factors. We provide likelihood ratios for scores on RC1, FBS-r, and NUC that can be used by the clinician to calculate posttest odds and probability of nonepileptic seizures using the base rate of nonepileptic seizures in his/her population. [Copyright &y& Elsevier]

Abstract: Epilepsy monitoring unit (EMU) admissions during 2007–2009 at Mayo Clinic Hospital Arizona were reviewed. Of the 106 indeterminate admissions, 13 (12%) went on to have a second admission. During the second admission, 8 (62%) were diagnosed. Five patients went on to have a third or fourth admission, with none of them receiving a diagnosis. Nineteen (18%) patients had ambulatory EEG monitoring after an indeterminate admission, with only one (5%) receiving a diagnosis after ambulatory EEG monitoring. Even in patients who were initially indeterminate, medication management changed 37% of the time. Admission to the EMU was helpful for spell classification, with 80% of the patients receiving a diagnosis after the first admission. Based on this study, a second admission should be considered if no diagnosis is reached after the first admission. If no diagnosis is made after the second EMU admission, subsequent admissions are unlikely to produce a definitive diagnosis. [Copyright &y& Elsevier]