Simulation-Based Assessments and Graduating Neurology Residents' Milestones: Status Epilepticus Milestones.

BACKGROUND: The American Board of Psychiatry and Neurology and the Accreditation Council for Graduate Medical Education (ACGME) developed Milestones that provide a framework for residents' assessment. However, Milestones do not provide a description for how programs should perform assessments. OBJECTIVES: We evaluated graduating residents' status epilepticus (SE) identification and management skills and how they correlate with ACGME Milestones reported for epilepsy and management/treatment by their program's clinical competency committee (CCC). METHODS: We performed a cohort study of graduating neurology residents from 3 academic medical centers in Chicago in 2018. We evaluated residents' skills identifying and managing SE using a simulation-based assessment (26-item checklist). Simulation-based assessment scores were compared to experience (number of SE cases each resident reported identifying and managing during residency), self-confidence in identifying and managing these cases, and their end of residency Milestones assigned by a CCC based on end-of-rotation evaluations. RESULTS: Sixteen of 21 (76%) eligible residents participated in the study. Average SE checklist score was 15.6 of 26 checklist items correct (60%, SD 12.2%). There were no significant correlations between resident checklist performance and experience or self-confidence. The average participant's level of Milestone for epilepsy and management/treatment was high at 4.3 of 5 (SD 0.4) and 4.4 of 5 (SD 0.4), respectively. There were no significant associations between checklist skills performance and level of Milestone assigned. CONCLUSIONS: Simulated SE skills performance of graduating neurology residents was poor. Our study suggests that end-of-rotation evaluations alone are inadequate for assigning Milestones for high-stakes clinical skills such as identification and management of SE.

Indications and yield of ambulatory EEG recordings.

To study the yield of prolonged ambulatory electroencephalogram (aEEG). A retrospective chart review of all patients who underwent aEEG studies between 2013 and 2017 was performed. Reasons for aEEG were classified into five categories: detection of interictal epileptiform discharges (IEDs), capturing clinical events, detection of unrecognized seizures, monitoring IEDs during treatment, and unclassifiable. Ambulatory EEG reports were reviewed to evaluate whether the study answered the clinical question. A total of 1,264 patients were included. Forty studies were excluded for incomplete data and 234 for being a repeat study. The average number of recording days was 1.57 ± 0.73. Based on initial clinical evaluation, patients carried the following presumptive diagnosis: 61% epilepsy, 11% single unprovoked or acute symptomatic seizure and 28% non-epileptic paroxysmal events (PEs). Overall, focal IEDs were seen in 16.1% of studies, generalized IEDs in 10.8%, focal seizures in 4.1%, and generalized seizures in 1.9%. The most frequent reason for ordering aEEG was to detect IEDs for diagnostic purposes (48.1%). For this indication, additional information was provided by the aEEG in 19.1% of cases (58.6% focal IEDs, 33.5% generalized IEDs, 7.9% seizures without IEDs). Ambulatory EEG was ordered with the intent to capture and characterize clinical events in 18.9%, mostly in patients who reported daily or weekly events. In these, aEEG captured either epileptic seizures or PEs in 102 (42.7%) of the studies (83.3% PEs, 16.7% epileptic seizures). Ambulatory EEG was ordered to evaluate unrecognized seizures in 17.8% of patients, and electrographic seizures were identified in 13.3% of these studies. The yield of aEEG varies based on the indication for the study. Ambulatory EEG can be a useful tool for recording IEDs in the outpatient setting and in a select group of patients to capture clinical events or unrecognized seizures.

Use of a simulation-based mastery learning curriculum for neurology residents to improve the identification and management of status epilepticus.

BACKGROUND: Appropriate and timely treatment of status epilepticus (SE) decreases morbidity and mortality. Therefore, skill-based training in the identification and management of SE is crucial for clinicians. OBJECTIVE: The objective of the study was to develop and evaluate the impact of a simulation-based mastery learning (SBML) curriculum to train neurology residents on the identification and management of SE. METHODS: We used pretest-posttest design with a retention test on SE skills for this study. Neurology residents in the second postgraduate year (PGY-2) were eligible to participate in the SE SBML curriculum. Learners completed a baseline-simulated SE skills assessment (pretest) using a 26-item dichotomous skills checklist. Next, they participated in a didactic session about the identification and management of SE, followed by deliberate skills practice. Subsequently, participants completed another skills assessment (posttest) using the same 26-item checklist. All participants were required to meet or exceed a minimum passing standard (MPS) determined by a panel of 14 SE experts using the Mastery Angoff standard setting method. After meeting the MPS at posttest, participants were reassessed during an unannounced in situ simulation session on the medical wards. We compared pretest with posttest simulated SE skills performance and posttest with reassessment in situ performance. RESULTS: The MPS was set at 88% (23/26) checklist items correct. Sixteen neurology residents participated in the intervention. Participant performance improved from a median of 44.23% (Interquartile range (IQR): 34.62-55.77) at pretest to 94.23% (IQR: 92.13-100) at the posttest after SBML (p < .001). There was no significant difference in scores between the posttest and in situ test up to 8 months later (94.23%; IQR: 92.31-100 vs. 92.31%; IQR: 88.46-96.15; p = .13). CONCLUSIONS: Our SBML curriculum significantly improved residents' SE identification and management skills that were largely retained during an unannounced simulated encounter in the hospital setting.

Automated seizure detection accuracy for ambulatory EEG recordings.

OBJECTIVE: To investigate the accuracy of preselected software automatic seizure files to detect at least one seizure per study in prolonged ambulatory EEG recording. METHODS: All the prolonged ambulatory EEG recordings (>24 hours) read at the Northwestern Memorial Hospital from January 2013 to October 2017 were included. We selected only the first study of each patient. We reviewed the studies entirely, and processed the recordings through 1 of 3 different detection software that are commercially available (Persyst 11, Persyst 12, and Gotman TM Event Detection). The proportion of patients with at least one electrographic seizure (≥10 seconds) correctly identified by a seizure detector was calculated. Finally, we evaluated whether the type of seizure (focal vs generalized) may affect the chances of being automatically detected. RESULTS: We read 1,478 ambulatory EEG studies entirely (2,323 days of EEG recording; average 1.6 d/study). From the first study of each patient (1,257 studies), we found electrographic seizures in 70 (5.6%) studies. In 37 of 70 patients (53%), the automatic detectors correctly identified at least one seizure. Detections happened slightly more frequently in generalized seizures (14/20, 70%) compared to focal seizures (23/50, 46%) (p = 0.06). CONCLUSION: Seizures were found in 5.6% of the studies. Automatic seizure detectors identified at least one electrographic seizure in only 53% of the studies. They performed slightly better detecting generalized than focal seizures. Therefore, the review of only automatically selected segments may be of decreased value to identify seizures, in particular when focal seizures are suspected.

Clinical classification of post anoxic myoclonic status.

INTRODUCTION: Despite decades of research into the prognostic significance of post anoxic myoclonic status (MS), no consistent definition has been used to describe its clinical appearance. We set out to characterize the clinical features of MS and hypothesized that there are distinct clinical subtypes that may have prognostic implications. METHODS: Video EEG reports from 2008 to 2016 were searched to identify adult patients with post anoxic MS defined as persistent myoclonus for >30min beginning within 3days of cardiac arrest in a comatose patient. Forty-three patients met inclusion and exclusion criteria. To generate definitions of the clinical features of MS, we reviewed videos of 23 cases and characterized 3 distinct clinical semiologies. An additional 20 cases were independently reviewed and categorized by 3 raters to evaluate inter-rater agreement (IRA). All 43 patients were assigned to a group based on consensus review for the first 23 patients and majority agreement for IRA patients. We also examined the relationship between semiology and outcome. RESULTS: Three distinct clinical semiologies of MS were identified: Type 1: distal, asynchronous, variable; type 2: axial or axial and distal, asynchronous, variable; and type 3: axial, synchronous, stereotyped. For IRA, Gwet's kappa was 0.64 indicating substantial agreement. Two of 3 type 1 patients (66.6%) and 7.4% of type 2 followed commands whereas none of type 3 followed commands (p=0.03). CONCLUSION: We defined and validated a classification system of post anoxic MS based on clinical semiology. This classification may be a useful bedside prognostication tool.