Prediction Tools for Psychiatric Adverse Effects After Levetiracetam Prescription.

Importance: Levetiracetam is a commonly used antiepileptic drug, yet psychiatric adverse effects are common and may lead to treatment discontinuation. Objective: To derive prediction models to estimate the risk of psychiatric adverse effects from levetiracetam use. Design, Setting, and Participants: Retrospective open cohort study. All patients meeting the case definition for epilepsy after the Acceptable Mortality Reporting date in The Health Improvement Network (THIN) database based in the United Kingdom (inclusive January 1, 2000, to May 31, 2012) who received a first-ever prescription for levetiracetam were included. Of 11 194 182 patients registered in THIN, this study identified 7400 presumed incident cases (66.1 cases per 100 000 persons) over a maximum of 12 years' follow-up. The index date was when patients received their first prescription code for levetiracetam, and follow-up lasted 2 years or until an event, loss to follow-up, or censoring. The analyses were performed on April 22, 2018. Exposure: A presumed first-ever prescription for levetiracetam. Main Outcomes and Measures: The outcome of interest was a Read code for any psychiatric sign, symptom, or disorder as reached through consensus by 2 authors. This study used regression techniques to derive 2 prediction models, one for the overall population and one for those without a history of a psychiatric sign, symptom, or disorder during the study period. Results: Among 1173 patients with epilepsy receiving levetiracetam, the overall median age was 39 (interquartile range, 25-56) years, and 590 (50.3%) were female. A total of 14.1% (165 of 1173) experienced a psychiatric symptom or disorder within 2 years of index prescription. The odds of reporting a psychiatric symptom were significantly elevated for women (odds ratio [OR], 1.41; 95% CI, 0.99-2.01; P = .05) and those with a preexposure history of higher social deprivation (OR, 1.15; 95% CI, 1.01-1.31; P = .03), depression (OR, 2.20; 95% CI, 1.49-3.24; P < .001), anxiety (OR, 1.74; 95% CI, 1.11-2.72; P = .02), or recreational drug use (OR, 2.02; 95% CI, 1.20-3.37; P = .008). The model performed well after stratified k = 5-fold cross-validation (area under the curve [AUC], 0.68; 95% CI, 0.58-0.79). There was a gradient in risk, with probabilities increasing from 8% for 0 risk factors to 11% to 17% for 1, 17% to 31% for 2, 30% to 42% for 3, and 49% when all risk factors were present. For those free of a preexposure psychiatric code, a second model performed comparably well after k = 5-fold cross-validation (AUC, 0.72; 95% CI, 0.54-0.90). Specificity was maximized using threshold cutoffs of 0.10 (full model) and 0.14 (second model); a score below these thresholds indicates safety of prescription. Conclusions and Relevance: This study derived 2 simple models that predict the risk of a psychiatric adverse effect from levetiracetam. These algorithms can be used to guide prescription in clinical practice.

Quality indicators for the adult epilepsy monitoring unit.

OBJECTIVE: Quality and safety in epilepsy monitoring units (EMUs) are of great importance because patients' seizures are induced rather than prevented in this hospital setting. However, the measurement and evaluation of quality and safety in EMUs are heterogeneous, as are practices and processes of care. To improve the measurement of quality and safety in EMUs, we sought to develop evidence-based and consensus-driven quality indicators, adhering to previously described methodologic standards. METHODS: Candidate quality indicators were identified using a recent systematic review on quality and safety indicators in EMUs. These were supplemented by expert opinion to identify other indicators that had not been reported previously. The candidate quality indicators were then evaluated using a modified Delphi technique among a multidisciplinary EMU quality improvement team. Candidate indicators identified as important and feasible through the Delphi technique were then developed into quality metrics. RESULTS: Thirty-four candidate indicators were abstracted from 135 studies included in the earlier systematic review, and two additional candidate indicators were suggested through consensus from experts. Consensus was reached after two modified Delphi rounds for 25 quality indicators identified as important. These 25 indicators were then developed into quality metrics using a standardized data collection form and were deployed in an online database for systematic data capture and further analyses. SIGNIFICANCE: These quality indicators have the potential to improve the reporting of quality and safety in EMUs through standardized measurement and evaluation of the quality and safety of care. The ultimate goal is improved patient care and clinical outcomes through safer and better care for people with epilepsy in the EMU.

Quality and safety in adult epilepsy monitoring units: A systematic review and meta-analysis.

OBJECTIVE: The epilepsy monitoring unit (EMU) is a valuable resource for optimizing management of persons with epilepsy, but may place patients at risk for adverse events due to withdrawal of treatment and induction of symptoms. The purpose of this study was to synthesize data on the safety and quality of care in EMUs to inform the development of quality indicators for EMUs. METHODS: A systematic review was conducted according to the Preferred Reporting and Items for Systematic Review and Meta-Analysis (PRISMA) statement. The search strategy, which included broad search terms and synonyms pertaining to the EMU, was run in six medical databases and included conference proceedings. Data abstracted included patient and EMU demographics and quality and safety variables. Study quality was evaluated using a modified 15-item Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. Descriptive statistics and meta-analyses were used to describe and synthesize the evidence. RESULTS: The search yielded 7,601 references, of which 604 were reviewed in full text. One-hundred thirty-five studies were included. The quality and safety data came from 181,823 patients and reported on 34 different quality and safety variables. Included studies commonly reported the number of patients (108 studies; median number patients, 171.5), age (49 studies; mean age 35.7 years old), and the reason for admission (34 studies). The most common quality and safety data reported were the utility of the EMU admission (38 studies). Thirty-three studies (24.4%) reported on adverse events, and yielded a pooled proportion of adverse events of 7% (95% confidence interval [CI] 5-9%). The mean quality score was 73.3% (standard deviation [SD] 17.2). SIGNIFICANCE: This study demonstrates that there is a great deal of variation in the reporting of quality and safety measures and in the quality and safety in EMUs. Study quality also varied considerably from one study to the next. These findings highlight the need to develop evidence-based, consensus-driven quality indicators for EMUs.

Quality indicators in an epilepsy monitoring unit.

Examining and improving the quality of care in epilepsy monitoring units (EMUs) is essential to delivering the best possible care and to mitigating undesirable outcomes. Epilepsy monitoring units are unique in that an admission to an EMU often involves the induction of symptoms (seizures) rather than minimizing and/or treating symptoms, which can lead to an increased risk to patient safety. Very little research has addressed the quality of care and safety in EMUs. The objective of this study was to examine quality indicators in a large population of patients admitted to an EMU in a large health region. Data were collected prospectively on 396 consecutive patients admitted to the EMU for scalp EEG recording from 2008 to 2011 using a standardized data abstraction form. Variables examined included the following: patient demographics, baseline clinical characteristics, EMU admission statistics, and EMU quality indicators. We found that an admission to the EMU was a safe and effective tool in the management of patients with epilepsy and seizure-like events. The number of adverse events during the study period was low at 4.9%. The admission question was answered in 78.8% of cases, and it was partially answered in 6.6%. The need for systematically developed and validated quality indicators in EMUs is emphasized. The research in this area is sparse, and thus these data aid in supporting the utility of EMUs in the management and care of those with seizures and seizure-like events.

Experience and satisfaction of staff working in a seizure monitoring unit.

The purpose of this study was to investigate staff experience and satisfaction in the seizure monitoring unit (SMU) of a large health region serving a population of 1.4 million. A staff satisfaction questionnaire was administered yearly between 2007 and 2010 with an average response rate of 58.4%. The majority of staff perceived the SMU to be a positive workplace. Strengths of the SMU were interdisciplinary teamwork and patient-centred care. Satisfaction with professional development opportunities increased from 2007 to 2010, likely reflecting the implementation of training sessions. The physical environment was the least satisfactory. This study highlights aspects of staff experience that have improved or still need to be improved since implementing a quality and safety program in our SMU.

Epilepsy is associated with unmet health care needs compared to the general population despite higher health resource utilization--a Canadian population-based study.

PURPOSE: (1) To determine whether health resource utilization (HRU) and unmet health care needs differ for individuals with epilepsy compared to the general population or to those with another chronic condition (asthma, diabetes, migraine); and (2) to assess the association among epilepsy status, sociodemographic variables and HRU. METHODS: Data on HRU were assessed using the 2001-2005 Canadian Community Health Surveys, a nationally representative population-based survey. Weighted estimates of association were produced as adjusted odds ratio with 95% confidence intervals, and logistic regression was used to explore the association between sociodemographic variables and HRU in those with epilepsy. All data on disease status, HRU, and unmet health care needs were self-reported. KEY FINDINGS: Individuals with epilepsy had the highest rate of hospitalizations and the highest mean number of consultations with physicians. Despite higher rates of consultation with psychologists and social workers compared to the general population, those with epilepsy were significantly more likely to say they had unmet mental health care needs. People with epilepsy were also less likely to use dental services compared to the general population. Epilepsy was a significant predictor of HRU in logistic regression models. SIGNIFICANCE: Given the prevalence of psychiatric comorbidities in those with epilepsy, it is concerning that this group perceives unmet mental health care needs. It is also troublesome that there was decreased utilization of dental health care resources in those with epilepsy considering that these patients are more likely to have poor oral health. Although individuals with epilepsy use more health care services than the general population, this increase appears to be insufficient to address their health care needs.