Georg Zimmermann

Diagnostic accuracy of the Salzburg EEG criteria for non-convulsive status epilepticus: a retrospective study

BACKGROUND Several EEG criteria have been proposed for diagnosis of non-convulsive status epilepticus (NCSE), but none have been clinically validated. We aimed to assess the diagnostic accuracy of the EEG criteria proposed by a panel of experts at the fourth London-Innsbruck Colloquium on Status Epilepticus in Salzburg, 2013 (henceforth called the Salzburg criteria). METHODS We did a retrospective, diagnostic accuracy study using EEG recordings from patients admitted for neurological symptoms or signs to three centres in two countries (Danish Epilepsy Centre, Dianalund, Denmark; Aarhus University Hospital, Aarhus, Denmark; and Paracelsus Medical University, Salzburg, Austria). Participants were included from the Danish centres if they were aged 4 months or older, and from the Austrian centre if aged 18 years or older. Participants were sorted into two groups: consecutive patients under clinical suspicion of having NCSE (the clinical validation group) or consecutive patients with abnormal EEG findings but no clinical suspicion of NCSE (the control group). Two raters blinded to all other patient data retrospectively analysed the EEG recordings and, using the Salzburg criteria, categorised patients as in NCSE or not in NCSE. By comparing with a reference standard inferred from all clinical and para-clinical data, therapeutic response, and the final outcome, we calculated sensitivity, specificity, overall diagnostic accuracy, positive and negative predictive values, and inter-rater agreement for the Salzburg criteria. The reference standard was inferred by two raters who were blinded to the scorings of the Salzburg criteria. FINDINGS We retrospectively reviewed EEG data from 220 patients. EEGs in the clinical validation group were recorded in 120 patients between Jan 1, and Feb 28, 2014 (Austria), and Aug 1, 2014, and Jan 31, 2015 (Denmark). EEGs in the control group were recorded in 100 patients between Jan 13 and Jan 22, 2014 (Austria) and Jan 12 and Jan 26, 2015 (Denmark). According to the reference standard, 43 (36%) of the 120 patients in the validation group had NCSE. In the validation cohort sensitivity was 97·7% (95% CI 87·9-99·6) and specificity was 89·6% (80·8-94·6); overall accuracy was 92·5% (88·3-97·5). Positive predictive value was 84·0% (95% CI 74·1-91·5) and negative predictive value was 98·6% (94·4-100). Three people in the control group (n=100) fulfilled the Salzburg criteria and were therefore false positives (specificity 97·0%, 95% CI 91·5-99·0; sensitivity not calculable). Inter-rater agreement was high for both the Salzburg criteria (k=0·87) and for the reference standard (k=0·95). Therapeutic changes occurred significantly more often in the group of patients fulfilling Salzburg criteria (42 [84%] of 50 patients) than in those who did not (11 [16%] of 70; p<0·0001). INTERPRETATION The Salzburg criteria for diagnosis of NCSE have high diagnostic accuracy and excellent inter-rater agreement, making them suitable for implementation in clinical practice. FUNDING None.

Personalized safety measures reduce the adverse event rate of long-term video EEG

Safety in epilepsy monitoring units (EMUs) has become an increasing concern because adverse events occur in up to 10% of patients undergoing long‐term video EEG in EMUs. The aim of this study was to assess the effectiveness of a specific safety protocol in an EMU.

Perampanel in routine clinical use across Europe: Pooled, multicenter, observational data

To pool observational data on the routine use of perampanel to obtain information on real‐world outcomes and data in populations typically underrepresented in clinical trials.

Basic terminology and quantities

We first describe how survival time can be appropriately modeled and define some basic quantities which characterize the survival time distribution. Then, we introduce some terminology and notation of survival time samples. In this context, we will also precisely state what censoring means.

Regression models for survival data

In this chapter, we, at first, give a short explanation why regression models are needed for many different types of survival data. Then, we introduce two very popular classes of survival analytic regression models and discuss the relationship between them. When examining the epilepsy dataset, we repeatedly use a particular parametric model called the Weibull model, which is therefore defined in this chapter.

Model checking procedures

In this chapter, we will basically address two important issues: Firstly, we will present some methods for checking, let’s say, the basic assumptions underlying a particular model. For example, when considering a Weibull model, we have to answer the following questions.

Potential years lost and life expectancy in adults with newly diagnosed epilepsy

Studies using relative measures, such as standardized mortality ratios, have shown that patients with epilepsy have an increased mortality. Reports on more direct and absolute measure such as life expectancy are sparse. We report potential years lost and how life expectancy has changed over 40 years in a cohort of patients with newly diagnosed epilepsy.