Association between inflammatory central nervous system diseases and epilepsy: A retrospective cohort study of 4252 patients in Germany

Prevalence of and factors associated with the early prescription of antiseizure medications in adults newly diagnosed with epilepsy in Germany

BACKGROUND

There is little information on prescription patterns of antiseizure medications (ASMs) during the early management of patients with epilepsy in Germany. Therefore, this study investigated the prevalence of and the factors associated with ASM prescription in patients newly diagnosed with epilepsy in this country.

METHODS

Adults diagnosed for the first time with epilepsy in one of 128 neurology practices in Germany between 2005 and 2021 were included (Disease Analyzer database, IQVIA). The prescription of ASMs was assessed within 30 days, six months, and 12 months of the diagnosis. Covariates were demographic factors, epilepsy sub-diagnoses, and co-diagnoses frequently associated with epilepsy.

RESULTS

This study included 55,962 participants (mean [SD] age 52.5 [20.0] years; 50.5 % men). The prevalence of ASM prescription ranged from 45.0 % within 30 days to 66.0 % within 12 months of the diagnosis. Men were less likely to receive ASMs within six and 12 months of epilepsy diagnosis than women. In addition, epilepsy sub-diagnoses of symptomatic, complex, or generalized nature were associated with increased odds of ASM prescription compared with epilepsy of unspecified nature. Finally, there was an inverse and significant association between multiple co-diagnoses (e.g., diabetes, mental and behavioral disorders due to use of alcohol, and traumatic brain injury) and ASM prescribing.

CONCLUSIONS

A substantial proportion of participants were prescribed ASMs in the year following epilepsy diagnosis, highlighting that the early prescription of ASMs was necessary for these patients. Further research is warranted to corroborate the present findings in other countries and settings.

Predicting the risk of stroke in patients with late-onset epilepsy: A machine learning approach

BACKGROUND

The goal of this cohort study was to estimate the predictors for ischemic stroke in patients with epilepsy in a large database containing data from general practitioners in Germany using machine learning methods.

METHODS

This retrospective cohort study included 11,466 patients aged ≥ 60 years with an initial diagnosis of epilepsy in 1182 general practices in Germany between January 2010 and December 2018 from the IQVIA Disease Analyzer database. The Sub-Population Optimization and Modeling Solutions (SOMS) tool was used to identify subgroups at a higher risk of stroke than the overall population with epilepsy based on 37 different variables.

RESULTS

A total of seven variables were considered important. Four co-diagnoses (diabetes, hypertension, heart failure, and alcohol dependence) were by far the strongest predictors with a combined predictive ability of more than 90%, whereby diabetes (41.4%) was the strongest predictor, followed by hypertension (35.0%) and heart failure (11.8%). The predictive importance of male gender was only 1.5%, and age was not recognized as an important predictor. Finally, the prescribed AEDs levetiracetam, with a predictive importance of 5.0%, and valproate, with 2.7%, were found to be weak predictors.

CONCLUSION

The stroke risk in patients with epilepsy was relatively high and could be predicted based on comorbidities such as diabetes mellitus, hypertension, heart failure, and alcohol dependence. Knowing and addressing these factors may help reduce the risk of stroke in patients with epilepsy.