AI-driven European Retrospective Database Study to predict disease onset in patients with epilepsy and depression
Alessandro Ruggieri, John Paul Leach, Elena Alvarez-Baron, Valeria Di Franco, Caroline Clare Benoist, Alessandro Comandini, Giorgio Di Loreto, Alessandro Lovera, Tom Constandse, Martina Pili, Julia Gallinaro, Pejman Farhadi Ghalati, Frida Bayard, Abdul Sattar Raslan, Aisling O’Loughlin, Balazs Vamosi, Agnese CattaneoBackground
Epilepsy and depression are prevalent, chronic conditions with a complex, bidirectional relationship that is not yet fully understood and contributes significantly to morbidity, mortality and healthcare burden. Despite advances in machine learning (ML) for analysing large real-world datasets, there is a lack of large-scale, multinational studies applying ML to explore the interplay between epilepsy and depression. This study aimed to identify predictors of depression in patients with epilepsy (PWE) and predictors of epilepsy in patients with depression (PWD), uncovering associations and shared risk factors across directions.
Methods
This retrospective, observational cohort study analysed longitudinal patient-level data from Denmark, France, Germany, Italy, Spain, Sweden and the United Kingdom. Supervised ML models were trained separately within each country. Demographics (age, gender), clinical (diagnosis and prescription history), socioeconomic status (SES, which includes employment status, education level, disposable income, long-term sick leave/financial state benefits, marital status) and healthcare utilisation features were used during training. Key predictors were identified using Shapley Additive Explanations.
Results
Approximately 2.2 million PWE and 9.7 million PWD were analysed across countries. Female gender, low SES and use of central nervous system (CNS) medications such as antipsychotics, anxiolytics and antimigraine agents were identified as predictors of depression in PWE. In PWD, epilepsy was associated with male gender, socioeconomic deprivation and use of selected CNS medications. Common predictors included demographics, socioeconomic factors and treatments for other CNS conditions, suggesting a shared higher multimorbidity burden.
Conclusions
This study demonstrates the potential of applying artificial intelligence to elucidate not only the multifactorial relationship between epilepsy and depression but also the interplay with other CNS disorders. The findings highlight the importance of demographic, clinical and social determinants in risk stratification and may assist development of screening tools for earlier intervention in high-risk patients.