Background: Most children with Benign epilepsy with centro-temporal spikes (BECTS) undergo remission during late adolescence and do not require treatment. In a small group of patients, the condition may evolve to encephalopathic syndromes including epileptic encephalopathy with continuous spike-and-wave during sleep (ECSWS), or Landau-Kleffner Syndrome (LKS). Development of prediction models for early identification of at-risk children is of utmost importance. Aim: To develop a predictive model of encephalopathic transformation using data-driven approaches, reveal complex interactions to identify potential risk factors. Methods: Data were collected from a cohort of 91 patients diagnosed with BECTS treated between the years 2005–2017 at a pediatric neurology institute. Data on the initial presentation was collected based on a novel BECTS ontology and used to discover potential risk factors and to build a predictive model. Statistical and machine learning methods were compared. Results: A subgroup of 18 children had encephalopathic transformation. The least absolute shrinkage and selection operator (LASSO) regression Model with Elastic Net was able to successfully detect children with ECSWS or LKS. Sensitivity and specificity were 0.83 and 0.44. The most notable risk factors were fronto-temporal and temporo-parietal localization of epileptic foci, semiology of seizure involving dysarthria or somatosensory auras. Conclusion: Novel prediction model for early identification of patients with BECTS at risk for ECSWS or LKS. This model can be used as a screening tool and assist physicians to consider special management for children predicted at high-risk. Clinical application of machine learning methods opens new frontiers of personalized patient care and treatment.
- Benign childhood epilepsy with centro-temporal spikes (BECTS)
- Electrical status epilepticus during slow-wave sleep (ESES)
- Epileptic encephalopathy with continuous spike-and-wave during sleep (ECSWS)
- Landau–Kleffner syndrome (LKS)
- Machine learning