A diagnostic algorithm for the evaluation of early onset genetic-metabolic epileptic encephalopathies

Area of Science:

• Neurology
• Genetics
• Metabolic Disorders

Background:

• Early onset epileptic encephalopathies (EOEE) pose significant clinical challenges, particularly in infants and young children.
• Unclear categorization and evolving genetic/metabolic discoveries complicate diagnosis and management of EOEE.
• Genetic and metabolic etiologies are central to EOEE, necessitating updated classification and diagnostic approaches.

Purpose of the Study:

• To provide an overview of genes, metabolic disorders, and syndromes involved in EOEE presenting before one year of age.
• To classify EOEE based on clinical and genetic-metabolic criteria into two main groups: seizures as the primary symptom and seizures with a syndromic phenotype.
• To propose a simplified diagnostic algorithm to guide metabolic and molecular-genetic investigations in clinical practice.

Main Methods:

• Review of current literature on genes, metabolic disorders, and syndromes associated with EOEE.
• Classification of EOEE based on a combined clinical and genetic-metabolic approach.
• Development of a diagnostic algorithm integrating biochemical, electroencephalographic, and neuroimaging data.

Main Results:

• EOEE are classified into two groups: those with prominent/unique seizures and those with associated syndromic phenotypes.
• A simplified diagnostic algorithm is proposed to aid in the workup of EOEE.
• The algorithm aims to clarify the diagnostic pathway for metabolic and molecular-genetic investigations.

Conclusions:

• A structured classification and diagnostic algorithm can improve the management of early onset epileptic encephalopathies.
• Integrated clinical, genetic, and metabolic investigations are crucial for accurate diagnosis.
• The proposed algorithm assists clinicians in navigating the diagnostic process for infants with EOEE.