Validating an Algorithm to Identify Patients With Infantile Spasms Using Medical Claims

Area of Science:

• Pediatric Neurology
• Medical Informatics
• Health Services Research

Background:

• Infantile spasms are a critical seizure type associated with West syndrome, primarily affecting infants aged 3-12 months.
• Early diagnosis and treatment of infantile spasms significantly improve clinical outcomes.
• Accurate identification of patients with infantile spasms using healthcare claims data has been lacking.

Purpose of the Study:

• To develop and validate algorithms for identifying infantile spasms in infants using claims data.
• To assess the performance of different algorithmic approaches for patient identification.

Main Methods:

• Algorithms were developed using claims data, including International Classification of Disease (ICD) codes, Current Procedural Terminology (CPT) codes, and prescription data.
• Data were sourced from an accountable care organization's claims database.
• Algorithm performance was evaluated based on sensitivity and specificity.

Main Results:

• An algorithm utilizing a specific ICD code for infantile spasms demonstrated the highest performance.
• This algorithm achieved high sensitivity and specificity in identifying patients with infantile spasms.
• Claims data analysis is a viable method for research on infantile spasms.

Conclusions:

• The developed algorithm using specific ICD codes is effective for identifying infantile spasms in claims data.
• This validated approach can facilitate further research into infantile spasms and West syndrome.
• Improved patient identification through claims data can support better clinical management and research.