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

Ling Wang¹, Shannon Yarosz², Hosain Aghamoosa³

¹1 Data Resource Center, Nationwide Children's Hospital, Columbus, OH, USA.

Journal of Child Neurology

|June 5, 2018

Summary

Identifying infantile spasms in infants using claims data is crucial for early diagnosis and treatment. An algorithm using specific International Classification of Disease (ICD) codes proved most effective for accurate patient identification.

Keywords:

ACTH epidemiology epilepsy

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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.