Validation of Chronic Inflammatory Demyelinating Polyradiculoneuropathy Coding in US Claims Data

Area of Science:

• Neurology
• Clinical Epidemiology
• Health Informatics

Background:

• Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is a rare autoimmune disorder affecting the peripheral nervous system.
• The accuracy of using administrative claims data for identifying CIDP cases is not well-established.
• Accurate case identification is essential for research and healthcare management.

Purpose of the Study:

• To validate the performance of claims-based algorithms for identifying CIDP cases.
• To assess the positive predictive value (PPV) of diagnosis codes and intravenous immunoglobulin (IVIG) use in claims data.
• To compare the accuracy of different algorithm configurations.

Main Methods:

• A validation study was conducted using linked electronic health record and Medicare claims data (2008-2020).
• Claims-based algorithms required specific International Classification of Diseases (ICD-9/10) CIDP codes prior to IVIG use.
• Chart review by neurologists served as the reference standard for CIDP diagnosis.

Main Results:

• The PPV of an algorithm requiring at least one CIDP code before IVIG was 66.2%.
• Requiring at least two CIDP codes increased the PPV to 71.2%.
• The PPV for patients meeting specific European diagnostic criteria was lower at 30.0%.

Conclusions:

• Claims-based algorithms identify a significant proportion of patients with other chronic inflammatory neuropathies, not strictly CIDP.
• Increasing the number of required CIDP codes enhances the PPV of claims-based case identification.
• Refined algorithms are needed for more precise identification of CIDP in large datasets.