Identification and validation of lupus nephritis cases using administrative data

Area of Science:

• Nephrology
• Health Services Research
• Medical Informatics

Background:

• Large administrative databases like Medicaid claims are valuable for studying rare diseases such as lupus nephritis.
• Accurate patient identification from claims data is crucial for reliable health services research.

Purpose of the Study:

• To develop and validate an algorithm for identifying lupus nephritis cases using ICD-9 billing codes.
• To assess the performance of different coding strategies for lupus nephritis case ascertainment.

Main Methods:

• Compared four ICD-9 code-based strategies to identify lupus nephritis patients from Medicaid data (2000-2007).
• Strategies involved combinations of Systemic Lupus Erythematosus (SLE) codes (ICD-9 710.0), renal ICD-9 codes, and nephrologist visits.
• Validated algorithm performance using positive predictive value (PPV) through medical record review.

Main Results:

• A total of 234 subjects were identified and their medical records reviewed.
• The third strategy, utilizing a combination of lupus and renal ICD-9 codes plus nephrologist encounter claims, demonstrated the highest PPV.
• This optimized strategy achieved an 88% positive predictive value for identifying lupus nephritis patients.

Conclusions:

• A specific algorithm combining lupus and renal ICD-9 codes with nephrologist claims provides a high PPV for identifying lupus nephritis patients.
• This method offers a reliable approach for health services research using administrative claims data.
• The developed strategy can serve as a model for identifying patients with other rare diseases within large datasets.