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Development and Validation of Algorithms to Identify Individuals With Cutaneous Lupus From Healthcare Databases
Lisa N Guo1, Jordan T Said1, Michael J Woodbury2
1Department of Dermatology, Brigham and Women's Hospital, Boston, MA, USA.
Insights
Developing algorithms to identify cutaneous lupus erythematosus (CLE) from health records is crucial for epidemiology. Algorithms using dermatology diagnosis codes offer a validated method for identifying CLE patients in electronic health records.
Area of Science:
- Dermatology
- Rheumatology
- Epidemiology
- Health Informatics
Background:
- Accurate identification of cutaneous lupus erythematosus (CLE) patients from large datasets like electronic health records (EHRs) is currently lacking.
- This limitation hinders epidemiological studies of CLE.
- Validated methods are needed to extract CLE cases from healthcare databases.
Purpose of the Study:
- To develop and validate accurate algorithms for identifying individuals with cutaneous lupus erythematosus (CLE) using healthcare records.
- To improve the ability to study CLE epidemiology through data mining.
- To establish reliable case-finding methods for CLE in large patient populations.
Main Methods:
- Twelve case-finding algorithms were created using International Classification of Diseases (ICD)-10 codes, provider specialties, and medication data.
- Algorithms were validated on a cohort of 300 individuals from a multi-institutional healthcare network.
- Case definition standard involved dermatologist/rheumatologist documentation or biopsy findings, with performance measured by positive predictive values (PPVs), specificity, and sensitivity.
Main Results:
- Positive predictive values (PPVs) for the algorithms ranged from 58.0% to 92.9%.
- Algorithms relying solely on a single CLE diagnosis code from any provider showed poor PPVs.
- The most effective algorithm, achieving 89.0% PPV with maintained sensitivity, required at least one ICD-10 CLE diagnosis code from a dermatologist.
Conclusions:
- Employing specific cutaneous lupus erythematosus (CLE) diagnosis codes combined with dermatology as the coding provider specialty represents a valid approach.
- This method enables accurate identification of CLE patients within electronic health record systems.
- The findings support the use of these algorithms for epidemiological research and clinical data analysis.
Background:
There are no validated methods to identify individuals with cutaneous lupus erythematosus (CLE) from large databases including claims data and electronic health records, severely limiting the study of the epidemiology of this disease.
Objectives:
To develop and validate accurate algorithms to identify individuals with CLE from healthcare records.
Methods:
Twelve case-finding algorithms were developed based on the International Classification of Diseases (ICD)-10 diagnosis codes, provider specialty, and medication prescription data. To validate performance, algorithms were applied to a test cohort of 300 individuals drawn from a clinical data repository of a multi-institutional healthcare network in Boston, MA. Documentation of a CLE diagnosis by a dermatologist or rheumatologist determined from chart review or supportive biopsy findings was used as the case definition standard. Performance was evaluated based on calculated positive predictive values (PPVs), specificities, and sensitivities of each algorithm.
Results:
PPVs ranged from 58.0% to 92.9%. The use of a single diagnosis code for CLE from any provider had poor PPV. The algorithm with the highest PPV (89.0%) while maintaining sensitivity required at least 1 ICD-10 CLE diagnosis code recorded by a dermatologist.
Conclusions:
Utilizing CLE diagnosis codes and dermatology as the coding provider specialty is a valid method for identifying CLE patients from electronic health records.
