Validity of Diagnostic Codes for Identification of Psoriasis Patients in Korea
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Background: Recently, the number of nationwide medical researches on psoriasis using the National Health Insurance Service database has been on the rise. However, identification of psoriasis using diagnostic codes alone can lead to misclassification. Accuracy of the diagnostic codes and their concordance with medical records should be validated first to identify psoriasis patients correctly. Objective: To validate the diagnostic codes of psoriasis (International Classification of Diseases, 10th Revision L40) and to find the algorithm for the identification of psoriasis. Methods: We collected medical records of patients who received their first diagnostic codes of psoriasis during 5 years from five hospitals. Fifteen percent of psoriasis patients were randomly selected from each hospital. We performed a validation by reviewing medical records and compared 5 algorithms to identify the best algorithm. Results: Total of 538 cases were reviewed and classified as psoriasis (n=368), not psoriasis (n=159), and questionable (n=11). The most accurate algorithm was including patients with ≥1 visits with psoriasis as primary diagnostic codes and prescription of vitamin D derivatives. Its positive predictive value was 96.5% (95% confidence interval [CI], 93.9%~98.1%), which was significantly higher than those of the algorithm, including patients with ≥1 visits with psoriasis as primary diagnostic codes or including ≥1 visits with diagnostic codes of psoriasis (primary or additional) (91.0% and 69.8%). Sensitivity was 90.8% (95% CI, 87.2%~ 93.4%) and specificity was 92.5% (95% CI, 86.9%~ 95.9%). Conclusion: Our study demonstrates a validated algorithm to identify psoriasis, which will be useful for the nationwide population-based study of psoriasis in Korea.
Abstract
Background: Recently, the number of nationwide medical researches on psoriasis using the National Health Insurance Service database has been on the rise. However, identification of psoriasis using diagnostic codes alone can lead to misclassification. Accuracy of the diagnostic codes and their concordance with medical records should be validated first to identify psoriasis patients correctly. Objective: To validate the diagnostic codes of psoriasis (International Classification of Diseases, 10th Revision L40) and to find the algorithm for the identification of psoriasis. Methods: We collected medical records of patients who received their first diagnostic codes of psoriasis during 5 years from five hospitals. Fifteen percent of psoriasis patients were randomly selected from each hospital. We performed a validation by reviewing medical records and compared 5 algorithms to identify the best algorithm. Results: Total of 538 cases were reviewed and classified as psoriasis (n=368), not psoriasis (n=159), and questionable (n=11). The most accurate algorithm was including patients with ≥1 visits with psoriasis as primary diagnostic codes and prescription of vitamin D derivatives. Its positive predictive value was 96.5% (95% confidence interval [CI], 93.9%~98.1%), which was significantly higher than those of the algorithm, including patients with ≥1 visits with psoriasis as primary diagnostic codes or including ≥1 visits with diagnostic codes of psoriasis (primary or additional) (91.0% and 69.8%). Sensitivity was 90.8% (95% CI, 87.2%~ 93.4%) and specificity was 92.5% (95% CI, 86.9%~ 95.9%). Conclusion: Our study demonstrates a validated algorithm to identify psoriasis, which will be useful for the nationwide population-based study of psoriasis in Korea.
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