Validation of claims-based algorithms to identify patients with psoriasis

Accurately identifying patients with psoriasis (PsO) is crucial for generating real-world evidence on PsO disease course and treatment utilization.

We developed nine claims-based algorithms for PsO using a combination of the International Classification of Diseases (ICD)-9 codes, specialist visit, and medication dispensing using Medicare linked to electronic health records data (2013-2014) in two healthcare provider networks in Boston, Massachusetts. We calculated positive predictive value (PPV) and 95% confidence interval (CI) for each algorithm using the treating physician's diagnosis of PsO via chart review as the gold standard. Among the confirmed PsO cases, we assessed their PsO disease activity.

The nine claims-based algorithms identified 990 unique patient records. Of those, 918 (92.7%) with adequate information were reviewed. The PPV of the algorithms ranged from 65.1 to 82.9%. An algorithm defined as ≥1 ICD-9 diagnosis code for PsO and ≥1 prescription claim for topical vitamin D agents showed the highest PPV (82.9%). The PPV of the algorithm requiring ≥2 ICD-9 diagnosis codes and ≥1 prescription claim for PsO treatment excluding topical steroids was 81.1% but higher (82.5%) when ≥1 diagnosis was from a dermatologist. Among 411 PsO patients with adequate information on PsO disease activity in EHRs, 1.5-5.8% had no disease activity, 31.3-36.8% mild, and 26.9-35.1% moderate-to-severe across the algorithms.

Claims-based algorithms based on a combination of PsO diagnosis codes and dispensing for PsO-specific treatments had a moderate-to-high PPV. These algorithms can serve as a useful tool to identify patients with PsO in future real-world data pharmacoepidemiologic studies.