Huifeng Yun

Objective The study objective was to update a method to identify comorbid conditions using only medication information in circumstances in which diagnosis codes may be undercaptured, such as in single‐specialty electronic health records (EHRs), and to compare the distribution of comorbidities across Rx‐Risk versus other traditional comorbidity indices. Methods Using First Databank, RxNorm, and its web‐based clients, RxNav and RxClass, we mapped Drug Concept Unique Identifiers (RxCUIs), National Drug Codes (NDCs), and Anatomical Therapeutic Chemical (ATC) codes to Rx‐Risk, a medication‐focused comorbidity index. In established rheumatoid arthritis (RA) and osteoarthritis (OA) cohorts within the Rheumatology Informatics System for Effectiveness registry, we then compared Rx‐Risk with other comorbidity indices, including the Charlson Comorbidity Index, Rheumatic Disease Comorbidity Index (RDCI), and Elixhauser. Results We identified 965 unique ingredient RxCUIs representing the 46 Rx‐Risk comorbidity categories. After excluding dosage form and ingredient related RxCUIs, 80,911 unique associated RxCUIs were mapped to the index. Additionally, 187,024 unique NDCs and 354 ATC codes were obtained and mapped to the index categories. When compared to traditional comorbidity indices in the RA cohort, the median score for Rx‐Risk (median 6.00 [25th percentile 2, 75th percentile 9]) was much greater than for Charlson (median 0 [25th percentile 0, 75th percentile 0]), RDCI (median 0 [25th percentile 0, 75th percentile 0]), and Elixhauser (median 1 [25th percentile 1, 75th percentile 1]). Analyses of the OA cohort yielded similar results. For patients with a Charlson score of 0 (85% of total), both the RDCI and Elixhauser were close to 1, but the Rx‐Risk score ranged from 0 to 16 or more. Conclusion The misclassification and under‐ascertainment of comorbidities in single‐specialty EHRs can largely be overcome by using a medication‐focused comorbidity index.