Abstract
To test the performance of a billing claims-based calcium pyrophosphate deposition disease (CPPD) algorithm for identifying pseudogout. We applied a published CPPD algorithm at an academic institution and randomly selected 100 patients for electronic medical record review for 3 phenotypes: (1) definite/probable CPPD, (2) definite/probable pseudogout; (3) definite pseudogout. Clinical data were recorded and positive predictive value (PPV) (95% CI) for each phenotype was calculated. We then modified the published algorithm to require ≥ 1 of 4 relevant terms ("pseudogout", "calcium pyrophosphate crystals", "CPPD", or "chondrocalcinosis") through automated text searching in clinical notes, and re-calculated PPVs. To estimate the percentage of pseudogout patients not identified by the published algorithm, we reviewed a random sample of 50 patients with ≥ 1 of 4 relevant terms in clinical notes who did not fulfill the published algorithm. Among patients fulfilling the published algorithm, 68% had ≥ 1 of 3 phenotypes. The published algorithm had PPV 24.0% (95% CI 19.3-28.7%) for definite/probable pseudogout and 18.0% (95% CI 14.5-21.5%) for definite pseudogout. Requiring ≥ 1 of 4 relevant terms in clinical notes increased PPV to 33.3% (95% CI 26.8-39.8%) for definite/probable pseudogout and 24.6% (95% CI 19.8-29.4%) for definite pseudogout. Among patients not fulfilling the published algorithm, 16.0% had definite/probable pseudogout and 6.0% had definite pseudogout. A billing code-based CPPD algorithm had low PPV for identifying pseudogout. Adding text searching modestly enhanced the PPV, though it remained low. These findings highlight the need for improved approaches to identify pseudogout to facilitate epidemiologic studies.