Claims-based algorithms using primary discharge diagnosis codes for acute ischemic or hemorrhagic stroke had a high positive predictive value of 92.6% (95% CI, 88.8%-96.4%) but low sensitivity (59.5%).
Cohort (n=15,089)
Yes
Do claims-based diagnostic coding algorithms accurately identify stroke in a contemporary Medicare population compared to expert adjudication?
Claims-based algorithms for stroke in Medicare data have high positive predictive value and specificity, making them suitable for outcomes in comparative effectiveness studies, but their low sensitivity makes them unsuitable for estimating stroke incidence.
Effect estimate: Sensitivity 59.5%, Specificity 99.8% (95% CI 88.8%-96.4%)
BACKGROUND: The accuracy of stroke diagnosis in administrative claims for a contemporary population of Medicare enrollees has not been studied. We assessed the validity of diagnostic coding algorithms for identifying stroke in the Medicare population by linking data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study to Medicare claims. METHODS AND RESULTS: The REGARDS Study enrolled 30 239 participants ≥45 years in the United States between 2003 and 2007. Stroke experts adjudicated suspected strokes, using retrieved medical records. We linked data for participants enrolled in fee-for-service Medicare to claims files from 2003 through 2009. Using adjudicated strokes as the gold standard, we calculated accuracy measures for algorithms to identify incident and recurrent strokes. We linked data for 15 089 participants, among whom 422 participants had adjudicated strokes during follow-up. An algorithm using primary discharge diagnosis codes for acute ischemic or hemorrhagic stroke (International Classification of Diseases, Ninth Revision, Clinical Modification codes: 430, 431, 433.x1, 434.x1, 436) had a positive predictive value of 92.6% (95% confidence interval, 88.8%-96.4%), a specificity of 99.8% (99.6%-99.9%), and a sensitivity of 59.5% (53.8%-65.1%). An algorithm using only acute ischemic stroke codes (433.x1, 434.x1, 436) had a positive predictive value of 91.1% (95% confidence interval, 86.6%-95.5%), a specificity of 99.8% (99.7%-99.9%), and a sensitivity of 58.6% (52.4%-64.7%). CONCLUSIONS: Claims-based algorithms to identify stroke in a contemporary Medicare cohort had high positive predictive value and specificity, supporting their use as outcomes for etiologic and comparative effectiveness studies in similar populations. These inpatient algorithms are unsuitable for estimating stroke incidence because of low sensitivity.
Kumamaru et al. (Wed,) conducted a cohort in Stroke (n=15,089). Claims-based diagnostic coding algorithms vs. Adjudicated strokes (gold standard) was evaluated on Positive predictive value of an algorithm using primary discharge diagnosis codes for acute ischemic or hemorrhagic stroke (Sensitivity 59.5%, Specificity 99.8%, 95% CI 88.8%-96.4%). Claims-based algorithms using primary discharge diagnosis codes for acute ischemic or hemorrhagic stroke had a high positive predictive value of 92.6% (95% CI, 88.8%-96.4%) but low sensitivity (59.5%).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: