Registration of acute stroke: validity in the Danish Stroke Registry and the Danish National Registry of Patients

BACKGROUND: The validity of the registration of patients in stroke-specific registries has seldom been investigated, nor compared with administrative hospital discharge registries. The objective of this study was to examine the validity of the registration of patients in a stroke-specific registry (The Danish Stroke Registry [DSR]) and a hospital discharge registry (The Danish National Patient Registry [DNRP]). METHODS: Assuming that all patients with stroke were registered in either the DSR, DNRP or both, we first identified a sample of 75 patients registered with stroke in 2009; 25 patients in the DSR, 25 patients in the DNRP, and 25 patients registered in both data sources. Using the medical record as a gold standard, we then estimated the sensitivity and positive predictive value of a stroke diagnosis in the DSR and the DNRP. Secondly, we reviewed 160 medical records for all potential stroke patients discharged from four major neurologic wards within a 7-day period in 2010, and estimated the sensitivity, specificity, positive predictive value, and negative predictive value of the DSR and the DNRP. RESULTS: Using the first approach, we found a sensitivity of 97% (worst/best case scenario 92%–99%) in the DSR and 79% (worst/best case scenario 73%–84%) in the DNRP. The positive predictive value was 90% (worst/best case scenario 72%–98%) in the DSR and 79% (worst/best case scenario 62%–88%) in the DNRP. Using the second approach, we found a sensitivity of 91% (95% confidence interval [CI] 81%–96%) and 58% (95% CI 46%–69%) in the DSR and DNRP, respectively. The negative predictive value was 91% (95% CI 83%–96%) in the DSR and 72% (95% CI 62%–80%) in the DNRP. The specificity and positive predictive value did not differ among the registries. CONCLUSION: Our data suggest a higher sensitivity in the DSR than the DNRP for acute stroke diagnoses, whereas the positive predictive value was comparable in the two data sources.