Identifying people living with cystic fibrosis in the Danish National Patient Registry: A validation study.

Background: The Danish National Patient Registry (DNPR) serves as a valuable resource for scientific research. However, to ensure accurate results in cystic fibrosis (CF) studies that rely on DNPR data, a robust case-identification algorithm is essential. This study aimed to develop and validate algorithms for the reliable identification of CF patients in the DNPR.

Methods: Using the Danish Cystic Fibrosis Registry (DCFR) as a reference, accuracy measures including sensitivity and positive predictive value (PPV) for case-finding algorithms deployed in the DNPR were calculated. Algorithms were based on minimum number of hospital contacts with CF as the main diagnosis and minimum number of days between first and last contact.

Results: An algorithm requiring a minimum of one hospital contact with CF as the main diagnosis yielded a sensitivity of 96.1 % (95 % CI: 94.2 %; 97.4 %) and a PPV of 84.9 % (82.0 %; 87.4 %). The highest-performing algorithm required minimum 2 hospital visits and a minimum of 182 days between the first and the last contact and yielded a sensitivity of 95.9 % (95 % CI: 94.1 %; 97.2 %), PPV of 91.0 % (95 % CI: 88.6 %; 93.0 %) and a cohort entry delay of 3.2 months at the 75th percentile (95th percentile: 38.7 months).

Conclusions: The DNPR captures individuals with CF with high sensitivity and is a valuable resource for CF-research. PPV was improved at a minimal cost of sensitivity by increasing requirements of minimum number of hospital contacts and days between first and last contact. Cohort entry delay increased with number of required hospital contacts.