Comparative analysis of Cystic Fibrosis Registry data from the UK with USA, France and Australasia.

BACKGROUND: Using the UK Cystic Fibrosis Database, we analysed the health of the UK CF paediatric population (UKPP) in terms of their biographical, clinical and infection status and compared outcomes with the US, French and Australasian CF Registries. METHODS: UKPP data were collected for 2,673 patients aged less than 18 years in 2001 and used as a reference base for comparison with the most recent equivalent CF Registry reports. RESULTS: Although differences exist between National CF Registries, all record similar demographic factors and key outcomes. Where plausible comparisons can be made, we report that the UKPP had the oldest median age (15.0 years), the Australasian population had the lowest median age at diagnosis (1.8 months). Approximately, double the expected number of UKPP patients (23% and 19%, respectively) fall below the 10th centile for height and weight with similar outcomes in Australasia. UKPP and French populations had similar proportions with FEV1 >80% predicted (53% and 54%, respectively). CONCLUSION: Each Registry's data systems have developed independently providing a first step towards international comparisons. Standardisation of data collection criteria and definition for national CF Registries is required and we propose a standardised minimum data set, which would facilitate data integration as part of a global Registry for CF.

Demographics of the UK cystic fibrosis population: implications for neonatal screening.

The objective was to determine the composition of the Cystic Fibrosis (CF) Population attending specialist UK CF centres in terms of age, gender, age at diagnosis, genotype and ethnicity. With the planned introduction of the national CF screening programme in the UK, cystic fibrosis transmembrane regulator (CFTR) mutations were compared between different ethnic groups enabling a UK-specific frequency of mutations to be defined. Data were analysed from the patient biographies held in the UK CF Database (see www.cystic-fibrosis.org.uk). The currently registered population of 5,274 CF patients is 96.3% Caucasian with a male preponderance that significantly increases with age. The majority of the 196 non-Caucasian CF patients are from the Indian Subcontinent (ISC), of which one in 84 UK CF patients are of Pakistani origin. The commonest CFTR mutation, deltaF508, is found in 74.1% of all CF chromosomes. In the Caucasian CF population, 57.5% are deltaF508 homozygotes but the UK ISC CF population with only 24.7%, has significantly fewer deltaF508 homozygotes patients (95% confidence interval (CI) 0.2-0.4). The distribution of Caucasian patients with deltaF508/deltaF508, deltaF508/Other and Other/Other does not fit the expected distribution with a Hardy-Weinberg model unless those patients without a detected mutation are excluded (P<0.001). The UK CF Database has shown the UK CF population to have distinct characteristics separate from the North American and European CF Registries. The ISC group contains many mutations not recognised by current genetic analysis, and one in four ISC patients have no CFTR mutations identified. The CFTR analysis proposed for the screening programme would detect 96% of patients registered in the database, but is unlikely to achieve the desired >80% detection rates in the ethnic minority groups. Screen-positive, non-Caucasian infants without an identifiable CFTR mutation should be referred for a sweat test and genetic counselling when serum trypsinogen concentrations remain elevated after birth.

Detailed clinical modelling approach to data extraction from heterogeneous data sources for clinical research.

The reuse of routinely collected clinical data for clinical research is being explored as part of the drive to reduce duplicate data entry and to start making full use of the big data potential in the healthcare domain. Clinical researchers often need to extract data from patient registries and other patient record datasets for data analysis as part of clinical studies. In the TRANSFoRm project, researchers define their study requirements via a Query Formulation Workbench. We use a standardised approach to data extraction to retrieve relevant information from heterogeneous data sources, using semantic interoperability enabled via detailed clinical modelling. This approach is used for data extraction from data sources for analysis and for pre-population of electronic Case Report Forms from electronic health records in primary care clinical systems.