Identifying children with Cystic Fibrosis in population-scale routinely collected data in Wales

Abstract

Introduction
 The challenges in identifying a cohort of people with a rare condition can be addressed by routinely collected, population-scale electronic health record (eHR) data, which provide large volumes of data at a national level. This paper describes the challenges of accurately identifying a cohort of children with Cystic Fibrosis (CF) using eHR and their validation against the UK CF Registry.
 Objectives
 To establish a proof of principle and provide insight into the merits of linked data in CF research and the benefits of access to multiple data sources. In particular, the UK CF Registry data, and to demonstrate the opportunity it represents as a resource for future CF research.
 
 Method
 Three eHR data sources were used to identify children with CF born in Wales between 1st January 1998 and 31st August 2015 within the Secure Anonymised Information Linkage (SAIL) Databank. The UK CF Registry was later acquired by SAIL and linked to the eHR cohort to validate the cases and explore the reasons for misclassifications.
 Results
 We identified 352 children with CF in the three eHR data sources. This was greater than expected based on historical incidence rates in Wales. Subsequent validation using the UK CF Registry found that 257 (73%) of these were true cases. Over 98% of individuals identified as CF cases in all three eHR data sources were confirmed as true cases; but this was only the case for 19.8% of those identified in a single data source.
 Conclusion
 Identifying health conditions in eHR data can be challenging, so data quality assurance and validation is important or the merit of the research is undermined. This retrospective review identifies some of the challenges in identifying CF cases and demonstrates the benefits of linking cases across multiple data sources to improve quality.

Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis

“…The similarity-based approach that we have developed re-used a comprehensive phenotypic description of patients based on their EHR data to detect ciliopathy patients in a clinical data warehouse. Unlike other studies using only a limited set of features presented in EHR, such as International Classification of Disease (ICD) codes (Griffiths et al, 2020), or a set of predefined disease specific phenotypes (Savolainen et al, 2021), we extracted all UMLS medical concepts in EHRs. Our results showed that the performance can be improved by including more phenotypes (6296 vs. 1578 phenotypes).…”