Abstract
The Clinical Practice Research Datalink is a nation-wide database of primary healthcare data records in England (UK) linked to several health services. A visit to a health practitioner can result in the digital storing of diagnostic and prescription therapeutic information. Access to patient primary care and linked service data depends on the research in mind; however, typically several flat files that describe patient interactions with a health practitioner are delivered. Some of these files will describe additional data such as the result of medical tests and patient lifestyles, denoted collectively into entity values. This data is used to supplement the medical notes recorded by a general practitioner. We have made available a set of R scripts that reads the clinical flat files, additional clinical flat files and entity values, and returns patient clinical data linked with the requested additional data. We have also included medcode descriptions associated with several entities along with instruction of how to extend the code for additional entities. The code is free to download under the MIT license: https://github.com/acnash/CPRD_Additional_Clinical
Highlights
The Clinical Practice Research Datalink (CPRD) is an NHS primary care service that stores clinical, referral, therapeutic data and linked medical services such as imaging and hospital records of patients enrolled in England (UK)
We describe the link between patient clinical data and additional clinical data in cases where such a CPRD data release has been made available
Both clinical data and additional clinical data are stored in two separate sets of files, often denoted as head_Extract_Clinical_##.txt and head_Extract_Additional_###.txt, respectively
Summary
The Clinical Practice Research Datalink (CPRD) is an NHS primary care service that stores clinical, referral, therapeutic data and linked medical services such as imaging and hospital records of patients enrolled in England (UK). CPRD data has been widely used to improve medical practice and to further our understanding of drug efficacy and drug safety and disease mechanism[2]. As with all longitudinal data, understanding the outcome of a disease is usually confounded by several factors[3]. Therapy data and social habits (to name but a few) are all potential confounders that should be treated appropriately (see 4 for a research example of common patient characteristics using a CPRD data release). The curation and manipulation of CPRD data can cause significant barriers for many researchers, especially those less familiar with manipulating text-based large datasets or programming
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
