{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T17:58:36Z","timestamp":1755799116346,"version":"3.44.0"},"reference-count":20,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute for Health Research (NIHR) School for Primary Care Research","award":["SPCR-2021-2026"],"award-info":[{"award-number":["SPCR-2021-2026"]}]},{"name":"Endeavour Health Charitable Trust","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>The Clinical Practice Research Datalink (CPRD) is a large and widely used resource of electronic health records from the UK, linking primary care data to hospital data, death registration data, cancer registry data, deprivation data and mental health services data. Extraction and management of CPRD data is a computationally demanding process and requires a significant amount of work, in particular when using R. The <jats:bold>rcprd<\/jats:bold> package simplifies the process of extracting and processing CPRD data in order to build datasets ready for statistical analysis. Raw CPRD data is provided in thousands of.txt files, making querying this data cumbersome and inefficient. <jats:bold>rcprd<\/jats:bold> saves the relevant information into an SQLite database stored on the hard drive which can then be queried efficiently to extract required information about individuals. <jats:bold>rcprd<\/jats:bold> follows a four-stage process: 1) Definition of a cohort, 2) Read in medical\/prescription data and save into an SQLite database, 3) Query this SQLite database for specific codes and tests to create variables for each individual in the cohort, 4) Combine extracted variables into a dataset ready for statistical analysis. Functions are available to extract common variable types (e.g., history of a condition, or time until an event occurs, relative to an index date), and more general functions for database queries, allowing users to define their own variables for extraction. The entire process can be done from within R, with no knowledge of SQL required. This manuscript showcases the functionality of <jats:bold>rcprd<\/jats:bold> by running through an example using simulated CPRD Aurum data. <jats:bold>rcprd<\/jats:bold> will reduce the duplication of time and effort among those using CPRD data for research, allowing more time to be focused on other aspects of research projects.<\/jats:p>","DOI":"10.1371\/journal.pone.0327229","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T17:34:11Z","timestamp":1755624851000},"page":"e0327229","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["rcprd: An R package to simplify the extraction and processing of Clinical Practice Research Datalink (CPRD) data, and create analysis-ready datasets"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0849-3458","authenticated-orcid":true,"given":"Alexander","family":"Pate","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rosa","family":"Parisi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evangelos","family":"Kontopantelis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Sperrin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"issue":"3","key":"pone.0327229.ref001","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1093\/ije\/dyv098","article-title":"Data Resource Profile: Clinical Practice Research Datalink (CPRD)","volume":"44","author":"E Herrett","year":"2015","journal-title":"Int J Epidemiol"},{"issue":"6","key":"pone.0327229.ref002","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1093\/ije\/dyz034","article-title":"Data resource profile: Clinical Practice Research Datalink (CPRD) Aurum","volume":"48","author":"A Wolf","year":"2019","journal-title":"Int J Epidemiol"},{"issue":"1","key":"pone.0327229.ref003","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s10654-018-0442-4","article-title":"Approach to record linkage of primary care data from Clinical Practice Research Datalink to other health-related patient data: overview and implications","volume":"34","author":"S Padmanabhan","year":"2019","journal-title":"Eur J Epidemiol"},{"issue":"2","key":"pone.0327229.ref004","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2017-020738","article-title":"Spatial distribution of clinical computer systems in primary care in England in 2016 and implications for primary care electronic medical record databases: a cross-sectional population study","volume":"8","author":"E Kontopantelis","year":"2018","journal-title":"BMJ Open"},{"key":"pone.0327229.ref005"},{"key":"pone.0327229.ref006","unstructured":"CPRD. CPRD GOLD September 2024 Dataset. https:\/\/www.cprd.com\/doi\/cprd-gold-june-2024-dataset"},{"issue":"2","key":"pone.0327229.ref007","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0171784","article-title":"rEHR: An R package for manipulating and analysing Electronic Health Record data","volume":"12","author":"DA Springate","year":"2017","journal-title":"PLoS One"},{"key":"pone.0327229.ref008","unstructured":"The Health Foundation Analytics Lab. Aurumpipeline [Internet]. 2021. Available from: https:\/\/github.com\/HFAnalyticsLab\/aurumpipeline"},{"key":"pone.0327229.ref009","doi-asserted-by":"crossref","unstructured":"Yimer BB, Selby D, Jani M, Nenadic G, Lunt M, Dixon WG. Drugprepr: Prepare Electronic Prescription Record Data to Estimate Drug Exposure. https:\/\/cran.r-project.org\/package=drugprepr. 2021.","DOI":"10.32614\/CRAN.package.drugprepr"},{"issue":"7","key":"pone.0327229.ref010","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1002\/pds.4440","article-title":"Assumptions made when preparing drug exposure data for analysis have an impact on results: An unreported step in pharmacoepidemiology studies","volume":"27","author":"SR Pye","year":"2018","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"pone.0327229.ref011","unstructured":"CPRD. CPRD Aurum Data Specification. 2022. https:\/\/www.cprd.com\/"},{"year":"2024","author":"K M\u00fcller","key":"pone.0327229.ref012"},{"issue":"2","key":"pone.0327229.ref013","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0212291","article-title":"Term sets: A transparent and reproducible representation of clinical code sets","volume":"14","author":"R Williams","year":"2019","journal-title":"PLoS One"},{"key":"pone.0327229.ref014","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbi.2017.04.010","article-title":"Clinical code set engineering for reusing EHR data for research: A review","volume":"70","author":"R Williams","year":"2017","journal-title":"J Biomed Inform"},{"issue":"11","key":"pone.0327229.ref015","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2017-019637","article-title":"Identifying clinical features in primary care electronic health record studies: methods for codelist development","volume":"7","author":"J Watson","year":"2017","journal-title":"BMJ Open"},{"issue":"9","key":"pone.0327229.ref016","article-title":"Selection of medical diagnostic codes for analysis of electronic patient records. Application to stroke in a primary care database","volume":"4","author":"eCRT Research Team","year":"2009","journal-title":"PLoS One"},{"key":"pone.0327229.ref017","first-page":"20","article-title":"Checklist and guidance on creating codelists for routinely collected health data research","volume":"4","author":"J Matthewman","year":"2024","journal-title":"NIHR Open Res"},{"key":"pone.0327229.ref018","unstructured":"Clinical Practice Research Datalink. CPRD Aurum Sample Dataset October 2021. 2021. https:\/\/doi.org\/10.48329\/hm7t-qs28"},{"key":"pone.0327229.ref019","doi-asserted-by":"crossref","DOI":"10.1093\/med\/9780198796619.001.0001","volume-title":"Prognosis Research in Healthcare: Concepts, Methods, and Impact","author":"RD Riley","year":"2019"},{"key":"pone.0327229.ref020","unstructured":"Richardson N, Cook I, Crane N, Dunnington D, Francois R, Keane J. Arrow: Integration to \u2019Apache\u2019 \u2019Arrow\u2019. https:\/\/github.com\/apache\/arrow\/. 2024."}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0327229","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T17:34:16Z","timestamp":1755624856000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0327229"}},"subtitle":[],"editor":[{"given":"Sreeram V.","family":"Ramagopalan","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,8,19]]},"references-count":20,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8,19]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0327229","relation":{},"ISSN":["1932-6203"],"issn-type":[{"type":"electronic","value":"1932-6203"}],"subject":[],"published":{"date-parts":[[2025,8,19]]}}}