Identifying clinical features in primary care electronic health record studies: methods for codelist development

Abstract

ObjectiveAnalysis of routinely collected electronic health record (EHR) data from primary care is reliant on the creation of codelists to define clinical features of interest. To improve scientific rigour, transparency and replicability, we describe and demonstrate a standardised reproducible methodology for clinical codelist development.DesignWe describe a three-stage process for developing clinical codelists. First, the clear definition a priori of the clinical feature of interest using reliable clinical resources. Second, development of a list of potential codes using statistical software to comprehensively search all available codes. Third, a modified Delphi process to reach consensus between primary care practitioners on the most relevant codes, including the generation of an ‘uncertainty’ variable to allow sensitivity analysis.SettingThese methods are illustrated by developing a codelist for shortness of breath in a primary care EHR sample, including modifiable syntax for commonly used statistical software.ParticipantsThe codelist was used to estimate the frequency of shortness of breath in a cohort of 28 216 patients aged over 18 years who received an incident diagnosis of lung cancer between 1 January 2000 and 30 November 2016 in the Clinical Practice Research Datalink (CPRD).ResultsOf 78 candidate codes, 29 were excluded as inappropriate. Complete agreement was reached for 44 (90%) of the remaining codes, with partial disagreement over 5 (10%). 13 091 episodes of shortness of breath were identified in the cohort of 28 216 patients. Sensitivity analysis demonstrates that codes with the greatest uncertainty tend to be rarely used in clinical practice.ConclusionsAlthough initially time consuming, using a rigorous and reproducible method for codelist generation ‘future-proofs’ findings and an auditable, modifiable syntax for codelist generation enables sharing and replication of EHR studies. Published codelists should be badged by quality and report the methods of codelist generation including: definitions and justifications associated with each codelist; the syntax or search method; the number of candidate codes identified; and the categorisation of codes after Delphi review.

Effect of pre-existing conditions on bladder cancer stage at diagnosis: a cohort study using electronic primary care records in the UK

“…15,16 Libraries of Read codes that might be used by GPs to record these symptoms were collated using robust methods. 17 Symptomatic patients were identified by the presence of any of these codes in their records. Symptoms presented >1 year before diagnosis are not reliably more common in people who are subsequently diagnosed with cancer compared with controls; therefore, searches were constrained to 1 year before diagnosis.…”

“…Libraries of Read codes for the above conditions were collated, 17 and patients with these conditions were identified.…”

“…The CPRD is the largest database of longitudinal primary care records in the world and is recognised for its high-quality data. 14 Robust methods for case identification in the CPRD were used 17 and cancer diagnoses were validated by NCRAS. Despite this, the use of CPRD and NCRAS data has some limitations.…”

Assessing data quality from the Clinical Practice Research Datalink: a methodological approach applied to the full blood count blood test

“…The CPRD database is formed of 10 main datasets, as described in the CPRD Data Specification [4]. Patient records are coded using medical codes, which are the numeric equivalent of Read codes available in patient records from the GP system [5,6]. Other coding systems, such as the International Classification of Diseases-10 (ICD-10) codes to identify diseases and malignancies, are available in linked EHRs only [7].…”

Validation of acute exacerbation of chronic obstructive pulmonary disease (COPD) recording in electronic health records: a systematic review protocol

“…These variables will generally be defined by using ‘code lists’ of relevant clinical codes from a particular clinical terminology, for example, the 10th Revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) 13. Guidelines14 15 have been produced to give researchers advice on how best to generate these code lists, with the ultimate aim being to produce an accurate and reusable definition of all variables in a study. Repositories16–18 have been created to enable researchers to share their code lists but appear underused.…”