Evidence map›Paper›PMID 37649988›Full record

ArticleJAMIA open2023

Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.

Emily L Graul, Philip W Stone, Georgie M Massen, Sara Hatam, Alexander Adamson, Spiros Denaxas, Nicholas S Peters, Jennifer K Quint

Abstract read
In one paragraph

Article in JAMIA open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Major Adverse Cardiovascular Events and Cause-Specific Mortality After Hospitalisation in COPD.International journal of chronic obstructive pulmonary disease · 2025
    Article
  4. A Standardized Guideline for Assessing Extracted Electronic Health Records Cohorts: A Scoping Review.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Emily L GraulSchool of Public Health, Imperial College London, London W12 0BZ, United Kingdom.ORCID https://orcid.org/0000-0003-4717-0907
Philip W StoneSchool of Public Health, Imperial College London, London W12 0BZ, United Kingdom.ORCID https://orcid.org/0000-0002-2326-4987
Georgie M MassenNational Heart & Lung Institute, Imperial College London, London W12 0BZ, United Kingdom.ORCID https://orcid.org/0000-0003-4355-6546
Sara HatamUsher Institute, University of Edinburgh, Edinburgh EH16 4UX, United Kingdom.ORCID https://orcid.org/0000-0003-0927-4027
Alexander AdamsonSchool of Public Health, Imperial College London, London W12 0BZ, United Kingdom.ORCID https://orcid.org/0000-0003-0265-5900
Spiros DenaxasInstitute of Health Informatics, University College London, London NW1 2DA, United Kingdom.ORCID https://orcid.org/0000-0001-9612-7791
Nicholas S PetersNational Heart & Lung Institute, Imperial College London, London W12 0BZ, United Kingdom.ORCID https://orcid.org/0000-0002-1014-7052
Jennifer K QuintSchool of Public Health, Imperial College London, London W12 0BZ, United Kingdom.ORCID https://orcid.org/0000-0003-0149-4869

Funding

Medical Research Council MC_PC_20059
6 · The paper itself

Abstract

Objective: To develop a standardizable, reproducible method for creating drug codelists that incorporates clinical expertise and is adaptable to other studies and databases. Materials and Methods: We developed methods to generate drug codelists and tested this using the Clinical Practice Research Datalink (CPRD) Aurum database, accounting for missing data in the database. We generated codelists for: (1) cardiovascular disease and (2) inhaled Chronic Obstructive Pulmonary Disease (COPD) therapies, applying them to a sample cohort of 335 931 COPD patients. We compared searching all drug dictionary variables (A) against searching only (B) chemical or (C) ontological variables. Results: In Search A, we identified 165 150 patients prescribed cardiovascular drugs (49.2% of cohort), and 317 963 prescribed COPD inhalers (94.7% of cohort). Evaluating output per search strategy, Search C missed numerous prescriptions, including vasodilator anti-hypertensives (A and B:19 696 prescriptions; C:1145) and SAMA inhalers (A and B:35 310; C:564). Discussion: We recommend the full search (A) for comprehensiveness. There are special considerations when generating adaptable and generalizable drug codelists, including fluctuating status, cohort-specific drug indications, underlying hierarchical ontology, and statistical analyses. Conclusions: Methods must have end-to-end clinical input, and be standardizable, reproducible, and understandable to all researchers across data contexts.

Indexed as

code setselectronic medical recordsepidemiologyhealth data sciencemisclassification biasvalue sets

Identifiers

PMID37649988
PMCPMC10463548

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.