Evidence map›Paper›PMID 39226468›Full record

ArticleLupus2024

Using linked electronic medical record-pharmacy data to examine lupus medication adherence: A retrospective cohort study.

Kai Sun, Daniel Wojdyla, Ankoor Shah, Amanda M Eudy, Megan Eb Clowse

Abstract readComparative Study
In one paragraph

Article in Lupus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

5 authors.

Kai SunDivision of Rheumatology and Immunology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.ORCID 0000-0002-8406-2932
Daniel WojdylaDuke Clinical Research Institute, Duke University School of Medicine, Durham NC, USA.
Ankoor ShahDivision of Rheumatology and Immunology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Amanda M EudyDivision of Rheumatology and Immunology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.ORCID 0000-0002-3107-5545
Megan Eb ClowseDivision of Rheumatology and Immunology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.

Funding

Institutional Career Development CoreKL2TR002554 · NCATS · DUKE UNIVERSITY · PI SVETKEY, LAURA P · 2018 to 2022
$8.2M
NCATS NIH HHS KL2 TR002554
6 · The paper itself

Abstract

introductionMedication nonadherence is common in systemic lupus erythematosus (SLE) and associated with morbidity and mortality. We explored the reliability of pharmacy data within the electronic medical record (EMR) to examine factors associated with nonadherence to SLE medications.

methodsWe included patients with SLE who were prescribed ≥1 SLE medication for ≥90 days. We compared two datasets of pharmacy fill data, one within the EMR and another from the vendor who obtained this information from pharmacies and prescription benefit managers. Adherence was defined by medication possession ratio (MPR) ≥80%. In addition to MPR for each SLE medication, we evaluated the weighted-average MPR and the proportion of patients adherent to ≥1 SLE medication and to all SLE medications. We used logistic regression to examine factors associated with adherence.

resultsAmong 181 patients (median age 36, 96% female, 58% Black), 98% were prescribed hydroxychloroquine, 34% azathioprine, 33% mycophenolate, 18% methotrexate, and 7% belimumab. Among 1276 pharmacy records, 74% overlapped between linked EMR-pharmacy data and data obtained directly from the vendor. Only 9% were available from the vendor but not through linked EMR-pharmacy data. The weighted-average MPR was 57%; 45% were adherent to hydroxychloroquine, 46% to ≥1 SLE medication, and 32% to all SLE medications. Older age was associated with adherence in univariable and multivariable analyses. DISCUSSION: Our study showed that obtaining linked EMR-pharmacy data is feasible with minimal missing data and can be leveraged in future adherence research. Younger patients were more likely to be nonadherent and may benefit from targeted intervention.

Indexed as

Antirheumatic AgentsDatasets as TopicDrug MonitoringDrug PrescriptionsLupus Erythematosus, SystemicPharmaciesAdultAge FactorsCorrelation of DataElectronic Health RecordsFeasibility StudiesFemaleHumansLogistic ModelsMaleMiddle AgedAntirheumatic Agentselectronic medical recordmedication adherenceSystemic lupus erythematosus

Identifiers

PMID39226468
PMCPMC11427133

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