Evidence map›Paper›PMID 39192871›Full record

ArticleResearch and practice in thrombosis and haemostasis2024

Ascertaining accurate exposure to aspirin and other antithrombotic medications using structured electronic health record data.

Mansour Gergi, Katherine Wilkinson, Timothy B Plante, Neil A Zakai

Abstract read
In one paragraph

Article in Research and practice in thrombosis and haemostasis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Mansour GergiDepartment of Medicine, Larner College of Medicine, University of Vermont, Burlington, Vermont, USA.
Katherine WilkinsonDepartment of Pathology and Laboratory Medicine, University of Vermont, Burlington, Vermont, USA.
Timothy B PlanteDepartment of Medicine, Larner College of Medicine, University of Vermont, Burlington, Vermont, USA.
Neil A ZakaiDepartment of Medicine, Larner College of Medicine, University of Vermont, Burlington, Vermont, USA.

Funding

Vermont Center for Cardiovascular and Brain HealthP20GM135007 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Kathryn H Morelli · 2020 to 2026
$17.4M
Thrombosis and Bleeding Risk Assessment in Medical InpatientsR01HL141290 · NHLBI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Deirdra R Terrell, Neil Adrian Zakai · 2019 to 2026
$3.5M
NHLBI NIH HHS R01 HL141290NIGMS NIH HHS P20 GM135007
6 · The paper itself

Abstract

Background: Ascertaining accurately the exposure to antithrombotic medications for both research and quality initiatives has been challenging due to a multitude of reasons: aspirin, the most commonly used antithrombotic, is available over the counter in the United States. Additionally, antithrombotic medications are frequently interrupted for bleeding and procedures. Objectives: We aimed to develop and validate an algorithm to capture accurately the longitudinal exposure to antithrombotic medications including aspirin using the electronic health record. Methods: We used the Medical Inpatient Thrombosis and Hemostasis cohort, which consists of primary care patients at a university medical center followed for a median of 6.2 years. Exposure to antithrombotic medications was captured using the medication reconciliation data linked to each ambulatory encounter. We developed an algorithm that used the taking "yes" or "no" tab as well as start and stop dates to define the duration of exposure for each medication. Eighty charts were reviewed and compared with results of the algorithm for validation. We estimated the sensitivity, specificity, and positive and negative predictive values. Results: The algorithm was 97% (95% CI, 94%-100%) sensitive and 95% (95% CI, 90%-100%) specific in identifying exposure to any antithrombotic medication. This translated to a 93% (95% CI, 85%-100%) positive predictive value and 98% (95% CI, 96%-100%) negative predictive value. When looking at aspirin alone, the sensitivity and the positive predictive value were 95% (95% CI, 88%-100%) and 87% (95% CI, 71%-100%). Conclusion: This current algorithm provides a new and easily adaptable strategy to capture accurately exposure to aspirin and other antithrombotic medications.

Indexed as

algorithmanticoagulantsantithrombotic medicationsaspirinelectronic health record

Identifiers

PMID39192871
PMCPMC11347841

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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.