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

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. We aimed to develop and validate an algorithm to capture accurately the longitudinal exposure to antithrombotic medications including aspirin using the electronic health record. 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. 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%). This current algorithm provides a new and easily adaptable strategy to capture accurately exposure to aspirin and other antithrombotic medications. •Exposure to aspirin and other blood thinners is challenging to capture in research.•We developed an algorithm to capture this exposure using the electronic health record.•The algorithm was 98% sensitive and 95% specific.•This algorithm has very good accuracy and is generalizable and adaptable to different settings.