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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 2 of 2 Research Studies DisplayedRhee TG, Kumar M, Ross JS
Age-related trajectories of cardiovascular risk and use of aspirin and statin among U.S. Adults Aged 50 or older, 2011-2018.
The purpose of this study was to examine age-related trajectories of cardiovascular risk and use of aspirin and statin among U.S. adults aged 50 or older. The investigators concluded that while adults aged ≥75 do not benefit from the use of aspirin to prevent the first CVD, many continue to take aspirin on a regular basis. In spite of the clear benefit of statin use to prevent a subsequent CVD event, many older adults in this risk category are not taking a statin.
AHRQ-funded; HS022882.
Citation: Rhee TG, Kumar M, Ross JS .
Age-related trajectories of cardiovascular risk and use of aspirin and statin among U.S. Adults Aged 50 or older, 2011-2018.
J Am Geriatr Soc 2021 May;69(5):1272-82. doi: 10.1111/jgs.17038..
Keywords: Elderly, Blood Thinners, Cardiovascular Conditions, Heart Disease and Health, Risk, Medication
Herrin J, Abraham NS, Yao X
Comparative effectiveness of machine learning approaches for predicting gastrointestinal bleeds in patients receiving antithrombotic treatment.
The purpose of this retrospective cross-sectional study was to compare the performance of 3 machine learning approaches with the commonly-used HAS-BLED (hypertension, abnormal kidney and liver function, stroke, bleeding, labile international normalized ratio, older age, and drug or alcohol use) risk score in predicting antithrombotic-related gastrointestinal bleeding (GIB). The machine-learning models were regularized Cox proportional hazards regression (RegCox), random survival forests, and extreme gradient boosting (XGBoost). Findings showed that the machine learning models revealed similar performance in identifying patients at high risk for GIB after being prescribed antithrombotic agents. Two models (RegCox and XGBoost) performed modestly better than the HAS-BLED score.
AHRQ-funded; HS025402.
Citation: Herrin J, Abraham NS, Yao X .
Comparative effectiveness of machine learning approaches for predicting gastrointestinal bleeds in patients receiving antithrombotic treatment.
JAMA Netw Open 2021 May;4(5):e2110703. doi: 10.1001/jamanetworkopen.2021.10703..
Keywords: Blood Thinners, Medication, Risk, Adverse Drug Events (ADE), Adverse Events, Medication: Safety, Patient Safety, Comparative Effectiveness