National Healthcare Quality and Disparities Report
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Search All Research Studies
AHRQ Research Studies Date
Topics
- Adverse Drug Events (ADE) (3)
- Adverse Events (3)
- (-) Blood Thinners (3)
- Cardiovascular Conditions (1)
- Clinical Decision Support (CDS) (1)
- Comparative Effectiveness (1)
- Decision Making (1)
- Electronic Health Records (EHRs) (1)
- Health Information Technology (HIT) (1)
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- (-) Medication: Safety (3)
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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.
Results
1 to 3 of 3 Research Studies DisplayedReese TJ, Del Fiol G, Morgan K
A shared decision-making tool for drug interactions between warfarin and nonsteroidal anti-inflammatory drugs: design and usability study.
Exposure to life-threatening drug-drug interactions (DDIs) occurs despite the widespread use of clinical decision support. The DDI between warfarin and nonsteroidal anti-inflammatory drugs is common and potentially life-threatening. Patients can play a substantial role in preventing harm from DDIs; however, the current model for DDI decision-making is clinician centric. This study aimed to design and examine the usability of DDInteract, a tool to support shared decision-making (SDM) between a patient and provider for the DDI between warfarin and nonsteroidal anti-inflammatory drugs.
AHRQ-funded; HS026198.
Citation: Reese TJ, Del Fiol G, Morgan K .
A shared decision-making tool for drug interactions between warfarin and nonsteroidal anti-inflammatory drugs: design and usability study.
JMIR Hum Factors 2021 Oct 26;8(4):e28618. doi: 10.2196/28618..
Keywords: Blood Thinners, Medication: Safety, Medication, Clinical Decision Support (CDS), Decision Making, Electronic Health Records (EHRs), Health Information Technology (HIT), Adverse Drug Events (ADE), Adverse Events, Patient Safety
Feng Y, Pai CW, Seiler K
Adverse outcomes associated with inappropriate direct oral anticoagulant starter pack prescription among patients with atrial fibrillation: a retrospective claims-based study.
This retrospective analysis investigated the risk for bleeding events with higher dosing of direct oral anticoagulant (DOAC) in the first 1-3 weeks of treatment for patients with atrial fibrillation (AF). Findings showed that patients who received an inappropriate DOAC prescription were more likely to identify as Black. Rates of ED visits, hospitalizations, and deaths overall were numerically lower in patients with starter pack DOAC prescriptions. In contrast, rates of ED visits and hospitalizations related to significant bleeding were numerically higher in patients with starter pack DOAC prescriptions. Among patients with AF but without acute venous thromboembolism, those who received an inappropriate DOAC starter pack had numerically higher rates of severe bleeding leading to ED visits and hospitalizations compared to those prescribed an appropriate non-starter pack DOAC anticoagulant.
AHRQ-funded; HS026874.
Citation: Feng Y, Pai CW, Seiler K .
Adverse outcomes associated with inappropriate direct oral anticoagulant starter pack prescription among patients with atrial fibrillation: a retrospective claims-based study.
J Thromb Thrombolysis 2021 May;51(4):1144-49. doi: 10.1007/s11239-020-02358-3..
Keywords: Blood Thinners, Medication, Medication: Safety, Medical Errors, Adverse Drug Events (ADE), Adverse Events, Heart Disease and Health, Cardiovascular Conditions
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