Background: Atrial fibrillation (AF) is the most common significant cardiac rhythm disorder and is a powerful common risk factor for stroke. Randomized trials have demonstrated that anticoagulation can reduce the risk of stroke in patients with AF. Yet, there continues to be widespread underutilization of this therapy. To address this practice gap locally and improve efforts to reduce the risk of stroke for patients with AF in our health system, we have designed a study to implement and evaluate the effectiveness of an Atrial Fibrillation Decision Support Tool (AFDST) embedded within our electronic health record.

Methods: Our intervention is provider-facing and focused on decision support. The clinical setting is ambulatory patients being seen by primary care physicians. Patients include those with both incident and prevalent AF. This randomized, prospective trial will enroll 800 patients in our University of Cincinnati Health System who are currently receiving less than optimal anticoagulation therapy as determined by the AFDST. Patients will be randomized to one of two arms - 1) usual care, in which the AFDST is available for use; 2) addition of a best practice advisory (BPA) to the AFDST notifying the clinician that their patient stands to gain a significant benefit from a change in their current thromboprophylactic therapy.

Results: The primary outcome is effectiveness of the BPA measured by change to "appropriate thromboprophylaxis" based on the AFDST recommendation at 3 months post randomization. Secondary endpoints include Reach and Adoption, from the RE-AIM framework for implementation studies. Sample size is based upon an improvement from inappropriate to appropriate anticoagulation therapy estimated at 4% in the usual care arm and ≥10% in the experimental arm.

Conclusion: Our goal is to examine whether addition of a BPA to an AFDST focused on primary care physicians in an ambulatory care setting will improve "appropriate thromboprophylaxis" compared with usual care. Results will be examined at 3 months post randomization and at the end of the study to evaluate durability of changes. We expect to complete patient enrollment by the end of June 2022.

Trial Registration: Clinicaltrials.gov NCT04099485.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ahj.2022.01.012DOI Listing

Publication Analysis

Top Keywords

decision support
12
atrial fibrillation
12
usual care
12
electronic health
8
reduce risk
8
risk stroke
8
stroke patients
8
health system
8
primary care
8
care physicians
8

Similar Publications

Implementation of a High-sensitivity Troponin Assay for Adult Patients Who Present to the Emergency Department With Chest Pain: The Role of Clinical Decision Support.

CJC Open

December 2024

Department of Laboratory Medicine and Pathology, Faculty of Medicine and Dentistry, College of Health Sciences, University of Alberta, Edmonton, Alberta, Canada.

Background: The objective of this study was to assess the health outcomes for patients who present to the emergency department (ED) with cardiac chest pain after the implementation of an accelerated diagnostic protocol using a high-sensitivity troponin assay (hs-TnI).

Methods: This prospective before-after cohort study used population-based linked health administrative data for adult patients who presented to a Canadian urban ED with chest pain of suspected cardiac origin over a 2-year study period. The primary outcome was ED length of stay (LOS).

View Article and Find Full Text PDF

Pasireotide is an effective treatment for both Cushing's disease (CD) and acromegaly due to its ability to suppress adrenocorticotropic hormone and growth hormone, and to normalize insulin-like growth factor-1 levels, resulting in tumor shrinkage. However, it may also cause hyperglycemia as a side effect in some patients. The aim of this study was to review previous recommendations regarding the management of pasireotide-induced hyperglycemia in patients with CD and acromegaly and to propose efficient monitoring and treatment algorithms based on recent evidence and current guidelines for type 2 diabetes treatment.

View Article and Find Full Text PDF

Background: Skin cancer poses a significant global health threat, with early detection being essential for successful treatment. While deep learning algorithms have greatly enhanced the categorization of skin lesions, the black-box nature of many models limits interpretability, posing challenges for dermatologists.

Methods: To address these limitations, SkinSage XAI utilizes advanced explainable artificial intelligence (XAI) techniques for skin lesion categorization.

View Article and Find Full Text PDF

Point mutations at codon 600 of the BRAF oncogene are the most common alterations in cutaneous melanoma (CM). Assessment of BRAF status allows to personalize patient management, though the affordability of molecular testing is limited in some countries. This study aimed to develop a model for predicting alteration in BRAF based on routinely available clinical and histological data.

View Article and Find Full Text PDF

This study evaluates the efficiency of public hospitals in Greece during the COVID-19 epidemic in 2020, using Data Envelopment Analysis (DEA) and the Analytical Hierarchy Process (AHP). Faced with unprecedented pressure from increased demand for medical services, these hospitals had to adapt quickly while playing a crucial role in supporting local economies, similar to the effect of tourism on rural economies. This study reveals that, despite average efficiency scores of 83% for result-oriented models (BCC) and 65% for constant return models (CCR), inefficiencies of scale emerged under the pressures of the pandemic.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!