Stepped-wedge randomised trial to evaluate population health intervention designed to increase appropriate anticoagulation in patients with atrial fibrillation

Abstract

BackgroundClinical guidelines recommend anticoagulation for patients with atrial fibrillation (AF) at high risk of stroke; however, studies report 40% of this population is not anticoagulated.ObjectiveTo evaluate a population health intervention to increase anticoagulation use in high-risk patients with AF.MethodsWe used machine learning algorithms to identify patients with AF from electronic health records at high risk of stroke (CHA2DS2-VASc risk score ≥2), and no anticoagulant prescriptions within 12 months. A clinical pharmacist in the anticoagulation service reviewed charts for algorithm-identified patients to assess appropriateness of initiating an anticoagulant. The pharmacist then contacted primary care providers of potentially undertreated patients and offered assistance with anticoagulation management. We used a stepped-wedge design, evaluating the proportion of potentially undertreated patients with AF started on anticoagulant therapy within 28 days for clinics randomised to intervention versus usual care.ResultsOf 1727 algorithm-identified high-risk patients with AF in clinics at the time of randomisation to intervention, 432 (25%) lacked evidence of anticoagulant prescriptions in the prior year. After pharmacist review, only 17% (75 of 432) of algorithm-identified patients were considered potentially undertreated at the time their clinic was randomised to intervention. Over a third (155 of 432) were excluded because they had a single prior AF episode (transient or provoked by serious illness); 36 (8%) had documented refusal of anticoagulation, the remainder had other reasons for exclusion. The intervention did not increase new anticoagulant prescriptions (intervention: 4.1% vs usual care: 4.0%, p=0.86).ConclusionsAlgorithms to identify underuse of anticoagulation among patients with AF in healthcare databases may not capture clinical subtleties or patient preferences and may overestimate the extent of undertreatment. Changing clinician behaviour remains challenging.

Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review

“…Blinding of participants was unsuccessful in two studies [ 62 , 72 ] and was unclear in six studies [ 44 , 46 , 54 , 57 , 69 , 70 ]. Blinding of outcome assessment was unsuccessful in seven studies [ 33 , 46 , 62 , 65 , 69 , 70 , 74 ] and was unclear in one study [ 57 ]. Three studies did not clearly state whether they had complete data for the enrolled participants [ 57 , 66 , 77 ].…”

“…All of the 13 studies had a low risk of selective reporting bias. Other potential sources of bias included a small sample size [ 62 , 70 , 72 ], a short study period [ 62 ], and a lack of detailed information regarding RCTs and follow-ups [ 57 , 66 ].…”

“…Given that health care providers may hold different perceptions toward different AI systems of varying performance and reliability, it would be helpful if the studies provide a transparent description of the AI system’s architecture, accuracy or reliability performance, and possible risks. Unfortunately, in our review, 21 studies did not provide adequate information about the architecture of the AI applications [ 25 , 29 , 32 , 34 , 37 , 44 , 46 , 50 , 52 , 54 , 56 - 62 , 65 , 68 , 70 , 75 ] and 22 studies did not reveal the performance and possible risks of AI under evaluation [ 26 , 29 , 34 , 37 , 39 , 46 , 48 - 50 , 52 , 54 , 56 - 62 , 64 , 65 , 68 , 69 ]. Further, considering that some self-evolving adaptive clinical AI applications continuously incorporate the latest clinical practice data and published evidence, it is important to undertake periodic monitoring and recalibration of AI applications to ensure that they are working as expected.…”

The 4S-AF Scheme (Stroke Risk; Symptoms; Severity of Burden; Substrate): A Novel Approach to In-Depth Characterization (Rather than Classification) of Atrial Fibrillation

“…Owing to the rapidly advancing technologies for computer-based decision support tools and machine learning application in medicine, the description of stroke risk in AF patients may evolve beyond clinical risk factor-based approach, but the decision to use OAC will always be a binary yes/no entity. 34 The Symptom severity (Sy) domain addresses the patientcentered, symptom-directed focus of AF management. This component focuses on the severity of symptoms, currently using the European Heart Rhythm Association (EHRA) symptom score, and is important for treatment decisions.…”

Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review

“…To evaluate a population health intervention to increase anticoagulation use in high-risk patients with atrial fibrillation Wang et al [61] The proposed system solved the problem of doctor recommendations to a good effect when evaluated by domain experts NA (data from 3 hospitals)…”