Background and Purpose This study evaluated the use of an artificial intelligence (AI) platform on mobile devices in measuring and increasing medication adherence in stroke patients on anticoagulation therapy. The introduction of direct oral anticoagulants (DOACs), while reducing the need for monitoring, have also placed pressure on patients to self-manage. Suboptimal adherence goes undetected as routine laboratory tests are not reliable indicators of adherence, placing patients at increased risk of stroke and bleeding. Methods A randomized, parallel-group, 12-week study was conducted in adults (n = 28) with recently diagnosed ischemic stroke receiving any anticoagulation. Patients were randomized to daily monitoring by the AI Platform (intervention) or to no daily monitoring (control). The AI application visually identified the patient, the medication and confirmed ingestion. Adherence was measured by pill counts and plasma sampling in both groups. Results For all patients (n = 28), mean (standard deviation [SD]) age was 57 (13.2) years and 53.6% were female. Mean (SD) cumulative adherence based on the AI Platform was 90.5% (7.5%). Plasma drug concentration levels indicated that adherence was 100% (15 of 15) and 50% (6 of 12) in the intervention and control groups, respectively. Conclusions Patients, some with little experience using a smartphone, successfully used the technology and demonstrated a 50% improvement in adherence based on plasma drug concentration levels. For patients receiving DOACs, absolute improvement increased to 67%. Real-time monitoring has the potential to increase adherence and change behavior, particularly in patients on DOAC therapy. Clinical Trial Registration-URL: http://www.clinicaltrials.gov. Unique identifier: NCT02599259.

Highlights Posterior reversible encephalopathy syndrome may be associated with coronavirus disease. Risk factors may include modest blood pressure fluctuations and anakinra. All reported patients had clinical improvement.

Niacin therapy does not lead to significant reductions in total or cause-specific mortality or recurrent cardiovascular events among persons with or at risk of atherosclerotic cardiovascular disease.

Heart failure with a preserved ejection fraction (HFpEF) is increasingly prevalent and a leading cause of morbidity and mortality worldwide. HFpEF has a complex pathophysiology, with recent evidence suggesting that an interaction of cardiovascular and noncardiovascular comorbidities (e.g. obesity, hypertension, diabetes, coronary artery disease, and chronic kidney disease) induces an inflammatory state that eventually leads to myocardial structural and functional alterations. Current ACCF/AHA guidelines suggest incorporation of biomarkers along with clinical and imaging tools to establish the diagnosis and disease severity in heart failure (HF). However, the majority of data on biomarkers relating to their levels, or their role in accurate diagnosis, prognostication, and disease activity, has been derived from studies in undifferentiated HF or HF with a reduced EF (HFrEF). As the understanding of the mechanisms underlying HFpEF continues to evolve, biomarkers reflecting different pathways including neurohormonal activation, myocardial injury, inflammation, and fibrosis have a clinical utility beyond the diagnostic scope. Accordingly, in this review article we describe the various established and novel plasma biomarkers and their emerging value in diagnosis, prognosis, response, and guiding of targeted therapy in patients with HFpEF.

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