DOAC Dosing Discordance Using Different Estimates of Kidney Function and Outcomes.

Direct oral anticoagulants (DOACs) are indicated for the prevention of stroke in nonvalvular atrial fibrillation. Although Food and Drug Administration labeling for DOACs uses estimated creatinine clearance according to the Cockcroft-Gault (C-G) equation, estimated glomerular filtration rate according to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation is often reported. The objectives of this study were to evaluate DOAC dosing discordance and to determine whether discordance based on various estimates of kidney function is associated with bleeding or thromboembolism. The study was an institutional review board approved retrospective analysis of patients at UPMC Presbyterian Hospital from January 1, 2010, to December 12, 2016. Data were obtained through electronic medical records. Adults who received a medication charge for rivaroxaban or dabigatran, had a diagnosis code for atrial fibrillation, and had a serum creatinine within 3 days of DOAC initiation were included. Doses were considered discordant if the calculated dose based on CKD-EPI did not match the patient's dose during index admission, if dosed correctly using C-G. Association of discordance with dabigatran, rivaroxaban, and clinical outcomes was determined using odds ratios and 95% confidence intervals. Rivaroxaban discordance was present among 49 of the 644 (8%) patients who were dosed correctly with C-G. Dabigatran discordance was present among 17 of the 590 (3%) patients who were dosed correctly. Discordance with rivaroxaban was found to increase the risk of thromboembolism when using CKD-EPI (odds ratio, 2.83; 95% CI, 1.02-7.79, P = .045) versus C-G. Our findings emphasize the need to dose DOACs, specifically rivaroxaban, appropriately in patients with nonvalvular atrial fibrillation.

Clinical decision support for drug related events: Moving towards better prevention.

Clinical decision support (CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors (ME) and adverse drug events (ADEs). Critically ill patients are at increased risk for ME, ADEs and serious negative outcomes related to these events. Capitalizing on CDS to detect ME and prevent adverse drug related events has the potential to improve patient outcomes. The key to an effective medication safety surveillance system incorporating CDS is advancing the signals for alerts by using trajectory analyses to predict clinical events, instead of waiting for these events to occur. Additionally, incorporating cutting-edge biomarkers into alert knowledge in an effort to identify the need to adjust medication therapy portending harm will advance the current state of CDS. CDS can be taken a step further to identify drug related physiological events, which are less commonly included in surveillance systems. Predictive models for adverse events that combine patient factors with laboratory values and biomarkers are being established and these models can be the foundation for individualized CDS alerts to prevent impending ADEs.