A decisional model to individualize warfarin recommendations: expected impact on treatment and outcome rates in a real-world population with atrial fibrillation

BACKGROUND: How the adoption of prediction models to decide which patient with atrial fibrillation (AF) to anticoagulate can affect prescription rates and outcomes is unclear.

METHODS: We retrospectively analyzed data from Danish registries on patients with a first-time recorded AF from 2005 to 2010. We simulated the adoption of a decisional model based on the individual absolute risk reduction of stroke and absolute risk increase of bleeding with warfarin, as expected from the patient CHA2DS2-VASc and HAS-BLED, adjusted for a 0.6 relative value for bleeding versus stroke. We studied 3 different model versions and calculated for each of them the net benefit associated with its adoption, measured as the value-adjusted reduction in stroke and bleeding events at 1year, compared with i) the actual practice, or ii) recommending warfarin consistently with the European Society of Cardiology (ESC) guidelines, irrespective of HAS-BLED.

RESULTS: We included 41,455 patients; 31.9% actually received warfarin. The expected treatment rate with the model ranged from 21% to 87% according to the version used. The model version resulting into the highest treatment rate (i.e. treating any patient with CHA2DS2-VASc ≥1) was associated with the greatest net benefit (0.98; 95% credible interval 0.72-1.23), compared with the actual practice, with a 1/3 reduction in overall mortality, as with the adoption of ESC guidelines.

CONCLUSIONS: Preliminarily to a randomized impact study, our analysis suggests that individualizing anticoagulation for AF using a decisional model might have a clinical advantage over actual practice, and no added advantage over following ESC guidelines.