BACKGROUND: Anticoagulation in patients with atrial fibrillation (AF) is challenging because stroke-risk reduction must be balanced against increased bleeding risk. OBJECTIVE: We developed a decision model integrating both stroke and bleeding risk schemes to guide optimal use of anticoagulation in AF, and compared model recommendations with warfarin use in a real-world database. METHODS: A Markov model based on demographics, CHADS(2) (Congestive Heart Failure, Hypertension, Age of 75 years and greater, Diabetes Mellitus and History of Stroke) stroke and ATRIA (Anticoagulation and Risk Factors in Atrial Fibrillation) bleed risk scores, and anticoagulation treatment effects from clinical trials simulated health state transitions for recently diagnosed AF patients. The model recommended the treatment with greater quality-adjusted life expectancy. Model recommendations were contrasted with actual warfarin use recorded in the Thomson Reuters MarketScan database (N = 64,946). RESULTS: 74.8% (n = 48,548) of the Marketscan AF cohort had CHADS(2) ≥1, of whom 14.3% had moderate/high (≥4) ATRIA bleeding risk. While the model recommended warfarin for almost all patients with CHADS(2) ≥1 who are at low bleeding risk, it recommended warfarin for fewer patients as bleeding risk increased. Of the 44,611 patients recommended warfarin, 63.4% of patients were considered warfarin exposed (concordant with model recommendation), and of the 20,335 patients recommended aspirin (acetylsalicylic acid), 59.7% received warfarin (discordant with model recommendations). Actual warfarin use decreased modestly with higher stroke risk (p < 0.0001) and with higher bleeding risk (p < 0.0001). CONCLUSION: High discordance between actual warfarin use and model recommendations suggests that anticoagulation decisions are not based on systematic evaluation of stroke and bleeding risks. Model-based clinical decision aids may improve oral anticoagulation decisions by more systematically weighing bleed and stroke risk.
BACKGROUND: Anticoagulation in patients with atrial fibrillation (AF) is challenging because stroke-risk reduction must be balanced against increased bleeding risk. OBJECTIVE: We developed a decision model integrating both stroke and bleeding risk schemes to guide optimal use of anticoagulation in AF, and compared model recommendations with warfarin use in a real-world database. METHODS: A Markov model based on demographics, CHADS(2) (Congestive Heart Failure, Hypertension, Age of 75 years and greater, Diabetes Mellitus and History of Stroke) stroke and ATRIA (Anticoagulation and Risk Factors in Atrial Fibrillation) bleed risk scores, and anticoagulation treatment effects from clinical trials simulated health state transitions for recently diagnosed AFpatients. The model recommended the treatment with greater quality-adjusted life expectancy. Model recommendations were contrasted with actual warfarin use recorded in the Thomson Reuters MarketScan database (N = 64,946). RESULTS: 74.8% (n = 48,548) of the Marketscan AF cohort had CHADS(2) ≥1, of whom 14.3% had moderate/high (≥4) ATRIA bleeding risk. While the model recommended warfarin for almost all patients with CHADS(2) ≥1 who are at low bleeding risk, it recommended warfarin for fewer patients as bleeding risk increased. Of the 44,611 patients recommended warfarin, 63.4% of patients were considered warfarin exposed (concordant with model recommendation), and of the 20,335 patients recommended aspirin (acetylsalicylic acid), 59.7% received warfarin (discordant with model recommendations). Actual warfarin use decreased modestly with higher stroke risk (p < 0.0001) and with higher bleeding risk (p < 0.0001). CONCLUSION: High discordance between actual warfarin use and model recommendations suggests that anticoagulation decisions are not based on systematic evaluation of stroke and bleeding risks. Model-based clinical decision aids may improve oral anticoagulation decisions by more systematically weighing bleed and stroke risk.
Authors: Anand R Shewale; Jill T Johnson; Chenghui Li; David Nelsen; Bradley C Martin Journal: J Stroke Cerebrovasc Dis Date: 2015-10-21 Impact factor: 2.136
Authors: Emer R McGrath; Alan S Go; Yuchiao Chang; Leila H Borowsky; Margaret C Fang; Kristi Reynolds; Daniel E Singer Journal: J Am Geriatr Soc Date: 2016-12-30 Impact factor: 5.562
Authors: Scott A Chapman; Catherine A St Hill; Meg M Little; Michael T Swanoski; Shellina R Scheiner; Kenric B Ware; M Nawal Lutfiyya Journal: BMC Health Serv Res Date: 2017-02-11 Impact factor: 2.655
Authors: Margaret C Fang; Alan S Go; Yuchiao Chang; Leila H Borowsky; Niela K Pomernacki; Natalia Udaltsova; Daniel E Singer Journal: Neurology Date: 2014-02-14 Impact factor: 9.910
Authors: Paul T Kocis; Guodong Liu; Dinara Makenbaeva; Jeffrey Trocio; Diana Velott; JoAnn B Trainer; Younos Abdulsattar; Marta I Molina; Douglas L Leslie Journal: Drugs Real World Outcomes Date: 2016-05-10
Authors: Gene R Quinn; Olivia N Severdija; Yuchiao Chang; Liane O Dallalzadeh; Daniel E Singer Journal: J Am Heart Assoc Date: 2018-06-09 Impact factor: 5.501
Authors: Daniel E Singer; Yuchiao Chang; Leila H Borowsky; Margaret C Fang; Niela K Pomernacki; Natalia Udaltsova; Kristi Reynolds; Alan S Go Journal: J Am Heart Assoc Date: 2013-06-21 Impact factor: 5.501