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      Association of Anticoagulant Therapy With Risk of Fracture Among Patients With Atrial Fibrillation

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          Abstract

          This comparative effectiveness cohort study tests the hypothesis that use of direct anticoagulants is associated with a lower risk of fracture compared with warfarin therapy among patients with atrial fibrillation. Are oral anticoagulants differentially associated with risk of fracture among patients with atrial fibrillation? In this comparative effectiveness cohort study of 167 275 patients with atrial fibrillation, direct oral anticoagulants (ie, dabigatran, rivaroxaban, and apixaban) were associated with modestly lower fracture risk compared with warfarin. This protective association was more pronounced among patients with atrial fibrillation who also had a diagnosis of osteoporosis; among the direct oral anticoagulants, fracture risk was lowest among apixaban users. These findings add to speculation that warfarin may be harmful to bone health and suggest that direct oral anticoagulants may be preferred among patients with atrial fibrillation and high fracture risk. Warfarin is prescribed to patients with atrial fibrillation (AF) for the prevention of cardioembolic complications. Whether warfarin adversely affects bone health is controversial. The availability of alternate direct oral anticoagulant (DOAC) options now make it possible to evaluate the comparative safety of warfarin in association with fracture risk. To test the hypothesis that, among patients with nonvalvular AF, use of DOACs vs warfarin is associated with lower risk of incident fracture. This comparative effectiveness cohort study used the MarketScan administrative claims databases to identify patients with nonvalvular AF and who were prescribed oral anticoagulants from January 1, 2010, through September 30, 2015. To reduce confounding, patients were matched on age, sex, CHA 2 DS 2 -VASc (congestive heart failure, hypertension, age [>65 years = 1 point; >75 years = 2 points], diabetes, and previous stroke/transient ischemic attack [2 points], vascular disease) score, and high-dimensional propensity scores. The final analysis included 167 275 patients with AF. Data were analyzed from February 27, 2019 to September 18, 2019. Warfarin and DOACs (dabigatran etexilate, rivaroxaban, and apixaban). Incident hip fracture, fracture requiring hospitalization, and all clinical fractures (identified using inpatient or outpatient claims) defined by International Classification of Diseases, Ninth Revision, Clinical Modification codes. In the study population of 167 275 patients with AF (38.0% women and 62.0% men; mean [SD] age, 68.9 [12.5] years), a total of 817 hip fractures, 2013 hospitalized fractures, and 7294 total fractures occurred during a mean (SD) follow-up of 16.9 (13.7) months. In multivariable-adjusted, propensity score–matched Cox proportional hazards regression models, relative to new users of warfarin, new users of DOACs tended to be at lower risk of fractures requiring hospitalization (hazard ratio [HR], 0.87; 95% CI, 0.79-0.96) and all clinical fractures (HR, 0.93; 95% CI, 0.88-0.98), whereas the association with hip fractures (HR, 0.91; 95% CI, 0.78-1.07) was not statistically significant. When comparing individual DOACs with warfarin, the strongest findings were for apixaban (HR for hip fracture, 0.67 [95% CI, 0.45-0.98]; HR for fractures requiring hospitalization, 0.60 [95% CI, 0.47-0.78]; and HR for all clinical fractures, 0.86 [95% CI, 0.75-0.98]). In subgroup analyses, DOACs appeared more beneficial among patients with AF who also had a diagnosis of osteoporosis than among those without a diagnosis of osteoporosis. In this real-world population of 167 275 patients with AF, use of DOACs—particularly apixaban—compared with warfarin use was associated with lower fracture risk. These associations were more pronounced among patients with a diagnosis of osteoporosis. Given the potential adverse effects of warfarin on bone health, these findings suggest that caution should be used when prescribing warfarin to patients with AF at high risk of fracture.

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          Osteoporosis: now and the future.

          Osteoporosis is a common disease characterised by a systemic impairment of bone mass and microarchitecture that results in fragility fractures. With an ageing population, the medical and socioeconomic effect of osteoporosis, particularly postmenopausal osteoporosis, will increase further. A detailed knowledge of bone biology with molecular insights into the communication between bone-forming osteoblasts and bone-resorbing osteoclasts and the orchestrating signalling network has led to the identification of novel therapeutic targets. Novel treatment strategies have been developed that aim to inhibit excessive bone resorption and increase bone formation. The most promising novel treatments include: denosumab, a monoclonal antibody for receptor activator of NF-κB ligand, a key osteoclast cytokine; odanacatib, a specific inhibitor of the osteoclast protease cathepsin K; and antibodies against the proteins sclerostin and dickkopf-1, two endogenous inhibitors of bone formation. This overview discusses these novel therapies and explains their underlying physiology. Copyright © 2011 Elsevier Ltd. All rights reserved.
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            Estimates of current and future incidence and prevalence of atrial fibrillation in the U.S. adult population.

            Estimates and projections of diagnosed incidence and prevalence of atrial fibrillation (AF) in the United States have been highly inconsistent across published studies. Although it is generally acknowledged that AF incidence and prevalence are increasing due to growing numbers of older people in the U.S. population, estimates of the rate of expected growth have varied widely. Reasons for these variations include differences in study design, covered time period, birth cohort, and temporal effects, as well as improvements in AF diagnosis due to increased use of diagnostic tools and health care awareness. The objective of this study was to estimate and project the incidence and prevalence of diagnosed AF in the United States out to 2030. A large health insurance claims database for the years 2001 to 2008, representing a geographically diverse 5% of the U.S. population, was used in this study. The trend and growth rate in AF incidence and prevalence was projected by a dynamic age-period cohort simulation progression model that included all diagnosed AF cases in future prevalence projections regardless of follow-up treatment, as well as those cases expected to be chronic in nature. Results from the model showed that AF incidence will double, from 1.2 million cases in 2010 to 2.6 million cases in 2030. Given this increase in incidence, AF prevalence is projected to increase from 5.2 million in 2010 to 12.1 million cases in 2030. The effect of uncertainty in model parameters was explored in deterministic and probabilistic sensitivity analyses. Variability in future trends in AF incidence and recurrence rates has the greatest impact on the projected estimates of chronic AF prevalence. It can be concluded that both incidence and prevalence of AF are likely to rise from 2010 to 2030, but there exists a wide range of uncertainty around the magnitude of future trends.
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              High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

              Adjusting for large numbers of covariates ascertained from patients' health care claims data may improve control of confounding, as these variables may collectively be proxies for unobserved factors. Here, we develop and test an algorithm that empirically identifies candidate covariates, prioritizes covariates, and integrates them into a propensity-score-based confounder adjustment model. We developed a multistep algorithm to implement high-dimensional proxy adjustment in claims data. Steps include (1) identifying data dimensions, eg, diagnoses, procedures, and medications; (2) empirically identifying candidate covariates; (3) assessing recurrence of codes; (4) prioritizing covariates; (5) selecting covariates for adjustment; (6) estimating the exposure propensity score; and (7) estimating an outcome model. This algorithm was tested in Medicare claims data, including a study on the effect of Cox-2 inhibitors on reduced gastric toxicity compared with nonselective nonsteroidal anti-inflammatory drugs (NSAIDs). In a population of 49,653 new users of Cox-2 inhibitors or nonselective NSAIDs, a crude relative risk (RR) for upper GI toxicity (RR = 1.09 [95% confidence interval = 0.91-1.30]) was initially observed. Adjusting for 15 predefined covariates resulted in a possible gastroprotective effect (0.94 [0.78-1.12]). A gastroprotective effect became stronger when adjusting for an additional 500 algorithm-derived covariates (0.88 [0.73-1.06]). Results of a study on the effect of statin on reduced mortality were similar. Using the algorithm adjustment confirmed a null finding between influenza vaccination and hip fracture (1.02 [0.85-1.21]). In typical pharmacoepidemiologic studies, the proposed high-dimensional propensity score resulted in improved effect estimates compared with adjustment limited to predefined covariates, when benchmarked against results expected from randomized trials.
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                Author and article information

                Journal
                JAMA Internal Medicine
                JAMA Intern Med
                American Medical Association (AMA)
                2168-6106
                November 25 2019
                Affiliations
                [1 ]Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis
                [2 ]Center for Care Delivery and Outcomes Research, Minneapolis Veterans Affairs Health Care System, Minneapolis, Minnesota
                [3 ]Department of Medicine, University of Minnesota School of Medicine, Minneapolis
                [4 ]Rollins School of Public Health, Department of Epidemiology, Emory University, Atlanta, Georgia
                Article
                10.1001/jamainternmed.2019.5679
                6902159
                31764956
                fbb4f51a-13a0-4ba0-b7cf-30a6ed0329fa
                © 2019
                History

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