5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Risk of hospitalised bleeding in comparisons of oral anticoagulant options for the primary treatment of venous thromboembolism

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Understanding of the comparative bleeding risks of oral anticoagulant (OAC) therapies for the primary treatment of venous thromboembolism (VTE) is limited. Therefore, among anticoagulant-naïve VTE patients, we conducted comparisons of apixaban, rivaroxaban and warfarin on the rate of hospitalised bleeding within 180 days of OAC initation. MarketScan databases for the time-period from 2011 to 2016 were used and, for each OAC comparison, new users were matched with up to 5 initiators of a different OAC. The final analysis included 83,985 VTE patients, who experienced 1,944 hospitalised bleeding events. In multivariable-adjusted Cox regression models, rate of hospitalised bleeding was lower among new users of apixaban when compared to new users of rivaroxaban [hazard ratio (95% confidence interval): 0.58 (0.41–0.80)] or warfarin [0.68 (0.50–0.92)]. Overall, the hospitalised bleeding rate was similar when comparing new users of rivaroxaban to new users of warfarin [0.98 (0.68–1.11)], though there was some suggestion that rivaroxaban was associated with lower bleeding risk among younger individuals. Findings from this large real-world population concur with results from the randomised trial which found lower bleeding risk with apixaban versus warfarin and, for the first time, reveal a lower risk of bleeding in a comparison of apixaban versus rivaroxaban.

          Related collections

          Most cited references20

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Clinical Pharmacokinetic and Pharmacodynamic Profile of Rivaroxaban

            Rivaroxaban is an oral, direct Factor Xa inhibitor that targets free and clot-bound Factor Xa and Factor Xa in the prothrombinase complex. It is absorbed rapidly, with maximum plasma concentrations being reached 2–4 h after tablet intake. Oral bioavailability is high (80–100 %) for the 10 mg tablet irrespective of food intake and for the 15 mg and 20 mg tablets when taken with food. Variability in the pharmacokinetic parameters is moderate (coefficient of variation 30–40 %). The pharmacokinetic profile of rivaroxaban is consistent in healthy subjects and across a broad range of different patient populations studied. Elimination of rivaroxaban from plasma occurs with a terminal half-life of 5–9 h in healthy young subjects and 11–13 h in elderly subjects. Rivaroxaban produces a pharmacodynamic effect that is closely correlated with its plasma concentration. The pharmacokinetic and pharmacodynamic relationship for inhibition of Factor Xa activity can be described by an E max model, and prothrombin time prolongation by a linear model. Rivaroxaban does not inhibit cytochrome P450 enzymes or known drug transporter systems and, because rivaroxaban has multiple elimination pathways, it has no clinically relevant interactions with most commonly prescribed medications. Rivaroxaban has been approved for clinical use in several thromboembolic disorders.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Real-World Evidence and Real-World Data for Evaluating Drug Safety and Effectiveness

                Bookmark

                Author and article information

                Journal
                British Journal of Haematology
                Br J Haematol
                Wiley
                0007-1048
                1365-2141
                November 18 2018
                June 2019
                March 28 2019
                June 2019
                : 185
                : 5
                : 903-911
                Affiliations
                [1 ]Division of Epidemiology & Community Health School of Public Health University of Minnesota Minneapolis MN USA
                [2 ]Division of Hematology/Oncology Department of Medicine & Department of Pathology and Laboratory Medicine Larner College of Medicine at the University of Vermont Burlington VT USA
                [3 ]Chronic Disease Research Group Hennepin Healthcare Research Institute Minneapolis MN USA
                [4 ]Department of Pharmaceutical Care and Health Systems College of Pharmacy University of Minnesota Minneapolis MN USA
                [5 ]Department of Epidemiology Rollins School of Public Health Emory University Atlanta GA USA
                Article
                10.1111/bjh.15857
                6536346
                30919942
                1a1dd9ed-a193-4992-b12d-51f1db5891db
                © 2019

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

                History

                Comments

                Comment on this article