Management and Outcomes of Major Bleeding During Treatment With Dabigatran or Warfarin

Majeed, Abdul; Hwang, Hun Gyu; Connolly, Stuart J.

doi:10.1016/j.jvs.2014.01.026

Cited by 48 publications

(61 citation statements)

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“…Other studies have suggested that patients with DOACassociated major bleeding inherently have better outcomes than those with warfarin-associated major bleeding. 15 These differences are important to consider when comparing outcomes between onlabel and off-label reversal usage.…”

Section: Re Sults/discussionmentioning

confidence: 99%

Off‐label use of 4‐factor prothrombin complex concentrate is common despite little known benefit: A retrospective study

Scharman

Shatzel

Kim

et al. 2018

European J of Haematology

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Section: Re Sults/discussionmentioning

confidence: 99%

Off‐label use of 4‐factor prothrombin complex concentrate is common despite little known benefit: A retrospective study

Scharman

Shatzel

Kim

et al. 2018

European J of Haematology

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“…Lastly, in pooled analyses comparing major bleeding events in patients on DOACs compared to their warfarin counterparts, there have been no worse outcomes in patients on DOACs compared to warfarin, despite no dedicated DOAC reversal agents being available at the time of these trials. In fact, in some cases, the patients who bled on DOACs have even done better [7].…”

Section: Reversal Of Warfarin Era Thinkingmentioning

confidence: 99%

Reversal of warfarin era thinking

et al. 2017

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“…Subsequent clinical trials have reexamined the risk of major bleeding events. The results of these trials are equally inconclusive, with greater transfusion needs among dabigatran treated patients counterbalanced by lower intensive care stay and lower mortality [22]. We contribute to this debate by considering a real world equivalent of the simulated study above.…”

Section: Real World Study: Warfarin and Dabigatranmentioning

confidence: 99%

Hierarchical models for multiple, rare outcomes using massive observational healthcare databases

Shaddox

Ryan

Schuemie

et al. 2016

Statistical Analysis

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Clinical trials often lack power to identify rare adverse drug events (ADEs) and therefore cannot address the threat rare ADEs pose, motivating the need for new ADE detection techniques. Emerging national patient claims and electronic health record databases have inspired post-approval early detection methods like the Bayesian self-controlled case series (BSCCS) regression model. Existing BSCCS models do not account for multiple outcomes, where pathology may be shared across different ADEs. We integrate a pathology hierarchy into the BSCCS model by developing a novel informative hierarchical prior linking outcome-specific effects. Considering shared pathology drastically increases the dimensionality of the already massive models in this field. We develop an efficient method for coping with the dimensionality expansion by reducing the hierarchical model to a form amenable to existing tools. Through a synthetic study we demonstrate decreased bias in risk estimates for drugs when using conditions with different true risk and unequal prevalence. We also examine observational data from the MarketScan Lab Results dataset, exposing the bias that results from aggregating outcomes, as previously employed to estimate risk trends of warfarin and dabigatran for intracranial hemorrhage and gastrointestinal bleeding. We further investigate the limits of our approach by using extremely rare conditions. This research demonstrates that analyzing multiple outcomes simultaneously is feasible at scale and beneficial.

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Management and Outcomes of Major Bleeding During Treatment With Dabigatran or Warfarin

Cited by 48 publications

References 0 publications

Off‐label use of 4‐factor prothrombin complex concentrate is common despite little known benefit: A retrospective study

Off‐label use of 4‐factor prothrombin complex concentrate is common despite little known benefit: A retrospective study

Reversal of warfarin era thinking

Hierarchical models for multiple, rare outcomes using massive observational healthcare databases

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