AngioJet rheolytic thrombectomy in patients with thrombolysis in myocardial infarction thrombus grade 5: an observational study

The aim of this study was to evaluate the effectiveness and safety of AngioJet rheolytic thrombectomy among patients with high thrombus burden. Routine manual thrombus aspiration in patients with ST-segment elevation myocardial infarction (STEMI) does not improve clinical outcomes and was associated with an increased rate of stroke. However, the safety of mechanical thrombus aspiration is still unknown. This was a retrospective, single-center study involving 621 patients with Thrombolysis In Myocardial Infarction thrombus grade 5. The primary outcome was the composite of major adverse cardiovascular events (MACE) within 12 months. The safety outcome was stroke within 1-year. Propensity matching score was calculated due to the significant baseline differences between the AngioJet rhelytic thrombectomy group and the routine treatment group. AngioJet rheolytic thrombectomy was performed in 117 patients. After propensity-score matching, there was no significant difference both in the incidence of MACE (11.1% vs 17.9%, hazard ratio, 1.641; 95% confidence interval [CI] 0.822 to 3.277, p = 0.161) and the incidences of stroke (1.7% vs 2.6%, hazard ratio 1.522; 95% confidence interval [CI] 0.254 to 9.107, p = 0.646) between two groups at 1-year follow-up. In patients with Thrombolysis In Myocardial Infarction thrombus grade 5, AngioJet rheolytic thrombectomy did not improve clinical outcomes at 1 year. However, AngioJet rheolytic thrombectomy did not increase the risk of stroke in patients with high thrombus burden.

Thrombus extraction catheters vs. angiojet rheolytic thrombectomy in thrombotic lesions/SV grafts

Primary percutaneous coronary intervention, (pPCI), of native coronaries and saphenous vein grafts (SVGs), is the recommended reperfusion strategy for STEMI, and an early invasive approach is recommended for high risk patients with UA/NSTEMI. Although PCI effectively restores flow in the infarct related artery/culprit vessel in both situations, myocardial perfusion often remains suboptimal due to microvascular obstruction, partly attributed to distal embolization of thrombus. Hence, thrombectomy (manual or mechanical), prior to stenting may further reduce hard clinical end points in patients with ACS. This article discusses accumulated evidence regarding the safety and effectiveness of thrombectomy in culprit native coronaries and SVGs in such patients, as well as possible strategies for maximizing its benefits relative to the size of the thrombotic burden.

Utility of Thrombectomy in Primary Percutaneous Coronary Intervention

Thrombectomy in the setting of primary percutaneous coronary intervention allows for improved macrovascular and microvascular perfusion, possible limitation of infarct size, and the preservation of left ventricular function and myocardial viability. The beneficial tissue level effects of thrombectomy have translated into an improvement in cardiovascular mortality. A variety of thrombectomy devices are currently available, including aspiration thrombectomy catheters and rheolytic catheters. A review of the various types of thrombectomy devices available, clinical evidence for their use, clinical pearls for use, and device troubleshooting are presented.

Bayesian hierarchical models combining different study types and adjusting for covariate imbalances: a simulation study to assess model performance

BACKGROUND

Bayesian hierarchical models have been proposed to combine evidence from different types of study designs. However, when combining evidence from randomised and non-randomised controlled studies, imbalances in patient characteristics between study arms may bias the results. The objective of this study was to assess the performance of a proposed Bayesian approach to adjust for imbalances in patient level covariates when combining evidence from both types of study designs.

METHODOLOGY/PRINCIPAL FINDINGS

Simulation techniques, in which the truth is known, were used to generate sets of data for randomised and non-randomised studies. Covariate imbalances between study arms were introduced in the non-randomised studies. The performance of the Bayesian hierarchical model adjusted for imbalances was assessed in terms of bias. The data were also modelled using three other Bayesian approaches for synthesising evidence from randomised and non-randomised studies. The simulations considered six scenarios aimed at assessing the sensitivity of the results to changes in the impact of the imbalances and the relative number and size of studies of each type. For all six scenarios considered, the Bayesian hierarchical model adjusted for differences within studies gave results that were unbiased and closest to the true value compared to the other models.

CONCLUSIONS/SIGNIFICANCE

Where informed health care decision making requires the synthesis of evidence from randomised and non-randomised study designs, the proposed hierarchical Bayesian method adjusted for differences in patient characteristics between study arms may facilitate the optimal use of all available evidence leading to unbiased results compared to unadjusted analyses.

The importance of adjusting for potential confounders in Bayesian hierarchical models synthesising evidence from randomised and non-randomised studies: an application comparing treatments for abdominal aortic aneurysms

BACKGROUND

Informing health care decision making may necessitate the synthesis of evidence from different study designs (e.g., randomised controlled trials, non-randomised/observational studies). Methods for synthesising different types of studies have been proposed, but their routine use requires development of approaches to adjust for potential biases, especially among non-randomised studies. The objective of this study was to extend a published Bayesian hierarchical model to adjust for bias due to confounding in synthesising evidence from studies with different designs.

METHODS

In this new methodological approach, study estimates were adjusted for potential confounders using differences in patient characteristics (e.g., age) between study arms. The new model was applied to synthesise evidence from randomised and non-randomised studies from a published review comparing treatments for abdominal aortic aneurysms. We compared the results of the Bayesian hierarchical model adjusted for differences in study arms with: 1) unadjusted results, 2) results adjusted using aggregate study values and 3) two methods for downweighting the potentially biased non-randomised studies. Sensitivity of the results to alternative prior distributions and the inclusion of additional covariates were also assessed.

RESULTS

In the base case analysis, the estimated odds ratio was 0.32 (0.13,0.76) for the randomised studies alone and 0.57 (0.41,0.82) for the non-randomised studies alone. The unadjusted result for the two types combined was 0.49 (0.21,0.98). Adjusted for differences between study arms, the estimated odds ratio was 0.37 (0.17,0.77), representing a shift towards the estimate for the randomised studies alone. Adjustment for aggregate values resulted in an estimate of 0.60 (0.28,1.20). The two methods used for downweighting gave odd ratios of 0.43 (0.18,0.89) and 0.35 (0.16,0.76), respectively. Point estimates were robust but credible intervals were wider when using vaguer priors.

CONCLUSIONS

Covariate adjustment using aggregate study values does not account for covariate imbalances between treatment arms and downweighting may not eliminate bias. Adjustment using differences in patient characteristics between arms provides a systematic way of adjusting for bias due to confounding. Within the context of a Bayesian hierarchical model, such an approach could facilitate the use of all available evidence to inform health policy decisions.