An Analysis of the Predictors of Major Bleeding After Transcatheter Aortic Valve Transplantation Using the National Inpatient Sample (2015–2018)

Abstract

BackgroundTranscatheter aortic valve replacement (TAVR) is now a common procedure to treat and improve quality of life, clinical outcomes, and self-sufficiency in high-risk patients with aortic stenosis, and its use has been expanding rapidly in younger and low-risk populations. The aim of this study was to evaluate the outcomes, trends, and predictors of major bleeding in patients undergoing TAVR. MethodologyWe utilized the National Inpatient Sample (NIS) data from the year 2015 to 2018. International Classification of Disease 10 codes were utilized to extract data. Baseline characteristics were compared using Pearson's chisquare test for categorical variables and independent samples t-test for continuous variables. A multivariable logistic regression model was used to evaluate the predictors of major bleeding. Propensity matching was done for adjusted analysis to compare outcomes in TAVR with and without major bleeding. The outcomes of interest in this study were (1) predictors of major bleeding after TAVR; (2) in-hospital mortality; and (3) resource utilization in terms of cost and length of stay. ResultsA total of 34,752 weighted hospitalizations for TAVR were included in the analysis. Of the patients undergoing the procedure, 2,294 (6.6%) had a major bleed while 32,458 (93.3%) did not. At baseline, patients with coagulopathy (odds ratio [OR]: 2.03; 95% confidence interval [

Bleeding risk differences after TAVR according to the ARC-HBR criteria: insights from SCOPE 2

“…In the present study, approximately 7% of patients who underwent TAVR had BARC 3 or 5 type bleeding at one year, which is consistent with recent reports [5,7]. A previous study reported that life-threatening bleeding after TAVR, as defined by the VARC criteria, occurred in approximately 15 to 20% of TAVR procedures [22].…”

“…Major bleeding or vascular complications have decreased as TAVR technology evolves into smaller device and sheath sizes [22,23] and bleeding complications are inconsistent in the early literature [24]. However, major bleeding is still associated with a three-fold increase in one-year mortality following TAVR [5][6][7]. Patients referred to TAVR are elderly, frail and at risk for both bleeding and ischemic complications [7,17,25].…”

“…Since the late 2000's, TAVR has led to constant improvement in clinical outcomes with the development of techniques and technological ameliorations along with increased operator experience. Although less prevalent compared with SAVR, the risk of major bleeding after TAVR has been estimated to be as high as approximately 6% and is associated with a three-fold increase in one-year mortality [5][6][7].…”

“…The VARC-3 consensus provided an overview of risk assessment after TAVR that included the definitions of bleeding, but the conditions that increased this risk were not the subject of this initiative [9]. Some investigations based on local series or national registries aimed to evaluate the predictors and outcomes of major bleeding in TAVR patients [7,10]. Peripheral vascular disease, end-stage renal disease, and coagulopathy have been identified to increase the rates of major bleeding.…”

Strategies for Reducing Vascular and Bleeding Risk for Percutaneous Left Ventricular Assist Device-supported High-risk Percutaneous Coronary Intervention

“…A recent National Inpatient Sample analysis of patients undergoing TAVR found that end-stage renal disease and coagulopathy were the most significant predictors of major bleeding. 21 A large, German singlecentre analysis of patients undergoing TAVR with third-generation devices, as well as the Perclose ProGlide ® system (Abbott Vascular, Chicago, IL, USA), found that higher sheath-to-femoral-artery ratio and more than two vessel entries with large-bore sheaths were significant predictors of access-related vascular complications (p<0.001 and p=0.029, respectively). 22 Recently, a TAVR-specific bleeding risk model was developed and validated with the patient populations of two large European registries; in this study, machine learning selected six variables to generate a bleeding risk score: blood haemoglobin, serum iron, creatinine clearance, common femoral artery (CFA) diameter, oral anticoagulation and dual antiplatelet therapy.…”