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  • The CRUSADE Can Rapid Risk Stratification of Unstable Angina

    2018-10-25

    The CRUSADE (Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes with Early Implementation of the ACC/AHA Guidelines) bleeding score has been recently introduced to predict bleeding in non-STEMI patients (Subherwal et al., 2009). A patient\'s CRUSADE Bleeding Score equals the sum of the weighted scores for the independent predictors (female sex, history of diabetes, peripheral vascular disease), admission clinical variables (heart rate, systolic blood pressure, signs of CHF), and admission laboratory values (hematocrit, calculated creatinine clearance), and ranged (1–100 points). Originally, CRUSADE considers likelihood of having an in-hospital aromatase inhibitors early major bleeding event. Later research validated CRUSADE durability to 30days, and even 1-year hemorrhagic risks, and expanded non-STEMI cohort to all post-PCI patients on DAPT (e.g. Al-Daydamony & Farag, 2016; Li et al., 2016). Indeed, low residual platelet reactivity while on DAPT may be linked to greater bleeding risks (Brar et al., 2011), however, the quality large uniformed datasets matched with CRUSADE are still lacking. We assessed simultaneous admission CRUSADE score with platelet reactivity for predicting major bleeding in a large cohort of post-stenting patients of Korean descent.
    Methods
    Results The baseline demographics and clinical characteristics of the entire patient pool dependent on experiencing bleeding event are presented in Table 1. Background clinical variables and admission biomarkers were distributed differently depended heavily on future bleeding events. In fact, patients experienced bleeding were older, more frequent females, and treated with newer P2Y12 inhibitors (prasugrel and ticagrelor) than after clopidogrel. Delayed bleeding risks were also associated with diabetes, hypertension, and prior stroke. Among acute coronary syndromes, NSTEMI and STEMI patients, but not unstable aromatase inhibitors were linked with more bleeding. Admission biomarkers were not particularly useful for dichotomization, with the exception of diminished glomerular filtration, which was lower in those who bleed. Both residual platelet reactivity and CRUSADE score were higher in patients who experienced bleeding event at 30-days, but not significantly different for 1-year bleeding when compared with no bleeding cohort. Additional statistical considerations, including assessing area under the curve are presented in Table 2. Importantly, multivariate adjusted model revealed that CRUSADE score was superior to platelet reactivity values for 30-days bleeding events. The distribution of BARC classification bleeding scores in presented in Fig. 1. The curve analyses (Fig. 2), and spread of platelet reactivity and CRUSADE scores dependent on bleeding are presented in Fig. 3A and B respectfully.
    Discussion There are four most important findings which can be yielded from the index study. First, major bleeding events on DAPT are much more common in the real-life setting than reported in the published randomized trials, at least in East Asians. Second, the CRUSADE score was superior to assessing residual platelet reactivity while on DAPT for predicting bleeding. Third, CRUSADE may be successfully applied not only for predicting in-hospital bleeding, as originally designed, but expanded for the entire first month post-stenting including many outpatients. Finally, while such predictions at admission for index ACS were quite reliable for 30-days hemorrhages, but both admission platelet testing or CRUSADE were not useful for the delayed 1-year risks. Recently, there was an explosion of publications regarding the monitoring of platelet activity concerning the impact of determined cut-off values on clinical outcomes, especially in patients with acute coronary syndromes treated invasively (e.g. Brar et al., 2011; Parodi et al., 2011; Breet et al., 2010; Guo et al., 2016). The main objective of these studies was to define the incidence of thrombotic occlusions or bleeding by linking such adverse events with residual platelet reactivity. Some evidence emerged lately assessing the risk of bleeding and specific cut-off values for its predictability in patients after acute coronary syndromes (Généreux et al., 2015; Aradi et al., 2015), and those undergoing heart surgery (Mishra et al., 2015; Kuliczkowski et al., 2015). The data on applying CRUSADE success are somewhat mixed for two main reasons. First, patients differ substantially, somewhat neglecting that this useful score was designed exclusively for non STEMI cohort predicting very early in-hospital major bleeding. Second, there are over dozen current bleeding classifications, and their inventors may be biased in promoting their own scales at expense of other useful algorithms. (Biancari et al., 2017). Some other integrative models, such as HASBLED are much more simple than CRUSADE, and unclear how they may be implemented for the similar delayed approach to pick up either bleeding or adverse thrombotic events (Hsieh et al., 2017). Expanding original CRUSADE applicability beyond exclusive non STEMI patients (Subherwal et al., 2009) to the entire post-ACS pool is also important, especially considering similar to our data yielded from Egyptian patients (Al-Daydamony & Farag, 2016). Another interesting study suggests some benefit of combining CRUSADE with platelet activity testing for yielded more accurate predictive value for 1-year bleeding risk, which was not achieved in our study. (Li et al., 2016). Overall, the available evidence suggests that mainstream use of platelet analyzers may be less reliable than clinical models such as CRUSADE to assess individual bleeding risk, and should not be currently recommended (Reed et al., 2015; Lordkipanidzé et al., 2008) what is in full agreement with the index data. Our results are also in agreement with another elegant study suggesting that both conventional aggregometry and VerifyNow tests were not particularly useful to identify patients at higher risk of bleeding (Fattorutto et al., 2003). Finally, (Garay et al., 2016) indicate the special difficulties in delayed bleeding prediction matching well with the index data. That message is particularly critical since late catastrophic hemorrhages are usually the most deadly, unexpected, and hard to prevent.