Drug resistance and secondary treatment of ischaemic stroke: The genetic component of the response to acetylsalicylic acid and clopidogrel.

INTRODUCTION: Cerebrovascular diseases are among the leading causes of death and disability in developed countries. Acetylsalicylic acid (ASA) and clopidogrel are the most widely-used antiplatelet drugs for secondary prevention of recurrent thromboembolic events. However, there have been cases in which antiplatelet drugs did not inhibit platelet activity; this phenomenon is called resistance, and it may be modulated at the genetic level. DEVELOPMENT: Following a literature search, we reviewed the current state of antiplatelet therapy and covered the different types of resistance to antiplatelet therapy, how it is measured, current problems and limitations, and any genetic factors that have been associated with resistance. We mainly used the Genome Wide Association Studies in the field of ASA and clopidogrel resistance. CONCLUSIONS: We observed an association between different genetic factors and antiplatelet drug resistance as measured by platelet activity. However, there is no evident association between these genetic factors and risk of new thromboembolic events.

A parsimonious score with a free web tool for predicting disability after an ischemic stroke: the Parsifal Score.

BACKGROUND: Most of the models to predict prognosis after an ischemic stroke include complex mathematical equations or too many variables, making them difficult to use in the daily clinic. We want to predict disability 3 months after an ischemic stroke in an independent patient not receiving recanalization treatment within the first 24 h, using a minimum set of variables and an easy tool to facilitate its implementation. As a secondary aim, we calculated the capacity of the score to predict an excellent/devastating outcome and mortality. METHODS: Eight hundred and forty-four patients were evaluated. A multivariable ordinal logistic regression was used to obtain the score. The Modified Rankin Scale (mRS) was used to estimate disability at the third month. The results were replicated in another independent cohort (378 patients). The "polr" function of R was used to perform the regression, stratifying the sample into seven groups with different cutoffs (from mRS 0 to 6). RESULTS: The Parsifal score was generated with: age, previous mRS, initial NIHSS, glycemia on admission, and dyslipidemia. This score predicts disability with an accuracy of 80-76% (discovery-replication cohorts). It has an AUC of 0.86 in the discovery and replication cohort. The specificity was 90-80% (discovery-replication cohorts); while, the sensitivity was 64-74% (discovery-replication cohorts). The prediction of an excellent or devastating outcome, as well as mortality, obtained good discrimination with AUC > 0.80. CONCLUSIONS: The Parsifal Score is a model that predicts disability at the third month, with only five variables, with good discrimination and calibration, and being replicated in an independent cohort.