Clinical Outcomes and Predictive Model of Platelet Reactivity to Clopidogrel After Acute Ischemic Vascular Events
Ischemic cardiovascular and cerebrovascular diseases are significant threats to human health, severely impacting quality of life. Antiplatelet therapy, including acetylsalicylic acid (ASA) and clopidogrel, is a standard regimen for secondary prevention of cardiovascular events. However, even with dual antiplatelet therapy (DAPT), some patients experience recurrent events. Clinical studies suggest that this recurrence may be related to high on-treatment platelet reactivity (HTPR). Previous research has indicated that HTPR is closely linked to stroke recurrence. Platelet reactivity to adenosine diphosphate (ADP) is influenced by genetic, cellular, and clinical factors. Clopidogrel, an ADP receptor antagonist, requires a two-step conversion process to become active, with the CYP2C19 enzyme playing a crucial role in its metabolism. CYP2C19 gene polymorphisms are considered significant factors in HTPR. Additionally, diabetes can cause changes in platelet morphology and function, leading to increased platelet reactivity. Despite known genetic and non-genetic factors, a significant portion of clopidogrel’s variable platelet reactivity remains unexplained, challenging the personalization of clopidogrel therapy. This study aimed to explore a predictive model of platelet reactivity to clopidogrel and its relationship with clinical outcomes.
The study included 441 patients with non-cardioembolic ischemic stroke (NCIS), coronary atherosclerosis heart disease (CAHD), or ischemic perivascular events (IPVEs) who received DAPT. Platelet reactivity was measured using light transmittance aggregometry (LTA), with HTPR defined as maximal platelet aggregation (MPA) greater than 46%. CYP2C19 loss-of-function polymorphisms were identified using DNA microarray analysis. The primary endpoint was major adverse clinical events (MACEs), and patients were followed for a median of 29 months. Baseline characteristics, including age, body mass index (BMI), diabetes history, and CYP2C19 genotype, were collected. Statistical analyses included binary logistic regression to identify risk factors, Kaplan-Meier survival curves, and log-rank tests to compare outcomes between HTPR and non-HTPR groups.
The study found that 17.2% of patients had HTPR. Logistic regression identified several predictors of HTPR: age, therapy regimen, BMI, diabetes history, and CYP2C192 or CYP2C193 variants. The area under the receiver operating characteristic (ROC) curve for the HTPR predictive model was 0.793, indicating good discrimination. Kaplan-Meier analysis showed that patients with HTPR had a higher incidence of MACEs compared to those without HTPR (21.1% vs. 9.9%). The predictive model of HTPR demonstrated useful discrimination and good calibration, suggesting its potential to predict long-term MACEs.
The study population included 441 patients, with 76 (17.2%) classified as having HTPR. Baseline characteristics revealed significant differences in gender, therapy methods, hypertension history, diabetes, BMI, plateletcrit (PCT), and cholesterol levels between HTPR and non-HTPR groups. CYP2C19 genotyping showed that 47.2% of participants carried at least one CYP2C192 allele, and 10.9% carried at least one CYP2C193 allele. The distribution of CYP2C19 phenotypes was 44.7% extensive metabolizers (EMs), 42.9% intermediate metabolizers (IMs), and 12.5% poor metabolizers (PMs).
Multivariate logistic regression analysis identified age, therapy regimen, BMI, diabetes history, and CYP2C192 or CYP2C193 variants as independent predictors of HTPR. The predictive model of HTPR was built using these risk factors, with the ROC curve showing an area under the curve (AUC) of 0.793. The model’s calibration, assessed using the Hosmer-Lemeshow test, was not significant, indicating good agreement between predicted and observed outcomes.
Clinical follow-up data were available for 96.3% of patients, with 52 experiencing MACEs during the follow-up period. The HTPR group had a higher risk of MACEs and shorter mean survival time compared to the non-HTPR group. Kaplan-Meier curves and log-rank tests confirmed significant differences in survival distribution between the two groups. However, CYP2C192 and CYP2C193 polymorphisms did not show significant differences in MACE rates.
The study confirmed that advanced age, higher BMI, regular DAPT, diabetes, and CYP2C192 or CYP2C193 carriers are significantly associated with HTPR to clopidogrel, as measured by LTA. The predictive model of HTPR demonstrated useful discrimination and good calibration, suggesting its potential to predict long-term MACEs. These findings highlight the importance of considering genetic and clinical factors in personalizing antiplatelet therapy to improve clinical outcomes in patients with acute ischemic vascular events.
Genetic factors, particularly CYP2C19 polymorphisms, play a significant role in clopidogrel response variability. CYP2C192 and CYP2C193 alleles are associated with reduced enzymatic activity, leading to higher platelet reactivity and increased thrombotic events. The study found that 47.2% of participants carried at least one CYP2C192 allele, and 10.9% carried at least one CYP2C193 allele. These genetic variations were significantly associated with HTPR, consistent with previous research. The CYP2C19 enzyme is crucial for the activation of clopidogrel, and its polymorphisms can lead to reduced formation of the active metabolite, resulting in inadequate platelet inhibition.
Non-genetic factors, such as age, BMI, and diabetes, also contribute to clopidogrel HTPR. The study identified advanced age and higher BMI as independent predictors of HTPR. Older patients may have reduced hepatic CYP450 enzyme activity, leading to decreased conversion of clopidogrel to its active metabolite. Higher BMI is associated with increased platelet reactivity and a higher prevalence of HTPR, possibly due to lower systemic exposure to the active metabolite in obese patients. Diabetes was another significant predictor of HTPR, with diabetic patients exhibiting increased platelet reactivity and a higher frequency of HTPR. Diabetes can cause changes in platelet morphology and function, leading to reduced responsiveness to clopidogrel.
Therapy regimen also influenced platelet reactivity, with patients receiving regular DAPT having a higher frequency of HTPR compared to those on intensive DAPT. The dose of clopidogrel is crucial for achieving adequate platelet inhibition, and higher loading doses can reduce the prevalence of HTPR. The study confirmed that the dose of clopidogrel is correlated with platelet reactivity, with intensive DAPT leading to lower HTPR rates.
The study’s predictive model of HTPR, based on age, therapy regimen, BMI, diabetes history, and CYP2C19 genotype, demonstrated useful discrimination and good calibration. The ROC curve showed an AUC of 0.793, indicating the model’s potential to predict HTPR accurately. The calibration plot confirmed good agreement between predicted and observed outcomes, suggesting the model’s reliability in clinical practice.
Clinical outcomes were significantly worse in patients with HTPR, who had a higher incidence of MACEs and shorter mean survival time compared to those without HTPR. The study confirmed that platelet function testing is helpful in judging clinical outcomes, with HTPR associated with increased risk of recurrent ischemic events. These findings underscore the importance of monitoring platelet reactivity and personalizing antiplatelet therapy to improve clinical outcomes in patients with acute ischemic vascular events.
In conclusion, the study identified advanced age, higher BMI, regular DAPT, diabetes, and CYP2C192 or CYP2C193 carriers as significant predictors of HTPR to clopidogrel. The predictive model of HTPR demonstrated useful discrimination and good calibration, suggesting its potential to predict long-term MACEs. These findings highlight the importance of considering genetic and clinical factors in personalizing antiplatelet therapy to improve clinical outcomes in patients with acute ischemic vascular events. Further research with expanded sample sizes is needed to validate these findings and explore additional confounding factors influencing HTPR.
doi.org/10.1097/CM9.0000000000000210
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