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. 2019 May 5;132(9):1053-1062.
doi: 10.1097/CM9.0000000000000210.

Clinical outcomes and predictive model of platelet reactivity to clopidogrel after acute ischemic vascular events

Qi Ma  1   2 , Guang-Zhong Chen  3 , Yu-Hu Zhang  4 , Li Zhang  2 , Li-An Huang  1
Affiliations

Clinical outcomes and predictive model of platelet reactivity to clopidogrel after acute ischemic vascular events

Qi Ma et al. Chin Med J (Engl). .

Abstract

Background: High on-treatment platelet reactivity (HTPR) has been suggested as a risk factor for patients with ischemic vascular disease. We explored a predictive model of platelet reactivity to clopidogrel and the relationship with clinical outcomes.

Methods: A total of 441 patients were included. Platelet reactivity was measured by light transmittance aggregometry after receiving dual antiplatelet therapy. HTPR was defined by the consensus cutoff of maximal platelet aggregation >46% by light transmittance aggregometry. CYP2C19 loss-of-function polymorphisms were identified by DNA microarray analysis. The data were compared by binary logistic regression to find the risk factors. The primary endpoint was major adverse clinical events (MACEs), and patients were followed for a median time of 29 months. Survival curves were constructed with Kaplan-Meier estimates and compared by log-rank tests between the patients with HTPR and non-HTPR.

Results: The rate of HTPR was 17.2%. Logistic regression identified the following predictors of HTPR: age, therapy regimen, body mass index, diabetes history, CYP2C192, or CYP2C193 variant. The area under the curve of receiver operating characteristic for the HTPR predictive model was 0.793 (95% confidence interval: 0.738-0.848). Kaplan-Meier analysis showed that patients with HTPR had a higher incidence of MACE than those with non-HTPR (21.1% vs. 9.9%; χ = 7.572, P = 0.010).

Conclusions: Our results suggest that advanced age, higher body mass index, treatment with regular dual antiplatelet therapy, diabetes, and CYP2C192 or CYP2C193 carriers are significantly associated with HTPR to clopidogrel. The predictive model of HTPR has useful discrimination and good calibration and may predict long-term MACE.

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Figures

Figure 1
Figure 1
Distribution of non-HTPR and HTPR patients in different risk factor groups. (A) HTPR between therapy groups. (B) HTPR between BMI groups. (C) HTPR between non-diabetics and diabetics groups. (D) HTPR between CYP2C19 variation groups.BMI: Body mass index; DAPT: Dual antiplatelet therapy; HTPR: High on-treatment platelet reactivity.
Figure 2
Figure 2
ROC curve analysis to the HTPR predictive model. AUC: Area under curve; CI: Confidence interval; HTPR: High on-treatment platelet reactivity; ROC: Receiver operating characteristic.
Figure 3
Figure 3
Calibration of the model to predicting HTPR assessed by Hosmer-Lemeshow goodness-of-fit test. HTPR: High on-treatment platelet reactivity.
Figure 4
Figure 4
Kaplan-Meier analysis of incidence of MACE, according to platelet function analysis. HTPR: High on-treatment platelet reactivity; MACE: Major adverse clinical events.
See this image and copyright information in PMC

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