Clinical, Angiographic, and Genetic Factors Associated With Early Coronary Stent Thrombosis | Cardiology | JAMA | JAMA Network
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Figure 1. Study Flow
Figure 1. Study Flow
Figure 2. Percentage of Participants With Early Stent Thrombosis According to the Risk Allele Count Score
Figure 2. Percentage of Participants With Early Stent Thrombosis According to the Risk Allele Count Score

The score was calculated by adding the number of risk alleles. One point is awarded for each copy of the risk alleles CYP2C19*2 and ABCB1 3435 TT. One point is awarded when a copy of protective alleles CYP2C19*17 or ITGB3 PLA2 is absent. A homozygous carrier of both the CYP2C19*2 (2 points) and the ABCB1 3435 TT (2 points) variants who has no copies of either the CYP2C19*17 (2 points) or the ITGB3PLA2 (2 points) alleles will score an 8, the highest score. Conversely, a patient with no copies of either the CYP2C19*2 (0 points) or the ABCB1 3435 CC (0 points) variants who carries 1 copy of the CYP2C19*17 (1 point) and 2 copies of the protective ITGB3PLA2 allele (0 points) will score 1, the lowest score.
aFor 2 participants, information on a genotype was missing and the score could not be calculated.

Figure 3. Receiver Operating Characteristic Curve for Association With Early Stent Thrombosis
Figure 3. Receiver Operating Characteristic Curve for Association With Early Stent Thrombosis

The clinical model is based on nongenetic factors (type C lesion, proton pump inhibitor use, diabetes mellitus, left ventricular dysfunction <40%, percutaneous coronary intervention in acute setting, and clopidogrel loading dose), with sensitivity of 60% and specificity of 70%, for a positive likelihood ratio of 2.1 (area under the curve, 0.73; 95% CI, 0.67-0.78; P <.001).The genetic model contains CYP2C19 metabolic status, ABCB1 3435 TT genotype, and ITGB3 PLA2 polymorphism, with a sensitivity of 48% and specificity of 78%, for a positive likelihood ratio of 2.0 (area under the curve, 0.68; 95% CI, 0.62-0.74; P <.001). The combined model contains all clinical, angiographic, and genetic predictors, with a sensitivity of 67% and specificity of 79%, for a positive likelihood ratio of 3.4 (area under the curve, 0.78; 95% CI, 0.73-0.83; P <.001). The diagonal dotted line is the expected receiver operating characteristic curve for a totally random classifier.

Table 1. Baseline Characteristics
Table 1. Baseline Characteristics
Table 2. Genetic Analyses in Cases and Controls (Genotypes CYP2C19 and CYP2C9)
Table 2. Genetic Analyses in Cases and Controls (Genotypes CYP2C19 and CYP2C9)
Table 3. Genetic Analyses in Cases and Controls (Genotypes CYP2B6, CYP3A5, POR, PON1, ABCB1, and P2Y12)
Table 3. Genetic Analyses in Cases and Controls (Genotypes CYP2B6, CYP3A5, POR, PON1, ABCB1, and P2Y12)
Table 4. Genetic Analyses in Cases and Controls (Genotypes ITGB3, MTHFR, PAI1, Factor V, Prothrombin, Fibrinogen Beta Chain, and VKORC1)
Table 4. Genetic Analyses in Cases and Controls (Genotypes ITGB3, MTHFR, PAI1, Factor V, Prothrombin, Fibrinogen Beta Chain, and VKORC1)
Table 5. Stepwise Multivariable Analysis for the Combined Model
Table 5. Stepwise Multivariable Analysis for the Combined Model
Original Contribution
October 26, 2011

Clinical, Angiographic, and Genetic Factors Associated With Early Coronary Stent Thrombosis

Author Affiliations

Author Affiliations: Institut de Cardiologie, INSERM Unité Mixte de Recherche (UMR)_S 937, Pitié-Salpêtrière Hospital (Drs Cayla, O’Connor, Silvain, Vignalou, Beygui, Barthélémy, Montalescot, and Collet) and Université Pierre et Marie Curie (Paris 6), UMR_S 956 (Dr Hulot and Mr Allanic), Paris, France; Department of Cardiology, Centre Hospitalo-Universitaire (CHU) Nîmes, Nîmes, France (Dr Cayla); Faculté de Médecine, Université Montpellier 1 (Drs Cayla and Huerre), and Department of Cardiology, CHU Montpellier (Dr Huerre), Montpellier, France; Cardiovascular Research Center (Dr Hulot) and Department of Genetics and Genomic Sciences (Dr Scott), Mount Sinai School of Medicine, New York, New York; Laboratoire de Pharmacologie Médicale et Clinique, INSERM U1048, Faculté de Médecine, Université de Toulouse, and Service de Cardiologie, CHU Rangueil, Toulouse, France (Dr Pathak); and Department of Hematology-Hemostasis, Centre National de La Recherche Scientifique, UMR 6239, and Department of Cardiology (Dr de la Briolle), CHU Tours, and Faculté de Médecine, Université Francois Rabelais (Drs Gruel and de la Briolle), Tours, France.

JAMA. 2011;306(16):1765-1774. doi:10.1001/jama.2011.1529

Context Despite dual antiplatelet therapy, stent thrombosis remains a devastating and unpredictable complication of percutaneous coronary intervention (PCI).

Objective To perform a sequential analysis of clinical and genetic factors associated with definite early stent thrombosis.

Design, Setting, and Participants Case-control study conducted in 10 centers in France between January 2007 and May 2010 among 123 patients undergoing PCI who had definite early stent thrombosis and DNA samples available, matched on age and sex with 246 stent thrombosis–free controls.

Main Outcome Measure Accuracy of early stent thrombosis prediction by 23 genetic variants.

Results Among the 23 genetic variants investigated in 15 different genes, the significant determinants of early stent thrombosis were CYP2C19 metabolic status (adjusted odds ratio [OR], 1.99; 95% CI, 1.47-2.69), ABCB1 3435 TT genotype (adjusted OR, 2.16; 95% CI, 1.21-3.88), and ITGB3 PLA2 carriage (adjusted OR, 0.52; 95% CI, 0.28-0.95). Nongenetic independent correlates were acuteness of PCI (adjusted OR, 3.05; 95% CI, 1.54-6.07), complex lesions (American College of Cardiology/American Heart Association type C) (adjusted OR, 2.33; 95% CI, 1.40-3.89), left ventricular function less than 40% (adjusted OR, 2.25; 95% CI, 1.09-4.70), diabetes mellitus (adjusted OR, 1.82; 95% CI, 1.02-3.24), use of proton pump inhibitors (adjusted OR, 2.19; 95% CI, 1.29-3.75), and higher clopidogrel loading doses (adjusted OR, 0.73; 95% CI, 0.57-0.93). The discriminative accuracy of the clinical-only model was similar to that of a genetic-only model (area under the curve, 0.73 [95% CI, 0.67-0.78] vs 0.68 [95% CI, 0.62-0.74], respectively; P = .34). A combined clinical and genetic model led to a statistically significant increase in the discriminatory power of the model compared with the clinical-only model (area under the curve, 0.78 [95% CI, 0.73-0.83] vs 0.73 [95% CI, 0.67-0.78]; P = .004).

Conclusions This case-control study identified 3 genes (CYP2C19, ABCB1, and ITGB3) and 2 clopidogrel-related factors (loading dose and proton pump inhibitors) that were independently associated with early stent thrombosis. Future studies are needed to validate the prognostic accuracy of these risk factors in prospective cohorts.

Percutaneous coronary intervention (PCI) with stent implantation has become the standard of care for myocardial revascularization, especially in the setting of unstable coronary artery disease.1 Despite the use of dual antiplatelet therapy (DAPT; ie, aspirin and clopidogrel), which reduces cardiovascular events after PCI by more than 80%, definite stent thrombosis remains a concern. Although it is a rare complication, occurring in 0.5% to 4% of patients within the first year of PCI,2 the majority of stent thromboses occur in the first month and are defined as early stent thrombosis. It is a devastating complication, with a mortality rate up to 40% and large myocardial infarction in roughly 80% of survivors who remain exposed to frequent recurrence.2,3 Clinical and angiographic correlates of stent thrombosis have been well described, of which premature interruption of DAPT is the most important risk factor for early stent thrombosis.2,4 In addition, comorbidities, the initial clinical presentation, diabetes, stent undersizing or underexpansion, complex and/or bifurcation lesions, and coronary dissection have been associated with stent thrombosis.2

Much attention recently has been focused on patient response to clopidogrel, with a strong relationship between high on-clopidogrel platelet reactivity and stent thrombosis despite stringent adherence to DAPT.5 Clopidogrel is a prodrug that requires metabolic activation to generate its active thiol metabolite. It repeatedly has been shown that variable or insufficient clopidogrel active metabolite generation is the primary explanation for poor responsiveness to clopidogrel.5 The reduced-function genetic variants in the hepatic cytochrome P450 2C19 (CYP2C19) gene have been identified as the most prominent contributors to this variability.6 There is a strong link among CYP2C19 genetic polymorphisms, decreased clopidogrel responsiveness as measured by platelet function assays, and adverse clinical outcomes.7-10 Other genes also might influence clopidogrel absorption and metabolism,11-13 but their relative contribution to the occurrence of stent thrombosis has been rarely explored. Additionally, a number of variants in genes encoding key factors of the coagulation/fibrinolytic systems and platelet receptor function have been associated with a higher risk of arterial thrombotic events, but their relation to stent thrombosis remains unknown.14-17

Our goal was to perform a case-control study using a candidate gene approach, testing all genetic variants previously associated with clopidogrel pharmacogenetics and arterial thrombosis to determine their relative contribution to definite early stent thrombosis in addition to classic clinical and angiographic factors.

Study Design

The Online Assistance for Stent Thrombosis (ONASSIST) project is a French nationwide Web registry of patients with definite stent thrombosis following stent implantation. This online interactive approach allows submission of clinical cases from any hospital followed by medical expert review.18 In each participating center, a cardiologist is responsible for the collection and submission of all stent thrombosis cases. Accuracy of case ascertainment is obtained by review of each Web-based case record by 3 interventional cardiologists who pay particular attention to discontinuation of aspirin or clopidogrel. Only data on definite stent thromboses not related to drug interruption are collected. This investigator-initiated registry is led and partially supported by a nonprofit academic research organization, ACTION (Allies in Cardiovascular Trials, Initiatives and Organized Networks), at Pitié-Salpêtrière Hospital, Paris. The workflow of the Web site is customized to provide online assistance with regard to on-treatment platelet reactivity and pharmacogenetic profiles. A total of 10 centers performing about 18 500 PCIs participated between January 2007 and May 2010. The protocol was approved by the Pitié-Salpêtrière University Hospital Ethics Committee and the study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants.

Participant Selection

Patients aged 18 years or older with an angiographically documented stent thrombosis according to Academic Research Consortium definitions19 were enrolled. Angiographic definite stent thrombosis consisted of partial or complete occlusion within a previously implanted stent and evidence of new thrombus (in the stent or in the 5 mm proximal or distal to the stent) associated with at least 1 of the following: ischemic symptoms, ischemic electrocardiographic changes, or elevation of biomarkers. Among early stent thromboses (≤30 days), acute stent thrombosis occurred within 24 hours after stent implantation and subacute stent thrombosis occurred more than 24 hours but no more than 30 days after stent implantation. Control participants were recruited in the same participating centers, were identified in the local PCI database, had to be receiving DAPT, had at least 6 weeks of follow-up without history of stent thrombosis, and had available genetic samples. For the case-control comparison, a ratio of 1 stent thrombosis case to 2 age- and sex-matched controls was used.

Baseline clinical, angiographic, and procedural characteristics of case and control participants were collected. Ancestry and region of birth were self-identified. Coronary angiograms of both case and control participants were reviewed by 2 independent interventional cardiologists (G.C., J.S.H., J.S., F.B., O.B., or G.M.). In addition, a dedicated questionnaire allowing an accurate description of the use of antiplatelet therapy within the 6 weeks prior to the occurrence of stent thrombosis was used. Partial antiplatelet therapy interruption was defined as clopidogrel or aspirin cessation for at least 3 consecutive days within 14 days prior to stent thrombosis. Complete interruption was defined as cessation of both aspirin and clopidogrel for at least 3 consecutive days within 14 days prior to stent thrombosis. No biological analyses were attempted if there was any evidence of treatment discontinuation. Definite stent thrombosis cases that occurred after partial or complete interruption of DAPT were excluded from the current analysis.


Genomic DNA of case and control participants was extracted from peripheral blood leukocytes by standard procedures (Puregene DNA isolation kit, Merck Eurolab). Genotyping was performed using TaqMan Validated SNP assays with the 7900HT Sequence Detection System (Applied Biosystems). All participants had genotyping for the common CYP2C19*2 (rs4244285) loss-of-function allele as well as CYP2C19*3 (rs4986893), CYP2C19*4 (rs28399504), CYP2C19*5 (rs56337013), CYP2C19*6 (rs72552267), CYP2C19*17 (rs12248560), CYP2C9*2 (rs1799853), CYP2C9*3 (rs1057910), CYP2B6*5 (rs3211371), CYP2B6*9 (rs3745274), CYP3A5*3 (rs776746), POR*28 (rs1057868), PON1 Q192R (rs662), PON1 L55M (rs854560), ABCB1 C3435T (rs1045642), P2Y12 (rs2046934), ITGB3 (rs5918), MTHFR (rs1801133), PAI1 (rs1799889), factor V (rs6025), prothrombin G20210A (rs1799963), fibrinogen beta chain (rs1800790), and VKORC1 (rs2359612), as previously described.7

CYP2C19, CYP2C9, and CYP2B6 metabolic status was determined for cases and controls based on identified polymorphisms and their effect on enzymatic function20,21 (eTable 1).

Statistical Analysis

All analyses were performed with SAS software, version 9.1 (SAS Institute Inc). All single-nucleotide polymorphisms were tested for deviation from Hardy-Weinberg equilibrium with the χ2 test. Continuous variables were expressed as means and standard deviations unless otherwise indicated and categorical variables as frequencies and percentages. P values were 2-sided with a level of statistical significance of P <.05.

The sample size calculation was based on the hypothesis of a 2-fold increase in the CYP2C19*2 allelic frequency in patients with stent thrombosis compared with controls. From our previous studies, the CYP2C19*2 allelic frequency was expected to be 17% in controls. We estimated that 120 cases matched with 2 controls each would be required to detect such a difference with a type I error risk of .05 and power of 80% (Quanto software, version 1.1, University of California, Santa Cruz).

Baseline characteristics and genotype distribution were compared between cases and controls using conditional logistic regression analyses. A univariate logistic model was used to compare all characteristics (clinical, angiographic, and genetic). Comparisons are expressed as univariate odds ratios (ORs) with 95% confidence intervals. A multivariable logistic regression analysis was then used to identify independent variables associated with the occurrence of early stent thrombosis. The following variables were considered for inclusion in the comprehensive model: baseline demographic (age, sex, body mass index, smoking, and ancestry), clinical (hypertension, diabetes mellitus, previous myocardial infarction, creatinine clearance <60 mL/min, left ventricular ejection fraction <40%, clopidogrel loading dose, and use of proton pump inhibitors [PPIs] and calcium channel blockers), and angiographic (American College of Cardiology/American Heart Association [ACC/AHA] type C lesion, number of vessels with disease, minimum stent diameter, acuteness of PCI, and post-PCI dissection) characteristics; predicted metabolic status (CYP2C19, CYP2C9, and CYP2B6); and genetic variants (CYP3A5*3, POR*28, PON1 Q192R and L55M, ABCB1 C3435T, P2Y12 H1/H2, ITGB3 T1565C, MTHFR C677T, PAI1 5G/4G, factor V G1691A, prothrombin G20210A, fibrinogen G455A, and VKORC1). Loading doses were classified as no loading dose, 150 to 300 mg, 300 to 600 mg, and more than 600 mg. CYP2C19 metabolic status was defined as rapid, extensive, intermediate, or poor according to a recent consensus definition22 (eTable 1). Inclusion in the final model was determined by a backward-stepwise technique evaluating all potential univariate variables (P < .20) to create a multivariable model containing variables with P < .05. We performed several sensitivity analyses that confirmed the models' consistency (eg, modeling using forward selection of variables, interaction analyses, and rejection of colinearity). We tested for interactions between clopidogrel loading dose and significant genotypes in all models. The main analyses were age- and sex-adjusted. Additional analyses were also restricted to the predominant ethnicity. The clinical and genetic models were built according to the same procedure. Baseline demographic, clinical, and angiographic characteristics were considered for the clinical-only model, whereas predicted metabolic status and genetic variants were considered for the genetic-only model.

For each model, the regression parameter estimates of the independent variables were calculated and used to derive 3 different weighted equations corresponding to the clinical-only, genetic-only, and comprehensive models, respectively. Nonparametric receiver operating characteristic curves were used to assess the discriminatory power of the 3 prediction algorithms to distinguish cases and controls. Pairwise comparisons of the area under the curve (AUC) were performed according to DeLong et al.23 For each model, the best cutoff that maximized the sensitivity + specificity sum was determined and gives an indication of the optimal model's sensitivity and specificity. Finally, individual scores were calculated for both cases and controls using the 3 equations. We divided the distribution of the resulting clinical-only, genetic-only, and comprehensive scores into tertiles and compared the risk of early stent thrombosis according to the tertiles for each model.

Characteristics of the Study Population

Among the 233 PCI patients with definite stent thrombosis who were referred to the ONASSIST registry between January 2007 and May 2010, 31 instances of stent thrombosis occurred after complete or partial interruption of DAPT and 33 occurred during antiplatelet monotherapy and were not considered in the analyses (Figure 1). Among the remaining 169 cases, 123 were identified as early stent thrombosis and matched according to age and sex with 246 controls.

Cases were more frequently found to be diabetic and to have impaired left ventricular function and less frequently had hypercholesterolemia (Table 1). They also presented more frequently with complex lesions (ACC/AHA type C) and acute coronary syndromes in comparison with controls. In addition, cases received significantly lower clopidogrel loading doses than controls and were more frequently receiving PPIs.

Genetic Factors Associated With Early Stent Thrombosis

Carriage of the CYP2C19*2 loss-of-function allele was more frequent in cases than in controls (48.8% [n = 60] vs 27.4% [n = 67], respectively; unadjusted OR, 2.53; 95% CI, 1.61-3.97) (Table 2, Table 3, and Table 4), including a 7-fold higher prevalence of *2/*2 homozygotes (16.3% [n = 20] vs 2.5% [n = 6]; unadjusted OR, 7.73; 95% CI, 3.02-19.82). Of note, carriage of the CYP2C19*17 gain-of-function allele was significantly less frequent in cases than in controls (20.4% [n = 25] vs 32.7% [n = 80]; unadjusted OR, 0.53; 95% CI, 0.31-0.88). Consequently, the CYP2C19 metabolic status distribution was significantly different between cases and controls (eFigure 1).

Of interest, cases were more frequently ABCB1 3435 TT homozygotes vs CT/CC (31.7% ([n = 39] vs 18.8% [n = 46]; unadjusted OR, 2.01; 95% CI, 1.22-3.30) and less frequently carriers of the ITGB3 PLA2 polymorphism (16.4% [n = 20] vs 28.2% [n = 69]; unadjusted OR, 0.50; 95% CI, 0.29-0.87) than controls. None of the other genetic variants that have been shown to interfere with clopidogrel pharmacology (CYP2B6, CYP2C9, CYP3A5, POR, PON1, and P2Y12) or with thrombotic disorders (MTHFR, PAI1, factor V, prothrombin, fibrinogen beta chain, and VKORC1) were associated with stent thrombosis.

To further evaluate the association between early stent thrombosis and combination of genetic determinants, a risk allele count score was developed to determine whether there was a gene-dose effect in CYP2C19, ABCB1, or ITGB3 variant allele carriers. As shown in Figure 2, we identified a gradual increase in the risk of early stent thrombosis according to the number of risk alleles carried by patients.

Independent Factors Associated With Early Stent Thrombosis

Multivariable logistic regression analyses were performed to identify which clinical, angiographic, and genetic variables were independently associated with the occurrence of early stent thrombosis (Table 5). Nongenetic independent correlates of early stent thrombosis were acuteness of PCI (adjusted OR, 3.05; 95% CI, 1.54-6.07), complex lesions (ACC/AHA type C) (adjusted OR, 2.33; 95% CI, 1.40-3.89), left ventricular function less than 40% (adjusted OR, 2.25; 95% CI, 1.09-4.70), diabetes mellitus (adjusted OR, 1.82; 95% CI, 1.02-3.24), use of PPIs (adjusted OR, 2.19; 95% CI, 1.29-3.75), and higher clopidogrel loading doses (adjusted OR, 0.73; 95% CI, 0.57-0.93). Among the genetic factors, predicted CYP2C19 metabolic status (rapid, extensive, intermediate, or poor) was a major determinant of early stent thrombosis (adjusted OR, 1.99; 95% CI, 1.47-2.69), along with ABCB1 3435 TT genotype (adjusted OR, 2.16; 95% CI, 1.21-3.88) and ITGB3 PLA2 carriage (adjusted OR, 0.52; 95% CI, 0.28-0.95). There was a gradual risk increase according to CYP2C19 metabolic status, the lowest being associated with rapid metabolic and the highest with poor metabolic status.

Similar results were found when restricting the analyses to white individuals, the overwhelming majority in our study population. No significant interactions were identified between the genetic variants and each of the independent clinical and angiographic factors. Notably, we did not find any significant interactions between clopidogrel loading doses and CYP2C19, ABCB1, or ITGB3 (P = .46, P = .84, and P = .63 for interaction, respectively). There were also no significant interactions in the CYP2C19, ABCB1, and ITGB3 genetic variants.

Performance of Different Models Associated With Early Stent Thrombosis

In addition to the combined (clinical and genetic) model, a model including only clinical and angiographic characteristics (clinical-only model) and a model including CYP2C19 metabolic status and ABCB1 (TT vs CC or CT) and ITGB3 genotypes (genetic-only model) were built using multivariable logistic regression analyses. The regression parameter estimates of the independent variables were used to derive 3 different weighted equations as defined in eTable 2. We then compared the predictive performance of these models by receiver operating characteristic curve analyses.

We found that the clinical-only and genetic-only models were able to similarly discriminate between early stent thrombosis and controls (AUC, 0.73 [95% CI, 0.67-0.78] vs 0.68 [95% CI, 0.62-0.74], respectively; P = .34) (Figure 3). The combined clinical and genetic model, however, had significantly greater power to discriminate early stent thrombosis than the clinical-only model (AUC, 0.78 [95% CI, 0.73-0.83] vs 0.73 [95% CI, 0.67-0.78]; P = .004). Moreover, it had greater sensitivity and specificity (67% and 79%, respectively) than the clinical-only (60% and 70%, respectively) or genetic-only (48% and 78%, respectively) models. Therefore, the positive likelihood ratio (LR) was highest with the combined clinical and genetic model (LR, 3.4) vs with the clinical-only (LR, 2.1) or genetic-only (LR, 2.0) models. Patients in the highest tertile of risk using the combined clinical and genetic model had a 7-fold increased risk of early stent thrombosis vs patients in the lowest tertile (OR, 7.63; 95% CI, 4.18-13.91) (eFigure 2).


Stent thrombosis is a serious complication of coronary stenting but its low incidence makes it difficult to study in a comprehensive manner. Registry studies have identified a certain number of risk factors that may or may not be connected to each other, but, to our knowledge, no study has evaluated a range of clinical, drug, angiographic, procedural, and genetic factors of stent thrombosis. Our multicenter study suggests that a combination of 3 genotypes related to clopidogrel metabolism and platelet function (CYP2C19, ABCB1, and ITGB3) is an independent risk factor for early stent thrombosis beyond clinical and angiographic factors. We also identified 2 potentially modifiable factors of early stent thrombosis: clopidogrel loading dose and clopidogrel interaction with PPIs.

Our study adds to the understanding of the genetic profile of patients treated with clopidogrel who are at risk of early stent thrombosis. Of the 23 preselected genetic variants, 4 were found to be independently associated with the occurrence of early stent thrombosis. These nonmodifiable risk factors are highly prevalent. All variants except 1 have been shown to directly interfere with clopidogrel metabolism and are significantly associated with on-clopidogrel platelet reactivity. Notably and contrasting with a recent report,11 we found no significant association between carriage of the PON1 Q192R allele and stent thrombosis. Together with the recent demonstration that the fate of 2-oxo-clopidogrel in vivo, the clopidogrel intermediate metabolism, depends mainly on CYP enzymes and only to a minor extent on esterases such as PON1,24 this further supports the lack of association between PON1 Q192R and clinical events recently reported by other groups.25-27 In contrast, the CYP2C19*2 allele was independently associated with a high risk of stent thrombosis, and the prevalence of homozygotes was impressively high in patients with stent thrombosis and was 7-fold higher than in controls. In addition, CYP2C19*17 gain-of-function allele carriers were at lower risk of early stent thrombosis, a variant associated with higher on-treatment platelet inhibition and more bleeding complications28 and less ischemic events in the CHARISMA study.29 This further strengthens the current evidence on the predominant role of CYP2C19 in clopidogrel metabolism.

The current study confirms a significant association between carriage of the ABCB1 3435 TT (vs CC or CT) genotype and early stent thrombosis. This gene encodes a drug efflux transporter, P-glycoprotein, that modulates clopidogrel absorption. It has been previously associated with reduced clopidogrel response,13,30 but with variable clinical consequences.10,13,31 One explanation for the discrepancy may be that ABCB1 C3435T is a synonymous variant (p.Ile1145Ile) that affects protein conformation and substrate specificity,32 which may vary according to clopidogrel dosing strategies as recently shown by a greater effect of this variant at the time of clopidogrel loading vs during the maintenance phase.33 Our finding that ABCB1 C3435T carriage and low clopidogrel loading dose were independent correlates of early stent thrombosis and the lack of saturable clopidogrel intestinal absorption demonstrated by previous investigation of the effects of different loading dose strategies on clopidogrel response34-36 are not supportive of such a hypothesis. Of note, assessment of both ABCB1 C3435T and CYP2C19 shows that the 2 genes offer complementary information, and nearly three-fourths of patients with stent thrombosis had an increased genetic risk profile defined by the number of alleles at risk. Our regression models also indicate that in patients with a low clinical likelihood of stent thrombosis, the influence of genetic factors was important in predicting stent thrombosis.

The ITGB3 gene encodes for the integrin β3, a component of the glycoprotein IIb/IIIa platelet receptor, which mediates the final pathway of platelet aggregation. It is highly polymorphic, with 2 common allelic isoforms, PLA1 and PLA2. The relation of PLA2 (p.Leu59Pro) with the occurrence of acute coronary syndromes has been controversial, with a trend toward an increased risk.37 There is also consistent evidence of lower platelet inhibition in response to clopidogrel among carriers of the PLA2 allele.38-40 Our investigation shows that the PLA2 polymorphism was less frequent in patients with stent thrombosis than in controls. This appears to be a new finding contradictory with previous investigations performed in another setting. Although it may add to the accuracy of our predictive model, replication is needed.

The procedure-related independent correlates of early stent thrombosis were expected and could not be altered. We found that the only 2 potentially modifiable correlates of early stent thrombosis (clopidogrel dose and PPI use) are also major determinants of high on-clopidogrel platelet reactivity. Several studies have shown that increasing the clopidogrel dose regimen results in higher clopidogrel active metabolite isomer H4,41 overcomes high on-clopidogrel platelet reactivity,34,36,41 and reduces the risk of early stent thrombosis.42 Although the clinical effect of concomitant PPI use in clopidogrel-treated patients is more controversial, consistent evidence exists of a significant pharmacological interaction between omeprazole and clopidogrel, with a decrease in clopidogrel metabolite formation,43 leading possibly to an increase in cardiovascular risk, especially in high-risk populations, as in patients with previous stent thrombosis.8 Whether the effect of PPIs was driven by an interaction with the CYP2C19 pathway could not be evaluated because the type of PPI was not recorded. Altogether, clopidogrel dose, PPI use, and presence of CYP2C19 and ABCB1 genetic variants suggest that the level of P2Y12 antagonism achieved through clopidogrel active metabolite generation is the key factor influencing the risk of early stent thrombosis in patients receiving clopidogrel and adherent to treatment.

The overlap between the clinical and genetic regression models appears limited and no significant interaction was found. Obviously, genetic information cannot be captured by clinical or procedural characteristics. The lack of variation in the OR values of early stent thrombosis clinical predictors before and after consideration of the genetic correlates and their similar absolute values to those of clinical and angiographic correlates further suggest a similar extent of the predicted risk and a lack of correlation.

The case-control design of our investigation may have underestimated some potential confounding factors and represents a limitation per se, although it is the only suitable design to evaluate rare events like stent thrombosis and we undertook thorough data collection on all variables. Additionally, we had a limited sample size to develop our predictive models and, thus, may have overestimated the models' predictive accuracy in independent cohorts. Skewed inclusions may have occurred within our Web base case selection because we could not ensure an exhaustive selection of patients. However, we estimated that 18 500 patients underwent stent implantation in the participant centers during the study period, corresponding to a stent thrombosis prevalence of 1.26% (0.6% for early stent thrombosis), concordant with the recent literature. We acknowledge that the score now should be prospectively validated in a new independent cohort of patients. Another limitation of this type of study is that only early stent thrombosis survivors could be studied, and the genotype-phenotype relation is not known for the most severe cases, who died soon after stent thrombosis. However, we may have also underestimated the weight of the identified variants by missing the most severe cases. We also acknowledge that the last generation of drug-eluting stents, with less thrombogenic platforms, represented less than 10% of the cases. This might have potentially biased our findings, although the benefit of these new platforms is mainly driven by a reduction in late and very late stent thrombosis, issues that were beyond the scope of the current investigation.44 Stent malapposition or underexpansions are other important factors associated with stent thrombosis that were not evaluated. Platelet reactivity was not included in the stent thrombosis prediction model because it could not be systematically measured at the time of the event. Moreover, platelet function testing has its own limitations and is difficult to use on admission for emergent cases such as the stent thrombosis cases in the current multicenter study. Finally, our findings apply to white individuals, who were dominantly represented in our cohort, and the effects of different genes according to different ethnic groups may warrant dedicated studies.

In conclusion, in addition to established clinical and angiographic factors, 3 genes involved in clopidogrel metabolism and platelet receptor function (CYP2C19, ABCB1, and ITGB3) and 2 clopidogrel-related factors (loading dose and PPI use) were independently associated with early stent thrombosis. Combining genetic factors with clinical factors improved risk stratification for stent thrombosis. Whether treatment adjustment on the basis of such global risk stratification can improve the prognosis of patients undergoing PCI will require future validation in independent cohorts.

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Article Information

Corresponding Author: Gilles Montalescot, MD, PhD, Institut de Cardiologie, Bureau 236, Pitié-Salpêtrière Hospital, 47 Bd de l’Hôpital, 75013 Paris, France (

Author Contributions: Drs Hulot and Collet had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Cayla and Hulot contributed equally to this work and are considered first coauthors.

Study concept and design: Cayla, Hulot, Montalescot, Collet.

Acquisition of data: Cayla, O’Connor, Pathak, Gruel, Silvain, Vignalou, Huerre, de la Briolle, Allanic, Barthélémy, Montalescot, Collet.

Analysis and interpretation of data: Cayla, Hulot, O’Connor, Scott, Silvain, Beygui, Beygui, Barthélémy, Montalescot, Collet.

Drafting of the manuscript: Cayla, Hulot, Scott, Montalescot, Collet.

Critical revision of the manuscript for important intellectual content: Hulot, O’Connor, Pathak, Scott, Gruel, Silvain, Vignalou, Huerre, de la Briolle, Allanic, Beygui, Beygui, Barthélémy, Collet.

Statistical analysis: Cayla, Hulot, Beygui, Beygui.

Obtained funding: Montalescot, Collet.

Administrative, technical, or material support: Pathak, Scott, Gruel, Vignalou, Allanic, Montalescot, Collet.

Study supervision: Hulot, Montalescot, Collet.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Cayla reports receiving a research grant from la Fédération Française de Cardiologie; consultant fees from Abbott Vascular, AstraZeneca, CLS Behring, Daiichi Sankyo, and Eli Lilly; and lecture fees from Abbott Vascular, AstraZeneca, Biotronik, CLS Behring, Daiichi Sankyo, Eli Lilly, and Iroko Cardio. Dr Hulot reports receiving research grant support from Fondation de France, INSERM, Federation Francaise de Cardiologie, Biotronik, and Medco Research Institute; consulting fees from Biotronik and Medco Health Solutions; and lecture fees from sanofi-aventis, Daiichi Sankyo, and Eli Lilly. Dr Pathak reports receiving honoraria from BMS, AstraZeneca, and Daiichi. Dr Silvain reports that he has received research grants (to his institution) from sanofi-aventis, Daiichi Sankyo, Eli Lilly, Brahms, INSERM, Fédération Française de Cardiologie, and Société Française de Cardiologie; has served as a consultant to Daiichi Sankyo and Eli Lilly; and has received lecture fees from AstraZeneca, Daiichi Sankyo, Eli Lilly, Iroko Cardio, and Servier. Dr Montalescot reports receiving grant support from Abbott Vascular, Boston Scientific, Cordis, Eli Lilly, Fédération Française de Cardiologie, Fondation de France, Guerbet Medical, INSERM, ITC Edison, Medtronic, Pfizer, sanofi-aventis, Société Française de Cardiologie, and Stago and consulting or board fees and lecture fees from AstraZeneca, Bayer, Boehringer Ingelheim, Cardiovascular Research Foundation, Cleveland Clinic Research Foundation, Daiichi Sankyo, Duke Institute, Eli Lilly, Europa, Lead-up, GlaxoSmithKline, Institut de Cardiologie de Montreal, Menarini, Nanospheres, Novartis, Pfizer, Portola, sanofi-aventis, the Medicines Company, and the TIMI Study Group. Dr Collet reports receiving research grants from Bristol-Myers Squibb, sanofi-aventis, Eli Lilly, Guerbet Medical, Medtronic, Boston Scientific, Cordis, Stago, Fondation de France, INSERM, Fédération Française de Cardiologie, and Société Française de Cardiologie; consulting fees from sanofi-aventis, Eli Lilly, and Bristol-Myers Squibb; and lecture fees from Bristol-Myers Squibb, sanofi-aventis, Eli Lilly and AstraZeneca. No other disclosures were reported.

Funding/Support: This study was funded by ACTION, the Société Française de Cardiologie, the Fédération Française de Cardiologie, and INSERM. The ONASSIST program was also partially supported by an unrestricted grant from Eli Lilly and by a grant from the SGAM Foundation.

Role of the Sponsor: The sponsors were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.

Additional Contributions: We thank Elodie Blicq, MD, University of Tours, for her contribution to data management. Dr Blicq did not receive compensation in association with her work on this article.

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