• aggregation;
  • clopidogrel;
  • clopidogrel resistance;
  • coronary artery disease;
  • platelets


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure of Conflict of Interests
  9. References

Summary. Background: Recent studies suggest a high interindividual variability of response to clopidogrel associated with adverse cardiovascular outcome. Different clinical factors are considered to influence a persistent residual platelet aggregation (RPA) despite conventional antiplatelet therapy.

Objectives: To investigate clinical factors that affect RPA after 600-mg clopidogrel loading in a large unselected cohort of patients with symptomatic CAD.

Methods: The study population included a consecutive cohort of 1092 patients treated with coronary stenting for stable angina and acute coronary syndromes (ACS). Residual platelet activity was assessed by ADP (20 μmol L−1)-induced platelet aggregation ≥ 6 h after LD. Eleven clinical factors were included in the primary analysis.

Results: In multivariate regression analysis increased RPA was significantly influenced by ACS, reduced LV-function, diabetes mellitus, renal failure (creatinine > 1.5 mg dL−1), and age > 65 years. In a factor-weighed model the risk for high RPA increased with higher score levels (OR for patients with a score of 1–3, 1.21, 95% CI 0.7–2.1; score 4–6, OR 2.0, 95% CI 1.17–3.5; = 0.01; score 7–9, OR 3.3, 95% CI 1.8–6.0). During a 30-day follow-up the incidence of major adverse events was higher in patients with RPA in the upper tertile (4.8% vs. 2.5% in the 2nd and 1.5% in the 1st tertile; < 0.05).

Conclusions: The PREDICT score provides a good tool to estimate residual platelet activity after clopidogrel LD by easily available patient details. Additionally, we demonstrate its association with short-term outcome. Thus, patients with a high score may benefit from intensified antiplatelet therapy by improved platelet inhibition and risk reduction for thromboischemic events.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure of Conflict of Interests
  9. References

Persistent residual platelet activity after coronary stent implantation plays a major role in the cardiovascular outcome after percutaneous coronary intervention (PCI) [1–3]. Although the benefit of additional platelet inhibition with clopidogrel in terms of risk reduction for adverse cardiovascular events has been documented by several clinical trials [4,5], a high interindividual variability of drug response to clopidogrel has been described in several reports [6–8]. Thus, attempts have been undertaken to define inadequate response to clopidogrel in various ways and to link it to the clinical prognosis. Recently, we and others have shown that patients who were identified as low responders to clopidogrel by a single post-treatment platelet function test are at increased risk for recurrent cardiovascular events [9–12] and subacute stent thrombosis [13].

Various clinical and demographic variables have been discussed that might influence response to antiplatelet therapy. The aim of the present study was to evaluate candidate variables, and thus, to establish a simple risk score that can be easily adopted by clinicians to identify patients who are at risk for persistent high residual platelet activity after coronary stenting despite post-interventional dual antiplatelet therapy consisting of aspirin and clopidogrel. The present PREDICT score may help to improve post-interventional clinical outcome through individualization of antiplatelet therapy in high-risk patients.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure of Conflict of Interests
  9. References

This study was a monocenter study conducted at the University Hospital, Tübingen, Germany. Patients admitted to our clinic due to coronary intervention for symptomatic coronary artery disease were consecutively evaluated by platelet function analysis. The study design was approved by the local ethics committee and signed informed consent was obtained from all patients. The major objective of the study was to describe clinical variables influencing residual platelet aggregation in an unselected cohort of cardiovascular patients. The primary endpoint was a high residual platelet aggregation after clopidogrel loading in cardiovascular patients receiving dual antiplatelet therapy.


From March 2005 to December 2006, 1125 patients were consecutively enrolled in this study. Relevant patient data were available for 1092 patients (97.1%). These patients were included in further analysis. The cohort consisted of unselected patients who underwent coronary stenting for symptomatic coronary artery disease (stable angina, = 529; acute coronary syndrome, = 563). Acute coronary syndrome (ACS) was diagnosed if one of the following criteria was fulfilled: unstable angina (clinical symptoms and new ECG changes, but no markers of myocardial necrosis), acute myocardial infarction with markers of myocardial necrosis (troponin or CK-MB) including ST-elevation myocardial infarction (STEMI) and non-ST elevation myocardial infarction (NSTEMI).

Inclusion criteria were age older than 18 years and willing consent. Patients with known platelet function disorders were excluded from the study. If patients received periprocedural treatment with GPIIb-IIIa inhibitors (abciximab) measurement was postponed for 1 week. A loading dose of 600 mg clopidogrel was given to all patients prior to PCI, followed by a daily dose of 75 mg for at least 3 months. Patients already on chronic clopidogrel treatment received a loading dose of 300 mg (= 155, 14.2%). All patients received 500 mg of acetylsalicylic acid (ASA) i.v. before PCI followed by ASA 100 mg day–1. The majority of patients (around 90%) were already pretreated with a chronic aspirin therapy (100 mg day–1). Unfractionated heparin was periprocedurally administered to all patients at a dosage of 70 U kg–1 body weight.

Blood sampling and platelet aggregation

Patient blood was collected at least 6 h after first administration of 600 mg clopidogrel, when maximum platelet inhibition is achieved [14,15]. In a minority of patients who were on chronic clopidogrel treatment (75 mg day−1) and received an additional loading dose of 300 mg, platelet function was assessed at the earliest after 24 h. Venous blood was collected in 3.8% citrate plasma. Samples were centrifuged at 150 × g for 10 min to obtain platelet-rich plasma (PRP) and additionally at 2000 × g for 10 min to recover platelet-poor plasma (PPP). Platelet concentration of PRP was adjusted to 2 × 105 μL−1 by adding homologous PPP. Per cent platelet aggregation after stimulation with 20 μmol L−1 adenosine disphosphate (ADP) was assessed with the turbodimetric method using a Chronolog Lumi aggregometer with Aggro-Link Software [16,17]. Residual platelet aggregation (RPA) was defined as aggregation measured 5 min after addition of ADP 20 μmol L−1.


At 30 days the incidence of cardiovascular events (cardiovascular death, myocardial infarction, ischemic stroke) and death was assessed by review of patients’ charts on readmission or by telephone interview. Telephone interviewers were blinded with respect to the results of platelet aggregation.

Statistical analysis and development of risk score

Continuous data are expressed as mean ± SD, and not normally distributed data are presented as median and interquartile range. Dichotomous variables are shown as number (%) and the equality of distribution between subgroups was analyzed by chi-squared test.

For the development of the risk score we chose variables that are available in clinical routine. Candidate variables included cardiovascular risk factors such as smoking history, diabetes, hypertension, renal failure with a serum creatinine > 1.5 mg dL−1, hypercholesterolemia, left ventricular dysfunction, age, gender, acute coronary syndrome on admission and previous co-medication with statins. Time difference and loading dose did not show significant difference in distribution within tertiles and were not included in multivariate analysis. The included variables were arranged in a dichotomous fashion. Age was dichotomized with a cut-off value of 65 years. Multivariate logistic regression analysis was then performed to identify independent predictors of RPA (platelet aggregation in the upper tertile) as dependent variables. Variables were selected by forward and backward logistic regression with a P-value of < 0.2 as entry criterion for the final model. Cox proportional hazards survival regression was used to investigate predictors of outcome with inclusion of relevant factors: cardiovascular medication including statin treatment, severe left ventricular dysfunction, age, gender, acute coronary syndromes, diabetes mellitus, smoking, serum creatinine, severity of coronary artery disease, type of stents and residual platelet aggregation categorized by tertiles. Variables were selected by forward and backward method with a P-value of < 0.05 as entry criterion.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure of Conflict of Interests
  9. References

A consecutive cohort of 1092 patients that were treated for symptomatic coronary artery disease with implantation of stents was included in the present analysis. Baseline characteristics are shown in Table 1; 51.6% of the patients were treated for acute coronary syndrome (= 563). Of these, 225 patients (20.6%) underwent coronary revascularization for non-ST elevation and 193 patients (17.7%) for ST-elevation myocardial infarction (STEMI). Around 40% of patients analyzed as STEMI received additional GPIIb-IIIa blockade. One-third of the patients (33.2%) suffered from type II diabetes and 15.8% (37.5% of diabetics) were insulin dependent. A total of 72.6% of included patients underwent coronary stenting employing bare metal stents (BMS) only, 18.9% had drug-eluting stents (DES) only, and 8.5% of patients received both, BMS and DES (Table 1). Residual platelet aggregation had a median of 36.3% (interquartile range 35.1). In the majority of patients, residual platelet aggregation (RPA) was determined around 24 h after administration of the loading dose clopidogrel (median 24.8 h; 21.9 interquartile range). Tertiles of RPA were divided by equal distribution of cases. Thus, the border of the upper tertile was defined by a value > 46.9%. A total of 9.6% of the analyzed patients received additional GPIIb-IIIa blockade (abciximab) and were measured at later time points (5 days after clopidogrel loading). There was no significant difference of residual platelet aggregation between GPIIb-IIIa inhibitor treated patients and patients who were on dual antiplatelet only (median 37%, interquartile range 35.5 vs. 33% ± 34.5; = 0.61).

Table 1.   Characteristics of the study population
VariablePatients (= 1092)
  1. *Mean ± SD; Median (interquartile range); LD, loading dose.

Gender, m/f (%)806/286 (73.8/26.2)
Age (years)* 67.5 ± 10.8
Age > 65 years (%)675 (61.8)
Body mass index 27.3 (5.2)
Acute coronary syndrome (%)564 (51.7)
 STEMI (%)193 (17.7)
 NSTEMI (%)225 (20.6)
Left ventricular function (%)
 Ejection fraction
  45–55%239 (21.9)
  35–45%183 (16.8)
  < 35%107 (9.8)
Hypertension (%)876 (80.2)
Hyperlipidemia (%)641 (58.7)
Diabetes (%)363 (33.2)
Insulin dependent (%)137 (15.8)
Smoking history425 (38.9)
Multivessel disease816 (74.9)
Serum creatinine mg dL−1†  1.1 (0.4)
Renal failure (serum creatinine > 1.5 mg dL−1)207 (19.0)
 Statins (%)953 (87.4)
 ACE-inhibitors (%)876 (80.6)
 Angiotensin receptor blockers139 (12.8)
 ß-blockers (%)1001 (92.1)
Previous myocardial infarction222 (27.4)
Bare metal stents/drug-eluting stents/both (%)793 (72.6)/206 (18.9)/93 (8.5)
Drug coating
 Sirolimus (%) 42 (3.8)
 Tacrolimus (%) 98 (9.0)
 Zotarolimus (%)162 (14.8)
Time difference from LD to platelet function test 24.8 (21.9)

Univariate influence of baseline characteristics

Patients’ characteristics dependent on tertile of RPA are shown in Table 2. Patients identified with RPA in the upper tertile of the collective were significantly older (= 0.02), more frequently had diabetes (= 0.001) and renal failure (= 0.001), were more often admitted with acute coronary syndrome (= 0.03) and showed a poorer left ventricular function (< 0.001) compared with patients measured in the lower tertiles. Time periods from administration of loading dose to platelet function test did not significantly differ between tertiles (Table 2). Deployment of different type of stents (BMS/DES), total number of implanted stents, and subgroups of various drug-coated stents, did not show a relevant different distribution depending on tertile (Table 2).

Table 2.   Patients‘ characteristics dependent on tertile of RPA
VariablesTertile of residual platelet activity
123P for tertile 3 vs. 1 + 2
Age (> 65 years) (%)55.563.266.80.02
Gender male/female (%)74.2/25.873.6/26.473.6/26.40.94
Diabetes (%)25.834.139.80.001
Hypertension (%)
Renal failure (%)15.716.524.70.001
Smoking (%)40.437.938.50.83
Hyperlipidemia (%)59.961.354.90.08
Acute coronary syndrome (%)49.242.857.20.01
Reduced left ventricular function (%)43.744.258.0< 0.001
Three vessel disease (%)37.944.943.50.52
Statins (%)87.389.385.40.18
Loading dose
 600 mg (%)83.484.185.10.61
 300 mg (%)16.615.914.9
Bare metal stents/drug-eluting stents (%)78.2/21.879.3/20.779.2/20.80.93
Number of stents 1/2/3/4/≥ 5 (%)69.8/22.5/5.2/2.2/0.368.1/21.4/7.7/2.2/0.563.5/26.2/6.6/2.8/0.80.63
Drug coating
 Sirolimus (%)
 Tacrolimus (%)
 Zotarolimus (%)
Time difference from LD to platelet function test (hours); median24.825.324.7   0.83

Multivariable analysis and development of risk score

An age > 65 years, diabetes mellitus, reduced left ventricular function, renal failure with a serum creatinine > 1.5 mg dL−1, and acute coronary syndrome, were selected by forward and backward logistic regression as predictors of increased RPA after inclusion of other variables such as gender, cardiovascular risk factors and cardiovascular co-medication (Hosmer Lemeshow Test for goodness of fit 0.997). By using these five factors a cumulative score was formed and applied on the total patient cohort to analyze its predictive value for increased RPA. Hereby, we found a right shift of distribution of platelet aggregation (Fig. 1) and increasing incidence of RPA in the upper tertile (Fig. 2) by cumulative number of score variables. To account for the unequal influence of score variables in univariate analysis we allocated a weighing factor of 1–3 depending on the significance level (1 = < 0.05; 2 = < 0.01; 3 = < 0.001) to each of the variables. In detail, diabetes was weighed by factor 2, acute coronary syndrome by factor 1, left ventricular dysfunction by factor 3, age >65 years by factor 1, and renal failure by factor 2. Thus, a score ranging from 0–9 was developed. In logistic regression analysis the risk of showing increased RPA was calculated with an odds ratio of up to 3.3 (95% CI 1.8–6.0, < 0.001) for a score level of 7–9 compared with a score of 0 (Table 3). The results were consistent with maximum platelet aggregation. There was a linear association of maximum platelet aggregation with increasing score levels. Thus, patients with a score level of 7–9 showed an odds ratio of 2.7 (95% CI 1.5–4.8; < 0.001) for maximum aggregation in the upper tertile.


Figure 1.  Histograms showing percentage distribution of residual platelet aggregation dependent on cumulative number of score variables.

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Figure 2.  Incidence of increased residual platelet aggregation according to cumulative number of score variables.

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Table 3.   Binary logistic regression for cumulative number of score variables to predict residual platelet activity in the upper tertile
PREDICT scoreOdds ratio (compared with score 0) 95% CISignificance
7–93.31.8–6.0< 0.001


Nine hundred and fifty patients (87%) were followed-up at 30 days and adverse cardiovascular events (myocardial infarction, cardiovascular death and ischemic stroke) and death were assessed either by telephone interview or by reviewing of patients’ files. There was no significant difference between patients who completed the study and those who were lost at follow-up (median of residual platelet aggregation 36% vs. 40%, = 0.23). Followed-up patients were equally distributed in tertiles of RPA. In 28 patients (2.9% of followed-up patients) an adverse event occurred within 30 days: 14 patients died, 10 patients developed a non-fatal myocardial infarction and four patients suffered from ischemic stroke. The rate of events showed increasing distribution dependent on the tertile of residual platelet aggregation: five events (1.5%) in tertile 1, eight events (2.5%) in tertile 2 and 15 events (4.8%) in tertile 3; < 0.05 (Fig. 3); 77.8% of patients with events had a score of 5 and higher compared with 32.4% of patients without events (< 0.001).


Figure 3.  Incidence of 30-day major adverse events (myocardial infarction, ischemic stroke, death, cardiovascular death) dependent on tertile of residual platelet aggregation.

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After forward and backward stepwise inclusion of relevant prognostic variables in Cox proportional hazards survival regression, residual platelet aggregation categorized by tertiles was independently associated with major adverse events (hazard ratio 1.71, 95% CI 1.03–2.84; = 0.037), apart from severe left ventricular dysfunction, diabetes mellitus and the non-use of beta blockers and statins.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure of Conflict of Interests
  9. References

The aim of the present study was to evaluate a risk score to assess enhanced residual platelet aggregation despite conventional dual antiplatelet therapy consisting of aspirin plus clopidogrel. We found that distinct clinical variables were associated with a high residual platelet aggregation (RPA), including an age > 65 years, acute coronary syndrome, diabetes mellitus, renal failure, and reduced left ventricular function. On the basis of these clinical entities we were able to establish a score (PREDICT score) to predict residual platelet activity (RPA) after coronary stenting. We demonstrate that patients with a score of 7–9 have a 3.3 times higher probability of exhibiting increased post-treatment residual platelet activity than patients with a score of 0. At the same time, the score correlated well with the occurrence of short-term major adverse events. The PREDICT score may help to characterize atherothrombotic risk, and thus, might offer a tool to individualize antiplatelet therapy and to improve further outcome in patients undergoing coronary stenting.

Dual antiplatelet therapy is the current established antithrombotic therapy in patients after coronary stenting [4,18]. Clopidogrel in combination with aspirin reduces percutaneous coronary intervention (PCI) related events in patients with stable coronary artery disease (CAD), and is the recommended standard of care in these patients. However, long-term major cardiovascular events occurred in 6–9% of patients despite dual antiplatelet therapy [19,20].

Recent studies have shown a marked variability in clopidogrel’s ability to inhibit platelet formation, and a substantial number of patients (11–34%) are considered to be low responders [6–11]. Recently, we demonstrated that low response to clopidogrel defined by a high residual platelet aggregation in patients with symptomatic CAD treated by stenting, significantly enhances the occurrence of cardiovascular events and death [9]. Thus, evaluation of residual platelet aggregation after loading with clopidogrel may help to identify patients at increased risk who may benefit from intensified antiplatelet strategy. Although, the effect of antiplatelet drugs can be monitored adequately by a variety of platelet function assays, the methods are not ubiquitously available and time consuming to perform. Thus, we asked whether individual clinical parameters are associated with enhanced residual platelet activation despite administration of dual antiplatelet therapy after coronary stenting. Several factors have been proposed to affect platelet responsiveness in cardiovascular patients treated with antiplatelet therapy. Extrinsic mechanisms such as failure to prescribe, non-compliance of drug intake, under-dosing or drug–drug interaction, as well as intrinsic mechanisms such as polymorphism of cytochrome P450 (CYP450) encoding genes [21,22] and metabolic disorders, have been considered to have a relevant impact on drug response. Decreased platelet sensitivity to clopidogrel has been described in diabetics [23,24] and patients with acute coronary events are known to present with high platelet activity [25] and reduced inhibition of platelet function [26]. Moreover, chronic renal failure with a serum creatinine > 1.5 mg dL−1 played a significant role in residual platelet aggregation. This observation goes along with previous results of clinical studies demonstrating an altered platelet function in chronic renal failure [27,28]. Thus, the variables identified in the PREDICT score fit well with previous reports about clinical factors affecting platelet reactivity. The PREDICT score corresponds well with the observed platelet activity in a large unselected patient cohort and can be easily calculated from data that are available from patients’ histories. However, we are aware that in general a score is susceptible to other relevant variables that were not included in the PREDICT score. Additionally, variability of drug response cannot be solely explained by clinical risk factors, as we know that variable response to antiplatelet substances’ effects has also been observed in healthy subjects [7] and there is a pre-existing variability of platelet aggregation even in patients naive to antiplatelet therapy [29]. As antiplatelet drug response is a multifactorial phenomenon [30,31], there might be other factors that had a relevant impact on residual platelet activity and have not been considered in the present score. We know that response to antiplatelet drugs is influenced by a wide number of variables, including genetic factors (in case of clopidogrel effects polymorphisms of CYP3A4, CYP2C19, GPIa, P2Y12 and GPIIIa). As a further limitation, additional procedural factors during coronary intervention that might play an important role (e.g. inflating time, type of lesion, stent length and diameters) were not considered in the present study.

Regarding our measurement protocol we used late ADP-induced platelet residual aggregation to describe platelet sensitivity to clopidogrel. This method has been frequently applied to evaluate clopidogrel-dependent platelet inhibition and still presents the gold standard compared with other sensitive platelet function tests. We employed high ADP concentrations (20 μmol L−1) to evaluate residual platelet aggregation and not only to monitor the rapidly reversible phase of platelet aggregation by lower concentrations of ADP. Platelet function analysis was performed at random time points later than 6 h after administration of the 600-mg clopidogrel loading dose when maximum clopidogrel-dependent platelet inhibition is achieved [14,15]. The majority of patients were measured around 24 h after loading. In a time-dependent correlation we did not observe a significant impact of the particular time point of platelet function analysis on measured residual platelet activity. Thus, we believe that at least in the periprocedural period, there is no relevant impact of random measurements on the results obtained. However, we are aware that initial high residual platelet aggregation might recover to normal values at later time points as late responsiveness to clopidogrel has been described in the literature [8].

Although in the past both pre- and post-treatment platelet activity has been used to measure effects of clopidogrel, we and others recently demonstrated that a single post-treatment assessment of platelet function can help to identify patients at increased risk for recurrent cardiovascular events [8–12]. In addition, measurement of post-treatment platelet aggregation has been considered as a better marker to estimate thrombotic risk, especially regarding the incidence of stent thrombosis [32,33]. Thus, we believe that assessment of residual platelet activity represents a reasonable approach suitable for clinical routine to estimate the individual risk for thrombotic events after coronary interventions.

In this study we could follow-up the majority of patients (= 950, 87%) and found an increased incidence of 30-day events in the group measured in the upper tertile of residual platelet aggregation. At the same time, patients with events showed significantly higher scores than patients without events, indicating the clinical impact of the score. Additionally, we could identify residual platelet aggregation to be independently associated with major adverse events. Of course, the association of residual platelet activity and clinical outcome as estimated by the PREDICT score has to be evaluated in large clinical trials. In the past, clinical scores have been developed to identify the risk for recurrent events in cardiovascular patients. Among these the TIMI risk score investigated predictor variable for recurrent events in patients with unstable angina and non-ST elevation and ST elevation myocardial infarction [34,35]. The score included seven clinical variables: age > 65 years, at least three risk factors for CAD, prior coronary stenosis > 50%, ST segment deviation on admission ECG, at least two anginal symptoms 24 h prior to admission, use of aspirin within the last 7 days and elevated cardiac markers. The patient collective investigated in the present PREDICT score differed from the one that was explored to develop the TIMI score and these scores cannot be easily transferred. Thus, we enrolled an unselected patient cohort with stable coronary artery disease and acute coronary syndrome, and the entity of non-ST elevating ACS as well as STEMI is entered in the PREDICT score but not in the TIMI score, as the latter only refers to ACS patients. However, a further modification of the PREDICT sore in a subcohort of ACS patients would be reasonable to allow correlation of both scores and to strengthen the prognostic role of residual aggregation together with other well-established predictors of cardiovascular outcome (e.g. TIMI score variables) after acute coronary syndromes. Thus, in the future we might be able to use clinical scores for identification of patients who are at increased risk for recurrent thromboischemic events due to lack of efficacy of conventional antiplatelet therapy and might therefore benefit from intensified antiplatelet regimen. There is accumulating evidence that raising the maintenance dose of clopidogrel to 150 mg day−1 improves the variability and effectiveness of platelet inhibition compared with conventional doses of 75 mg day−1 [36]. Furthermore, increasing the loading dose should be considered in these patients. In the recently published ALBION trial enrolling patients with NSTEMI acute coronary syndrome, a 900-mg clopidogrel loading dose led to a faster onset and higher maximum platelet inhibition during the first 24 h after coronary stenting (< 0.05) [37]. Also, in this trial higher doses were not associated with increased incidence of major bleeding. Probably, due to a small sample size, the incidence of major cardiovascular events was not significant between groups of different loading doses. Alternatively, novel antiplatelet substances like prasugrel or the oral P2Y12-antagonist AZD6140 show promising results and might provide a higher and a more rapid platelet inhibition [38,39]. Our results suggest that patients with a high score (7–9) show increased risk for elevated residual platelet aggregation and are also most jeopardized for recurrent events. Therefore, these patients should require particular monitoring of efficacy of platelet inhibition and optimization of antiplatelet therapy should be considered.

In conclusion, it might be necessary to individualize antiplatelet therapy on behalf of individual risk and to adapt dosing of clopidogrel or to change to alternative compounds in high-risk patients.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure of Conflict of Interests
  9. References

We greatly appreciate the excellent technical help of K. Kathrilaka and I. Schaefer. The study was supported in part by the Deutsche Forschungsgemeinschaft and grants from the Karl & Lore Klein Stiftung and Karl Kuhn Stiftung.

Disclosure of Conflict of Interests

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure of Conflict of Interests
  9. References

The authors state that they have no conflict of interest.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure of Conflict of Interests
  9. References
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