This was a prospective study comparing two groups: personalized and non-personalized treatment with P2Y12 receptor blockers during a 12-month follow-up. We aimed to investigate whether personalized antiplatelet treatment in patients with high on-treatment platelet reactivity (HTPR) improves clinical outcome. Platelet reactivity was assessed by adenosine diphosphate induced aggregation using a multiple electrode aggregometry (MEA) in 798 patients with coronary artery disease undergoing percutaneous coronary intervention (PCI). Patients with HTPR received up to four repeated loading doses of clopidogrel or prasugrel in the personalized treatment group (n=403), whereas no change in the treatment strategy was undertaken in patients with HTPR in the non-personalized treatment group (n=395). There were fewer major adverse cardiac events (MACE) in the personalized treatment group than in the non-personalized treatment group (7.4% compared with 15.3% respectively; P<0.001). The multivariate Cox regression analysis showed that the relative risk to develop MACE was 51% lower in the personalized treatment group as compared with the non-personalized treatment group [hazard ratio (HR)=0.49; 95% confidence interval (CI): 0.31–0.77; P<0.001]. Similarly, there was a clear net benefit of the personalized antiplatelet treatment over the non-personalized treatment (ischemic and bleedings events: 8.2% versus 18.7% respectively; HR=0.46; 95%CI: 0.29–0.70; P<0.001). Further analysis indicated that patients with aggregation values within the therapeutic window (21–49 units) experienced the lowest event rates (stent thrombosis and major bleeding: 2.5%) as compared with poor responders (≥50 units: 5.4%) or ultra-responders (0–20 units: 5.2%). In conclusion, personalized antiplatelet treatment might improve patients’ outcome without increasing bleeding complications compared with the non-personalized treatment during a 12-month follow-up.

CLINICAL PERSPECTIVES

  • Following a link between high on clopidogrel platelet reactivity and thrombotic events, we investigated whether personalized antiplatelet treatment by lowering platelet reactivity as frequently as required to reach the sufficient platelet inhibition improves patients’ outcome.

  • Patients in the personalized treatment group received up to four repeated loading doses of clopidogrel or prasugrel, whereas no change in the treatment strategy was undertaken in the non-personalized treatment group (n=395). This study showed that personalized antiplatelet treatment might reduce ischaemic evens without increasing bleeding complications compared with the non-personalized treatment during a 12-month follow-up.

  • Further work is needed to define the value of personalized antiplatelet therapy strategies before it will be incorporated into routine clinical practice.

INTRODUCTION

The accumulating body of evidence underlines a considerable concern regarding the one-size-fits-all strategy with use of P2Y12 receptor inhibitors, especially of clopidogrel [1]. More than 40 studies in over 20000 patients linked high on treatment platelet reactivity (HTPR) to cardiac adverse events [2]. Hence, these intriguing observations led to the suggestion that the level of platelet inhibition by clopidogrel can be measured and individually adjusted [3]. Indeed, the majority of published studies have demonstrated that HTPR can be overcome in most patients with higher loading or maintenance doses of clopidogrel, or by switching to more potent P2Y12 receptor inhibitors prasugrel, ticagrelor or cangrelor [412]. The next logical question which was addressed was whether the reduction in platelet reactivity by tailored antiplatelet treatment would improve clinical outcome. Interestingly, a considerable variability exists in the results of published studies. An improved clinical outcome with individualized antiplatelet therapy resulted in a reduction in major adverse cardiac events without an increase in major bleeding complications in some smaller studies [7,8,13]. In contrast, larger trials have so far failed to demonstrate benefit of individually tailored anti-platelet therapy, although design issues may have contributed to these results, as recently discussed [14]. We have already presented the short-term results of the Multiple electrode Aggregometry in patients receiving Dual antiplatelet therapy to guide treatment with Novel platelet Antagonists (MADONNA) study, which has shown that personalized antiplatelet treatment according to the platelet function testing resulted in an improved efficacy with an equal safety compared with the standard treatment during 1-month follow-up [13]. In the present paper, we present 1-year clinical outcome data of the MADONNA study with the focus on the net clinical benefit.

MATERIALS AND METHODS

Study design

The design of the MADONNA study was described previously [13]. Briefly, the MADONNA study was a prospective non-randomized and non-blinded study comparing two cohorts investigating personalized versus non-personalized antiplatelet therapy. The Ethics Committees approved the study protocol in accordance with the Declaration of Helsinki. Patients gave informed consent prior to inclusion in the present study. Participants were included into the study between March 2007 and November 2010. Inclusion criteria were: stent implantation, percutaneous coronary intervention (PCI) at least 2 h after clopidogrel loading with 600 mg. The only exclusion criterion was participation in interventional trials. Patients in the non-personalized group received only one clopidogrel loading dose (Plavix® 600 mg), patients in the personalized group received an individualized antiplatelet treatment according to the following algorithm: after an initial clopidogrel loading dose of 600 mg on-treatment platelet reactivity was measured the day after PCI and the earliest after 12 h after loading by multiplate electrode aggregometry (MEA). In case of high on-treatment platelet reactivity (HTPR ≥ 50 units), patients were reloaded with clopidogrel 600 mg. Subsequently, on-treatment platelet reactivity was measured the day after loading. In case of HTPR after a second or a third clopidogrel loading, patients were re-loaded with clopidogrel 600 mg for a third and fourth time. After prasugrel became available, patients with HTPR were loaded with prasugrel (Efient® 60 mg) except in case of contraindications (history of stroke or intracranial haemorrhage) where a second loading dose of clopidogrel would have been administered. After each re-loading with clopidogrel or switch to prasugrel platelet function was measured 1 day thereafter to ensure that HTPR was overcome. During the maintenance treatment, patients received the antiplatelet therapy matching with the last loading dose they received (clopidogrel responders: clopidogrel 75 mg; clopidogrel non-responders: clopidogrel 75 mg or prasugrel 10 mg). Clinical follow-up information was obtained by contacting all patients by phone at 12 months. Source documents of potential events were obtained. Additionally, information concerning the cause of death was obtained from national death registry (Statistics Austria). Blood samples from patients were obtained after PCI.

Impedance aggregometry

Whole blood aggregation was determined using MEA on a new generation impedance aggregometer (Multiplate Analyzer, Verum Diagnostica). The system detects the electrical impedance change due to the adhesion and aggregation of platelets on two independent electrode-set surfaces in the test cuvette [15]. We used hirudin as anticoagulant, which is recommended by the manufacturer and ADP as agonist [16,17]. A 1:2 dilution of whole blood anticoagulated with hirudin and 0.9% NaCl was stirred at 37°C for 3 min in the test cuvettes, ADP: 6.4 μM was added and the increase in electrical impedance was recorded continuously for 6 min [18]. The mean values of the two independent determinations are expressed as the area under the curve (AUC) of the aggregation tracing. We reported AUC in units, as described previously [19]. HTPR cut-off was prespecified and defined as values ≥50 units (poor responders) [20,21]. Therapeutic window was defined as values: 21–49 units (regular responders). Patients with values of 0–20 units were defined as ultra-responders. Measurements were performed by trained laboratory technicians blinded to the outcomes.

We used MEA device due to the reported high effectiveness to predict stent thrombosis [odds ratio (OR)=9–37; area under the receiver operating characteristic (ROC) curve: 0.78–0.92, sensitivity: 70–90% and specificity: 84–100%] [2022] and a satisfied reproducibility (<6% variability) [23].

Study end points

The primary efficacy end point at 12 months was the composite of major adverse cardiovascular events (MACE: definite or probable stent thrombosis, myocardial infarction and death). Definite stent thrombosis was defined according to the Academic Research Consortium criteria as the occurrence of an acute coronary syndrome with either angiographic or pathological confirmation of thrombosis [24]. Probable stent thrombosis was defined as any unexplained death within 30 days or target vessel MI without angiographic confirmation of thrombosis or other identified culprit lesion [24]. Myocardial infarction was defined according to the universal definition [25]. The primary safety end point was the incidence of thrombolysis in myocardial infarction (TIMI) major bleeding [26]. The net clinical end point was a composite of MACE and TIMI major bleeding.

Statistical analysis

Based on a 4.2% rate of stent thrombosis (defined according to the Academic Research Consortium criteria) in the non- personalized group as compared with 0.5% in the personalized group [7], we calculated that at least 351 patients per group would provide 90% power to detect significant differences in the rate of stent thrombosis (two sided alpha value of <0.05). For the MACE end point during 12-month follow-up the study had 94% power. Normal distribution was tested with the Kolmogorov–Smirnov test. Data are expressed as means, S.D., 95% confidence intervals (CI), medians or interquartile range (IQR). A ROC curve analysis was used to determine the ability of the test to distinguish between patients (i) with or without myocardial infarction and (ii) with or without major bleeding. For the calculation of the area under the curve only one value assessed in responders and the last value measured after re-loading in non-responders were used. Statistical comparisons were performed with Mann–Whitney's U test and χ2 test when applicable. Kaplan–Meier curves with the log rank test were used for survival analyses. The multivariate Cox regression analysis was performed to distinguish whether the assignment to the group can discriminate the outcome and included diabetes mellitus, hypertension, cigarette smoking, body mass index, age, stent length and use of statins or proton pump inhibitors (PPIs). Classification tree analysis [χ2 automatic interaction detection (CHAID)] was used to detect independent discriminators of the composite of MACE in the personalized treatment group. The analysis included: ADP-induced platelet aggregation, indication for PCI [stable coronary artery disease (CAD) or primary PCI for myocardial infarction], common risk factors for coronary artery disease (cigarette smoking, diabetes mellitus, hypertension, family history of coronary artery disease, hyperlipidaemia and obesity), co-morbidities (renal failure, periphery or cerebral vascular disease), age and sex. All statistical calculations were performed using commercially available statistical software (IBM SPSS Version 21.0).

RESULTS

Patient demographics

A total of 798 patients were included in the study: 403 (50.5%) patients in the personalized group and 395 (49.5%) patients in the non-personalized group (Table 1) [13]. Use of PPIs was higher in the non-personalized group, whereas use of statins was higher in the personalized group (Table 1). Concerning the use of PPIs, two-thirds of patients received pantoprazole, which does not interact with the metabolism of clopidogrel and one-third received esomeprazole in both groups (results not shown). There was also a difference in the total stent length between the groups (Table 1). Three patients were lost to follow-up.

Table 1
Patient demographics

Data are reported as means±S.D., number of patients (n) or percentages. **P<0.01, ***P<0.001 personalized vs. non-personalized group. ACE=angiotensin-converting enzyme; ARB=angiotensin receptor blocker; CAD=coronary artery disease.

Patient demographics (n=798)Personalized treatment group (n=403)Non-personalized treatment group, (n=395)
Age (years) 66±12 64±12 
Gender (male) (n285 (71%) 300 (76%) 
Risk factors/past medical history (n  
 Hypertension 339 (84%) 337 (85%) 
 Smoking 203 (50%) 216 (55%) 
 Diabetes mellitus 141 (35%) 132 (34%) 
 Hyperlipidaemia 307 (76%) 301 (76%) 
 Prior myocardial infarction or PCI 160 (40%) 177 (45%) 
 Peripheral arterial occlusive disease 56 (14%) 53 (14%) 
 Cerebrovascular disease 47 (12%) 41 (10%) 
Body mass index (kg/m228.5±4.8 28.1±5.3 
Laboratory data   
 Platelets (×109/l) 247±79 230±65 
 Haemoglobin (g/dl) 13.8±7.5 14.2±8.1 
Medications (n  
 ACE inhibitors or ARB 307 (78) 278 (73%) 
 Aspirin 403 (100%) 395 (100%) 
 Calcium channel blockers 70 (17%) 75 (19%) 
 Statins 371 (92%) 320 (81%)*** 
 β-Blockers 309 (77%) 315 (80%) 
 PPIs 249 (62%) 305 (77%)*** 
PCI data (n  
 Elective PCI 254 (63%) 250 (63%) 
 Acute PCI due to myocardial infarction 149 (37%) 145 (37%) 
 ST-elevation myocardial infarction 41 (28%) 51 (35%) 
 Non-ST-elevation acute coronary syndrome 108 (72%) 94 (65%) 
 Drug-eluting stents 374 (93%) 357 (91%) 
 Bare metal stents 29 (7%) 38 (9%) 
 Number of stents per patient 2.1±1.5 1.8±1.0 
 Total stent length 41±33 32±22*** 
Patient demographics (n=798)Personalized treatment group (n=403)Non-personalized treatment group, (n=395)
Age (years) 66±12 64±12 
Gender (male) (n285 (71%) 300 (76%) 
Risk factors/past medical history (n  
 Hypertension 339 (84%) 337 (85%) 
 Smoking 203 (50%) 216 (55%) 
 Diabetes mellitus 141 (35%) 132 (34%) 
 Hyperlipidaemia 307 (76%) 301 (76%) 
 Prior myocardial infarction or PCI 160 (40%) 177 (45%) 
 Peripheral arterial occlusive disease 56 (14%) 53 (14%) 
 Cerebrovascular disease 47 (12%) 41 (10%) 
Body mass index (kg/m228.5±4.8 28.1±5.3 
Laboratory data   
 Platelets (×109/l) 247±79 230±65 
 Haemoglobin (g/dl) 13.8±7.5 14.2±8.1 
Medications (n  
 ACE inhibitors or ARB 307 (78) 278 (73%) 
 Aspirin 403 (100%) 395 (100%) 
 Calcium channel blockers 70 (17%) 75 (19%) 
 Statins 371 (92%) 320 (81%)*** 
 β-Blockers 309 (77%) 315 (80%) 
 PPIs 249 (62%) 305 (77%)*** 
PCI data (n  
 Elective PCI 254 (63%) 250 (63%) 
 Acute PCI due to myocardial infarction 149 (37%) 145 (37%) 
 ST-elevation myocardial infarction 41 (28%) 51 (35%) 
 Non-ST-elevation acute coronary syndrome 108 (72%) 94 (65%) 
 Drug-eluting stents 374 (93%) 357 (91%) 
 Bare metal stents 29 (7%) 38 (9%) 
 Number of stents per patient 2.1±1.5 1.8±1.0 
 Total stent length 41±33 32±22*** 

Strategy of personalized treatment

After loading with 600 mg of clopidogrel, 26% of patients (n=106) had HTPR. A total of 56 of those patients received a prasugrel loading dose of 60 mg and all patients reached a sufficient level of platelet inhibition. Fifty patients were re-loaded with 600 mg of clopidogrel. Nevertheless, seven patients still required a third clopidogrel loading dose of 600 mg, five patients received four clopidogrel loading doses of 600 mg and two patients were switched to prasugrel (60 mg loading). After this strategy, two patients of 403 (0.5%) did not reach a sufficient platelet inhibition despite reloading with clopidogrel or prasugrel [13].

Efficacy and safety

The composite end point of MACE occurred significantly less often in the personalized treatment group than in the non-personalized treatment group during 12-month follow-up (7.4% versus 15.3% respectively; P<0.001; Figure 1). The multivariate Cox regression analysis showed that the relative risk to develop MACE was 51% lower in the personalized treatment group as compared with the non-personalized treatment group [HR (hazard ratio)=0.49; 95% CI: 0.31–0.77; P<0.001; Table 2). Definite and probable stent thrombosis occurred numerically less frequently in the personalized group than in the non-personalized group (1.2% versus 2.6%; P=0.221; Table 2). Whereas three definite and seven probable stent thromboses occurred in the non-personalized group, no definite and five probable stent thromboses were seen in the personalized group. Myocardial infarction was a major driver in the composite of MACE and occurred significantly less often in the personalized treatment group than in the non-personalized treatment group (1.0% versus 10.2% respectively; P<0.001; Table 2). There was no difference in the event rates of death between the groups (6.9% versus 5.9%; P=0.518; Table 2). Similarly, no statistical difference in TIMI major bleeding events was seen between the personalized and non-personalized group (1.0% versus 2.8%; P=0.167; Table 2). There was a clear net benefit of the personalized antiplatelet treatment over the non-personalized treatment (ischaemic and bleedings events: 8.2% versus 18.7% respectively; HR=0.46; 95%CI: 0.29–0.70; P<0.001; Table 2). The landmark analysis suggests that the effect of personalized antiplatelet treatment on the MACE rate was also evident after the first 30 days of treatment (Figure 1).

Table 2
Event rates in the personalized and non-personalized groups during 1-year follow-up
EventPersonalized treatment group (n=403)Non-personalized treatment group (n=395)Adjusted HR (95% CI)P
The composite of MACE (stent thrombosis, myocardial infarction, death) (n30 (7.4%) 60 (15.3%) 0.49 (0.31–0.77) <0.001 
Myocardial infarction (n4 (1%) 40 (10.2%) 0.10 (0.04–0.29) <0.001 
Stent thrombosis: definite and probable (n5 (1.2%) 10 (2.6%) 0.50 (0.17–1.50) 0.221 
Stent thrombosis: definite (n0 (0%) 3 (0.8%) 0.15 (0.00–159.0) 0.078 
Death (n28 (6.9%) 23 (5.9%) 1.21 (0.67–2.20) 0.518 
TIMI major bleeding (n4 (1%) 11 (2.8%) 0.43 (0.13–1.41) 0.167 
Net benefit (the composite of MACE and TIMI major bleeding events) (n33 (8.2%) 71 (18.7%) 0.46 (0.29–0.70) <0.001 
EventPersonalized treatment group (n=403)Non-personalized treatment group (n=395)Adjusted HR (95% CI)P
The composite of MACE (stent thrombosis, myocardial infarction, death) (n30 (7.4%) 60 (15.3%) 0.49 (0.31–0.77) <0.001 
Myocardial infarction (n4 (1%) 40 (10.2%) 0.10 (0.04–0.29) <0.001 
Stent thrombosis: definite and probable (n5 (1.2%) 10 (2.6%) 0.50 (0.17–1.50) 0.221 
Stent thrombosis: definite (n0 (0%) 3 (0.8%) 0.15 (0.00–159.0) 0.078 
Death (n28 (6.9%) 23 (5.9%) 1.21 (0.67–2.20) 0.518 
TIMI major bleeding (n4 (1%) 11 (2.8%) 0.43 (0.13–1.41) 0.167 
Net benefit (the composite of MACE and TIMI major bleeding events) (n33 (8.2%) 71 (18.7%) 0.46 (0.29–0.70) <0.001 

Kaplan–Meier estimates of the composite of MACE (stent thrombosis, myocardial infarction, death) in the personalized and non-personalized treatment groups during 12-month follow-up

Figure 1
Kaplan–Meier estimates of the composite of MACE (stent thrombosis, myocardial infarction, death) in the personalized and non-personalized treatment groups during 12-month follow-up

Landmark analysis of MACE 30–365 after inclusion to the study presented in the right upper corner.

Figure 1
Kaplan–Meier estimates of the composite of MACE (stent thrombosis, myocardial infarction, death) in the personalized and non-personalized treatment groups during 12-month follow-up

Landmark analysis of MACE 30–365 after inclusion to the study presented in the right upper corner.

Performance of ADP-induced platelet aggregation assessed by MEA for assessment of response to clopidogrel in order to predict myocardial infarction or major bleeding

ROC curve analysis demonstrated that ADP-induced platelet aggregation distinguished between patients with and without subsequent myocardial infarction (area under the curve–c-index=0.65; 95%CI: 0.59–0.75; P<0.001; Figure 2). ROC curve analysis also demonstrated that ADP-induced platelet aggregation distinguished between patients with and without subsequent TIMI major bleeding (c-index=0.65; 95%CI: 0.53–0.78; P=0.041; Figure 2).

ROC curves for prediction of myocardial infarction and TIMI major bleedings by ADP-induced platelet aggregation with use of MEA during 12-month follow-up

Figure 2
ROC curves for prediction of myocardial infarction and TIMI major bleedings by ADP-induced platelet aggregation with use of MEA during 12-month follow-up
Figure 2
ROC curves for prediction of myocardial infarction and TIMI major bleedings by ADP-induced platelet aggregation with use of MEA during 12-month follow-up

Independent discriminators of the composite of MACE in the personalized treatment group

Classification tree analysis (CHAID) was used to detect discriminators of 12-month MACE in the personalized treatment group. The analysis included platelet reactivity after adjusted treatment (ADP-induced platelet aggregation), indication for PCI (stable angina or myocardial infarction), common risk factors for coronary artery disease, past medical history, co-morbidities, age and sex. Myocardial infarction emerged as the strongest variable influencing the risk of MACE in the personalized treatment group (Figure 3). The second strongest discriminator in the cohort of patients presenting with myocardial infarction at admission was age. Whereas MACE occurred in 29.9% of patients older than 69 years of age, patients younger than or equal 69 years of age experienced MACE only in 2.4% (P<0.001; Figure 3). In patients undergoing elective PCI due to stable angina, diabetes mellitus was the strongest discriminator of outcome. Patients with diabetes mellitus experienced MACE in 6.7%, whereas patients without diabetes mellitus in 1.2% (P=0.019; Figure 3).

CHAID analysis for detection of independent predictors of the composite of MACE (stent thrombosis, myocardial infarction, death) during 12-month follow-up in the personalized treatment group

Figure 3
CHAID analysis for detection of independent predictors of the composite of MACE (stent thrombosis, myocardial infarction, death) during 12-month follow-up in the personalized treatment group
Figure 3
CHAID analysis for detection of independent predictors of the composite of MACE (stent thrombosis, myocardial infarction, death) during 12-month follow-up in the personalized treatment group

Therapeutic window

In the non-personalized treatment group the rates of major bleedings increased inversely proportional to the aggregation units and were higher in patients with the aggregation values 0–19 units (4.9%), than in those with the aggregation values 20–49 units (2.8%) and ≥50 units (0.8%; P=0.047; Figure 4A). In contrast and as expected, the rates of stent thrombosis increased directly proportional to the aggregation units and were numerically higher in patients with aggregation values ≥50 units (4.7%), than in those with aggregation values 21–49 units (2.1%) or <20 units (0.8%; P=0.065; Figure 4A). In the personalized group, major bleedings occurred only in patients with aggregation values <20 units (3.1% vs. 0%; P=0.015; Figure 4B). In the overall study cohort, patients with the aggregation values within the therapeutic window (21–49 units) experienced numerically the lowest rates of the composite of stent thrombosis and major bleeding events (2.5%) as compared with patients with aggregation values >50 units (5.4%) or <20 units (5.2%; P=0.092; Figure 4C).

The percentage of TIMI major bleedings and stent thrombosis (definite and probable) during 12-month follow-up according to the therapeutic window ranges by ADP-induced platelet aggregation with use of MEA in the (A) non-personalized treatment group, (B) personalized treatment group and (C) overall study cohort.

Figure 4
The percentage of TIMI major bleedings and stent thrombosis (definite and probable) during 12-month follow-up according to the therapeutic window ranges by ADP-induced platelet aggregation with use of MEA in the (A) non-personalized treatment group, (B) personalized treatment group and (C) overall study cohort.
Figure 4
The percentage of TIMI major bleedings and stent thrombosis (definite and probable) during 12-month follow-up according to the therapeutic window ranges by ADP-induced platelet aggregation with use of MEA in the (A) non-personalized treatment group, (B) personalized treatment group and (C) overall study cohort.

DISCUSSION

Following a link between HTPR and cardiac adverse events, we investigated whether lowering platelet reactivity improves 1-year clinical outcome. This study suggested that personalized antiplatelet therapy might be superior over the non-personalized antiplatelet therapy in regard to ischaemic events as well as the net clinical benefit.

In concordance with our study, an improved clinical outcome with individualized antiplatelet therapy resulted in a reduction in major adverse cardiac events without an increase in major bleeding complications in some smaller studies [7,8]. In contrast, despite reducing platelet reactivity, this strategy was ineffective in larger trials as ARCTIC, GRAVITAS or TRIGGER-PCI [2729]. Therefore, these multiple lines of evidence raise the question whether the study design of these trials might in part have led to negative results. Issues related to treatment strategy, study end points, sample size calculation, patient selection or time point of randomization could have an impact on study findings [30]. Concordantly, the mixed results from several trials investigating the usefulness of personalized antiplatelet therapy probably relate to the lack of an optimized method for measuring platelet reactivity and cut-off values that would be aligned to this specific level of clinical risk associated with the patient characteristics [3]. Methods used to determine platelet reactivity are difficult to standardize and therefore can vary greatly over time and between laboratories [2]. Unfortunately, clinical cross-validation of the assays is limited to two studies: the Phenotyping versus Genotyping for prediction of cardiac Adverse events in patients Undergoing Percutaneous Coronary Intervention (PEGASUS-PCI) study, in which MEA was most predictive for stent thrombosis and MACE and the POPULAR study (Do Platelet Function Assays Predict Clinical Outcomes in Clopidogrel-Pretreated Patients Undergoing Elective PCI), in which LTA, Plateletworks and VerifyNow were shown to have the best predictive values for ischaemic events [22,31]. In regards to feasibility of the most frequently used tests, light transmission aggregometry and the VASP assay seem to be too labour intensive to be performed on a daily basis. In contrast, the results of VerifyNow and MEA are available within few minutes. The major shortcoming of VerifyNow, however, is the cost of a single measurement. Moreover, the additional limitation of VerifyNow is the poor correlation with the P2Y12 receptor occupancy, where the sensitivity of the VerifyNow P2Y12 assay decreased at improved clopidogrel responses [32]. This might be the reason why studies using VerifyNow to guide antiplatelet therapy (e.g. GRAVITAS, ARTIC) failed so far. Therefore, based on the comparison to the MADONNA study [13] as well to the Hungarian registry study, which both used the MEA device [33] and were positive in terms of improving patients outcome, one can argue that it could well be that the MEA is more sensitive than the VerifyNow P2Y12 assay for guidance of the therapy with P2Y12 receptor inhibitors. Recently, it has been shown that the degree of concordance between VerifyNow and MEA regarding the diagnosis of HTPR under treatment with clopidogrel is poor and largely dependent on the selected diagnostic cut-off points [34], which might further underscore the above-stated hypothesis. Hopefully, well-designed future studies will verify which assay should be used when testing the response to antiplatelet drugs in order to guide the therapy.

Although it has been previously assumed that HTPR occurs only under treatment with clopidogrel, recent data show that in the acute phase of ST-elevation myocardial infarction (STEMI) a significant percentage of patients treated with prasugrel or ticagrelor also exhibited HTPR in the early phase [3540]. In detail, roughly 46–60% (ticagrelor) and 37–43% (prasugrel) of patients suffering from STEMI and treated with one of the new P2Y12-receptor antagonists exhibited HTPR when assessed 2 h after the loading dose, thus demonstrating no consistent efficacy in reducing platelet function during or shortly after primary PCI [40,41]. Therefore, even in the era of these faster acting new agents immediate intravenous blocking of platelet aggregation, e.g. by abciximab may be important to close this window [42]. In the stable phase of the disease, two studies indicated that 4–12.5% of patients exhibited HTPR while treated with prasugrel, dependent on the assay used [36,43]. In our study, only one patient exhibited HTPR under treatment with prasugrel. Nevertheless, it is important to note that in our study all patients were primarily treated with clopidogrel and prasugrel was a second choice therapy only in those with HTPR.

Recently, the findings of the ADAPT-DES registry were published [44]. HTPR on clopidogrel was related to stent thrombosis (HR 2.49) and myocardial infarction (HR 1.42), was inversely related to bleeding events (HR 0.73), but was not related to mortality [45]. Based on these findings, authors of the study have concluded that due to the counter-balancing effects of haemorrhagic and ischaemic complications after PCI, tailored antiplatelet strategies must be developed if the benefits of greater platelet inhibition are to be achieved. Noteworthy, this scenario is mirrored in our study: personalized antiplatelet therapy improved the net clinical benefit (ischaemic and bleeding events), but did not influence mortality.

Accordingly, our study supports the existence of a therapeutic window indicating that a moderate level of platelet inhibition more likely correlates best with the net benefit: patients with aggregation values in the middle of the range (21–49 units) experienced less bleeding events and stent thromboses than patients with lower or higher aggregation values. Reflecting the growing body of evidence indicating a therapeutic window for P2Y12 receptor inhibitors [22,37,4649]. One option for future studies on personalized antiplatelet treatment would be to guide the therapy in order to reach the therapeutic window.

Ischaemic events also occurred in the personalized antiplatelet group in our study, although significantly less frequently. Not surprisingly, myocardial infarction, age and diabetes mellitus were independent discriminators of the composite of MACE in the personalized treatment group. It is well known that the above-mentioned variables independently influence outcome in the overall cohort of patients treated with clopidogrel [19,22]. Our study, therefore, provides a novel information that in the setting of sufficient platelet inhibition, myocardial infarction, age and diabetes are still important characteristics associated with poor outcome.

Limitations

Study results might be influenced by chance based on a limited sample size. Nevertheless, the power calculation has confirmed that the number of patients investigated allowed statistical evaluation. An additional limitation is that patients in the two study groups were included at two different centres (possible centre referral bias). Several confounders could have influenced the event rate between treatment arms over the course of 12 months that might not be related to the initial platelet function personalized strategy that was restricted rather to the periprocedural setting. Moreover, concomitant medication as PPIs and statins, which might interfere with clopidogrel metabolism and therefore influence patient's outcome [50], differed between the groups. Nevertheless, the adjustment for those factors in the multivariate Cox regression model confirmed that personalized therapy improved patient's outcome [13]. A lack of platelet function data during the chronic phase of the study might also be a limitation. Due to the limited sample size, the study was not powered to compare outcomes in patients with stable CAD comapred with those with myocardial infarction. Therefore, the study results could be interpreted as hypothesis generating. Additionally, we used only one platelet function assay which was due to the lack of funding for using two different devices. Nevertheless, despite the limitations, this study in the real-word clinical setting highlights the potential net clinical benefit of individualized antiplatelet treatment.

Conclusions

Personalized antiplatelet treatment according to platelet impedance aggregometry testing might improve the net clinical benefit compared with the non-personalized treatment. Further work is needed to define the prognostic role, value and also the cost-effectiveness for personalized antiplatelet therapy techniques to be incorporated into routine clinical practice.

AUTHOR CONTRIBUTION

Jolanta Siller-Matula: concept, design, statistical analysis, interpretation of data, writing the intellectual content, final approval of the version to be published. Marcel Francesconi: patient inclusion, data collection, revising the intellectual content, final approval of the version to be published. Carina Gruber: patient inclusion, data collection, revising the intellectual content, final approval of the version to be published. Cornelia Dechant: patient inclusion, data collection, revising the intellectual content, final approval of the version to be published. Georg Delle-Karth: patient inclusion, revising the intellectual content, final approval of the version to be published. Bernd Jilma: interpretation of data, revising the intellectual content, final approval of the version to be published. Katharina Grohs: laboratory analysis, final approval of the version to be published. Andrea Podczeck-Schweighofer: revising the intellectual content, final approval of the version to be published. Günter Christ: concept, design, analysis, interpretation of data, writing and revising the intellectual content, final approval of the version to be published.

FUNDING

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Abbreviations

     
  • AUC

    area under the curve

  •  
  • CHAID

    χ2 automatic interaction detection

  •  
  • CI

    confidence interval

  •  
  • HR

    hazard ratio

  •  
  • HTPR

    high on-treatment platelet reactivity

  •  
  • IQR

    interquartile range

  •  
  • MACE

    major adverse cardiac events

  •  
  • MADONNA

    Multiple electrode Aggregometry in patients receiving Dual antiplatelet therapy to guide treatment with Novel platelet Antagonists

  •  
  • MEA

    multiple electrode aggregometry

  •  
  • PCI

    percutaneous coronary intervention

  •  
  • PPI

    proton pump inhibitor

  •  
  • ROC

    receiver operating characteristic

  •  
  • TIMI

    thrombolysis in myocardial infarction

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