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Archives of cardiovascular diseases
Volume 104, n° 5
pages 306-312 (mai 2011)
Doi : 10.1016/j.acvd.2011.03.091
Received : 2 February 2011 ;  accepted : 3 Mars 2011
Non-adherence to aspirin in patients undergoing coronary stenting: Negative impact of comorbid conditions and implications for clinical management
Non adhérence à l’aspirine après angioplastie coronaire : identification des facteurs de risque et implication en pratique clinique

Thomas Cuisset a, , b , Jacques Quilici a, Lionel Fugon c, d, e, William Cohen b, Perrine Roux c, d, e, Bénédicte Gaborit b, Laurent Molines b, Laurent Fourcade f, Jean-Louis Bonnet a, Patrizia Carrieri c, d, e
a Department of Cardiology, CHU Timone, 264, rue Saint-Pierre, 13385 Marseille, France 
b Inserm, U626, laboratoire d’hématologie, faculté de médecine, CHU Timone, Marseille, France 
c Inserm, U912 (SE4S), Marseille, France 
d Université Aix-Marseille, IRD, UMR-S912, Marseille, France 
e ORS PACA, observatoire régional de la santé Provence-Alpes-Côte-d’Azur, Marseille, France 
f HIA Laveran, Marseille, France 

Corresponding author. Fax: ++33 4 91 25 43 36.

Premature discontinuation of and reduced adherence to antiplatelet therapy have been identified as major risk factors for stent thrombosis and poor prognosis after acute coronary syndrome.


We aimed to identify correlates of non-adherence to aspirin among patients who had undergone coronary stenting.


We prospectively included all patients who had undergone coronary stenting in our institution. Response to aspirin was assessed during the hospital phase with arachidonic acid-induced platelet aggregation (AA-Ag) and only good responders to aspirin (AA-Ag<30%) were included in the study for longitudinal assessment (n =308). Response to aspirin was reassessed 1 month after hospital discharge and non-responders received a directly observed intake of aspirin to exclude any biological non-response due to bioavailability problems. After excluding patients with such problems, response to aspirin based on platelet function testing was used to estimate non-adherence to aspirin after coronary stenting. A logistic regression model was used to identify predictors of non-adherence.


Non-adherence to aspirin concerned 14% of the study sample (n =43). After adjustment for age, those who reported the highest risk of non-adherence to aspirin were migrants (odds ratio [95% confidence interval], 8.3 [3.5–19.8], followed by patients receiving treatment for diabetes (4.5 [1.9–10.9]). Smokers had a threefold risk of non-adherence (3.1 [1.4–6.9]).


Non-adherence to aspirin is relatively frequent in populations at high risk of cardiovascular events. Appropriate case management and special interventions targeting these groups need to be implemented to avoid fatal events and assure long-term adherence to treatment.

The full text of this article is available in PDF format.

L’arrêt prématuré et la mauvaise adhérence aux antiplaquettaires ont été clairement identifiés comme des facteurs de risque majeurs de thrombose de stent et de pronostic défavorable après syndrome coronarien aigu. L’objectif de ce travail était d’identifier des facteurs de risque de mauvaise adhérence à l’aspirine après stenting coronarien. Trois cent huit patients consécutifs bénéficiant d’une angioplastie coronaire ont été inclus, si leur réponse à l’aspirine à l’hôpital, évaluée par l’agrégation à l’acide arachidonique (AA-Ag) était satisfaisante (AA-Ag<30 %). La réponse à l’aspirine était réévaluée un mois plus tard en consultation et les « non-répondeurs » recevaient une prise contrôlée d’aspirine pour identifier les patients non adhérents. Quarante-trois patients (14 %) étaient identifiés comme non adhérents. Une régression logistique était utilisée pour déterminer les facteurs de non-adhérence. Après ajustement par l’âge, les facteurs de risque de non-adhérence étaient : le caractère migrant des patients (OR [95 % intervalle de confiance], 8,3 [3,5–19,8]), les patients traités pour un diabète (4,5 [1,9–10,9]) et le tabagisme actif (3,1 [1,4–6,9]). En conclusion, la mauvaise adhérence à l’aspirine est assez fréquente après angioplastie coronaire avec des facteurs de risque identifiés rendant nécessaire le renforcement de l’éducation de ces populations à risque.

The full text of this article is available in PDF format.

Keywords : Aspirin, Coronary stenting, Non-adherence, Compliance

Mots clés : Aspirine, Angioplastie coronaire, Non-adhérence

Abbreviations : AA, AA-Ag, AUC, ROC


Platelet inhibition with aspirin and clopidogrel has significantly reduced recurrent ischaemic events after both coronary stenting and non-ST-segment elevation acute coronary syndrome [1, 2]. Nevertheless, ischaemic events still occur in clinical practice, and for patients treated with aspirin these events have been attributed by some investigators to aspirin resistance [3]. Aspirin resistance, usually assessed by arachidonic acid (AA)-induced platelet aggregation (AA-Ag), has been widely investigated [3] and is associated with adverse clinical outcomes [4, 5, 6, 7, 8]. Several mechanisms have been proposed for this wide variability in antiplatelet therapy response, including polymorphisms in platelet receptor genes, interaction with medication and malabsorption [3]. However, the primary reason for inadequate platelet inhibition in patients treated with aspirin is non-adherence. As non-adherence to aspirin is often mistaken for aspirin resistance, platelet function testing is used to assess adherence to aspirin only after exclusion of patients with real bioavailability problems and biological aspirin resistance [9]. Using this same method to detect non-adherent patients, we aimed to identify clinical and social risk factors for non-adherence to aspirin in patients who had undergone coronary stenting.

Study population and design

All patients admitted to the Department of Cardiology at La Timone Hospital in Marseilles between September 2008 and June 2009 were considered eligible to enter the study if they had: chronic therapy with aspirin 75mg for at least 1 week; undergone coronary stenting for non-ST-segment elevation acute coronary syndrome; a good in-hospital aspirin response defined by AA-Ag less than 30%.

The exclusion criteria were as follows: history of bleeding diathesis; acute coronary syndrome less than 4 days; glycoprotein IIb/IIIa antagonist less than 48h; New York Heart Association class IV; contraindications to antiplatelet therapy; platelet count less than 100 G/L; creatinine clearance less than 30mL/min; and low response to aspirin during the hospital phase (AA-Ag>30%).

Patients received non-enteric coated aspirin 75mg daily as a directly-observed therapy administered by a nurse during hospitalization, to minimize the risk of non-adherence associated with daily clopidogrel 150mg. After 3 days, the ‘in-hospital aspirin response’ was measured within 12hours after each aspirin intake using AA-Ag. Patients were discharged with a prescription of aspirin 75mg and clopidogrel 150mg daily and were provided with educational sessions highlighting the importance of patient adherence to physicians’ recommendations. One month after hospital discharge, patients were admitted to our Antiplatelet Monitoring Unit and were asked if they were actually taking their medication. Assessment of ‘outpatient response to aspirin’ with AA-Ag was then performed between 1 and 12h after each aspirin intake. Patients identified as non-responders received directly observed aspirin therapy 75mg before reassessment on the same day, 1 to 12h after administration, in order to exclude bioavailability problems and to properly identify non-adherent patients (Figure 1). Patients gave written informed consent for participation.

Figure 1

Figure 1. 

Study design.


Platelet variables

Blood samples were drawn from a peripheral venous catheter. The platelet count was determined in the platelet-rich plasma sample and adjusted to 2.5×108mL−1 with homologous platelet-poor plasma. Platelets were stimulated with AA (0.5mg/mL) and aggregation was assessed with a PAP4 aggregometer (Biodata Corporation, Wellcome, Paris, France). Aggregation was expressed as the maximal percentage change in light transmittance from baseline with the platelet-poor plasma as reference. Here we report data on maximal intensity of platelet aggregation with AA concentration. The coefficient of variation of maximal intensity of platelet aggregation with AA was 6%. Non-response to aspirin was defined by AA-Ag greater than 30%, as described previously [5].

Statistical analysis

Statistical analyses were performed using the SPSS software program, version 15.0 (SPSS Inc., Chicago, IL, USA). Potential risk factors and patients’ social and clinical characteristics were screened for inclusion in the model by testing each independently for any significant association with non-adherence, using univariate logistic regression. Variables that achieved a liberal significance level of p 0.25 in the univariate analysis were included in the multivariate model. For the multivariate analysis, a logistic regression based on a backward elimination approach was used and variables were considered to be significantly associated with the outcome if the p value was less or equal to 0.05. A good way of assessing a binary logistic regression model’s ability to accurately classify observations is to use a receiver operating characteristic (ROC) curve. A ROC curve is constructed by generating several classification tables for cut-off values ranging from 0 to 1, and calculating the sensitivity (proportion of truly non-adherent patients who were correctly identified as such) and specificity (proportion of truly adherent patients who were correctly identified as such) for each threshold value. Sensitivity is plotted against 1specificity (i.e. one minus specificity) to create a ROC curve.

The area under the ROC curve (AUC) is commonly used as a summary measure of the receiver operating characteristic curve and provides a measure of discrimination: a model with a large area under the ROC curve suggests that the model is able to accurately predict the value of an observation’s response. Hosmer and Lemeshow have provided general rules for interpreting AUC values [10]: AUC=0.5, no discrimination; 0.7 ≤ AUC<0.8, acceptable discrimination; 0.8 ≤ AUC<0.9, excellent discrimination; AUC ≥ 0.9, outstanding discrimination (this is extremely rare).


Among all those who had undergone coronary stenting, 308 patients who fulfilled the enrolment criteria were included in our study. After verifying possible bioavailability problems, non-adherent patients accounted for 14% of the study sample (n =43). The distribution of non-adherence in terms of sociodemographic variables and medical characteristics of the study population is summarized in Table 1. Mean age±standard deviation was 63±12 years, men accounted for the 81% of the sample, 4% benefited from free public health care because of their low incomes and 10% were born outside France (migrants). Thirty-five patients (11%) were receiving treatment for diabetes and 69% were receiving beta-blockers; converting enzyme inhibitors were being used by 74% patients and 94% were using statins. Moreover, more than 60% of patients had a body mass index greater than 25 and 44% were smokers.

Table 2 presents results from the univariate and multivariate analyses. Almost all the factors described previously had a p value below the 0.25 threshold in the univariate analysis; the only two variables that were not eligible were sex and use of statins. The factors most associated with non-adherence to aspirin were being a migrant and being treated for diabetes. Results from the multivariate model concerning independent factors associated with non-adherence to aspirin revealed that migrants exhibited the highest risk of non-adherence to aspirin (odds ratio [95% confidence interval]: 8.3 [3.5–19.8]), while smokers had a threefold risk of non-adherence compared with non-smokers (3.1 [1.4–6.9]). Coprescription for diabetes significantly increased the likelihood of non-adherence by a factor greater than four (4.5 [1.9–10.9]). We also evaluated the discriminatory performance of the final statistical model: the AUC was 0.78 (95% confidence interval [0.70–0.85]), which is an acceptable discrimination according to Hosmer and Lemeshow [10] (Figure 2).

Figure 2

Figure 2. 

Discriminatory performance of the multivariate model using the receiver operating characteristic (ROC) curve.



The results from the present study suggest that there is a high rate of non-adherence to aspirin in patients treated for coronary stenting during the first month after hospital discharge. The study identifies specific groups at high risk of non-adherence and, as a consequence, reduced response to treatment leading to possible fatal events. The study also identifies subgroups (such as migrants or individuals already receiving chronic treatments, such as patients with diabetes) requiring specialized interventions for improving adherence.

It has already been shown that non-adherence to aspirin is associated with recurrent ischaemic events, while the independent effect of adherence on mortality after a myocardial infarction has been consistently shown in different studies [11, 12]. More recently, premature discontinuation of antiplatelet therapy has been identified as the greatest risk factor for stent thrombosis [13]. The relatively high prevalence of patients non-adherent to aspirin suggests that all those undergoing coronary stenting should be targeted for aggressive and repeated educational sessions to ensure better adherence and sustained response to treatment. Our results are consistent with previous studies conducted in the field of adherence to thienopyridine [14, 15], where a lack of secondary school education was associated with a higher risk of treatment withdrawal. This suggests that early interventions for patients with less formal education (such as migrants) should be reinforced and diversified to ensure that they fully appreciate that adherence to prescriptions is a crucial issue for survival. These results may also influence daily practice and may advocate the systematic use of platelets tests to better monitor patients at risk of non-adherence or of being lost to follow-up. Moreover, as there is quite a strong association between non-adherence and the identified risk factors, effective interventions targeting these populations in the maintenance phase of treatment, as well as appropriate case management, could drastically reduce the risk of non-adherence and possible recurrent cardiovascular events. Previous investigations showing that a strong belief by patients in the necessity of their medications is a predictor of long-term adherence, in turn support the idea that physicians need to be vigilant in explaining the importance of sustained adherence to medications [16]. To achieve this aim, a rehabilitation programme after hospital discharge might be very helpful in improving patient comprehension and education. As side effects have already been found to be associated with reduced adherence to aspirin [17], our findings also underline the importance of both education and management of side effects in daily clinical practice and appropriate case management of patients affected by other comorbidities requiring chronic treatment. The rare occurrence of aspirin resistance in the present study strongly suggests that its occurrence in previous literature has been overestimated due to both adherence-related issues and the use of non-specific platelet tests such as PFA-100.

The concept of aspirin resistance has emerged as a potential risk factor for recurrent cardiovascular events [4, 5, 6, 7, 8]. Several mechanisms have been proposed to explain the variability of platelet inhibition with aspirin [3]. A recent review suggested a rate of aspirin non-responders of 28%, based on different functional platelet tests [18]. However, before estimating the response to antiplatelet therapy using various tests, the risk of a patient’s non-adherence should be appropriately assessed. While different tools are available to assess aspirin non-adherence, such as platelet tests and metabolite dosing, no study has yet used patient self-reporting to assess it [6, 8]. As self-reported adherence may be under-reported because of social desirability bias, a simple self-reported measure may not be effective enough to identify non-adherent patients. It is possible therefore that in order to accurately assess adherence to aspirin using self-reports, a special tool would need to be developed. Such tools are already being used to measure adherence to other chronically administered treatments (e.g. for diabetes, asthma and human immunodeficiency virus infection) [19, 20]. In our study, we observed an association between age and non-compliance. This finding has already been reported in a previous study by Tuppin et al. [21].

In this study, we demonstrated the additional value of platelet function testing for identification of non-adherent patients. AA-Ag is an excellent qualitative assay for the detection of platelet inhibition by aspirin. A single dose of aspirin, even lower than 100mg, will inhibit AA-Ag for more than 48h. Accordingly, the patients identified as non-adherent had not followed the prescription for at least 3 days. In our previous study [9], we already observed that self-reported non-compliance to aspirin is not effective and that AA-Ag is probably the best qualitative method for detecting aspirin compliance. In the next few years, a different formulation of aspirin (e.g. polypill), rather than the powder formulation, may be required and may also be tested using self-reports to assess adherence.

Study limitations

Some limitations need to be acknowledged. This was a single centre study from a southern European country. However, as access to care is free for the French population, it is unlikely that most marginalized populations were under-represented. The data collection included only a limited number of factors and it is possible that important predictors may have been not revealed by this analysis. We did not simultaneously assess non-adherence to clopidogrel, as no distinguishing platelet function tests are currently able to discriminate non-responders from non-adherent subjects. This is because of the wide variability of platelet response to clopidogrel in adherent patients.


Our results illustrate that after identifying non-responders to aspirin in patients undergoing coronary stenting, non-adherence behaviours should be detected and prevented before treatment with alternative and/or additional antiplatelet medications. Future research should focus on the evaluation of future interventions to improve adherence in at-risk populations – particularly multitreated patients – especially in the initial phase, which remains the most critical period for this population.

Disclosure of interest

The authors declare that they have no conflicts of interest concerning this article.


We gratefully acknowledge the assistance of our teams of nurses and technicians in the execution of this study. We also thank Jude Sweeney for the English revision and editing of our manuscript.


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