CC BY
44
Research Results in Pharmacology
Research Results in Pharmacology 6(2): 97-103
UDC: 615.331 DOI 10.3897/rrpharmacology. 6.54130
3 Research Article
Medication adherence in patients with stable coronary artery disease in primary care
Sergey K. Zyryanov1, Sergey B. Fitilev1, Alexander V. Vozzhaev1, Irina I. Shkrebniova1, Natalya N. Shindryaeva2, Dmitry A. Klyuev1, Liusine N. Stepanyan1, Nikolay N. Landyshev1, Yana G. Voronko1
1 RUDN University, Medical Institute, Department of Pharmacology and Clinical Pharmacology, 6Miklukho-Maklaya St., Moscow 117198, Russia
2 City Polyclinic #2 of Moscow Healthcare Department, 12 Fruktovaya St., Moscow 117556, Russia
Corresponding author: Alexander V. Vozzhaev (vozzhaev-av@rudn.ru)
Academic editor: Tatyana Pokrovskaya ♦ Received 11 April 2020 ♦ Accepted 15 May 2020 ♦ Published 30 June 2020
Citation: Zyryanov SK, Fitilev SB, Vozzhaev AV, Shkrebniova II, Shindryaeva NN, Klyuev DA, StepanyanLN, Landyshev NN, Voronko YG (2020) Medication adherence in patients with stable coronary artery disease in primary care. Research Results in Pharmacology 6(2): 97-103. https://doi.org/10.3897/rrpharmacology.6.54130
Abstract
Introduction: Lack of research targeting non-adherence to cardiovascular medications in Russia prevents from developing effective interventions to improve adherence. The aim was to study medication adherence in patients with stable coronary artery disease in primary care.
Material and methods: The study was conducted in a primary care setting of Moscow. Demography, medical history, pharmacotherapy data were obtained retrospectively from 386 coronary patients' medical records. Medication adherence was measured by 8-item Morisky Medication Adherence Scale (MMAS-8). A statistical analysis was performed using SPSS Statistics V16.0.
Results and discussion: According to the results from MMAS-8, 188 (48.7%) coronary patients had high medication adherence, 135 (35.0%) - moderate, and 63 (16.3%) - low. By the dichotomous interpretation: 48.7% (n = 188) - were adherent, 51.3% (n = 198) - were non-adherent. These groups were similar in gender distribution, age, and medical history profile (p > 0.1 for all variables). Smokers prevailed in the non-adherent group (13.6 vs. 5.3%; p = 0.009). Both groups were equally prescribed beta-blockers, antiplatelets, and statins (p > 0.1 for all). Use of fixed dose combinations (11.7 vs. 5.6%; p = 0.048) and the number of pills taken (mean 5.64 ± 1.52 vs. 5.99 ± 1.62; p = 0.029) were associated with better adherence. Higher values of total cholesterol (mean 5.2 ± 1.4 vs. 4.7 ± 1.2 mmol/L; p < 0.001) and low-density lipoprotein cholesterol (mean 2.9 ± 1.2 vs. 2.4 ± 0.9 mmol/L; p < 0.001) were revealed in non-adherents. Subjects with suboptimal adherence visited general practitioners more frequently (median 5 vs. 3 visits; p = 0,003).
Conclusion: Medication non-adherence in coronary outpatients exceeded 50%. High adherence was associated with more frequent use of fixed dose combinations and fewer pills taken by patient. Smoking and poorer control of blood lipids prevailed in non-adherents, who also caused higher load on general practitioners.
Keywords
coronary artery disease, medication adherence, Morisky scale, primary care.
Copyright Zyryanov SK et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Introduction
The famous quote of American surgeon Dr. C. Everett Coop - "Drugs don't work in patients who don't take them" (1985) - has become especially relevant over the recent years to highlight an important role of adequate medication adherence in effective pharmacotherapy. Developed countries have already built upscale healthcare systems, so a further increase in pharmacological performance strongly depends on improvement of patient adherence to evidence-supported medications. The global nature of this problem was first raised by The World Health Organization back in 2003 stating that "adherence to long-term therapy for chronic illnesses in developed countries averages 50%. In developing countries, the rates are even lowef' (De Geest and Sabate 2003).
According to later studies, medication adherence in patients with coronary artery disease (CAD) is on average 30-70% (Chowdhuiy et al. 2013; Chen et al. 2015). Such a situation was described as «non-adherence pandemic» (Kolandaivelu et al. 2014). Meanwhile, there is enough evidence that poor adherence is associated with unfavorable clinical outcomes (Du et al. 2015; Lenzi et al. 2017) and higher treatment costs (Bitton et al. 2013) in CAD patients.
In the Russian outpatient care practice, adherence to cardiovascular medications is far from being optimal, and this issue lacks proper scientific attention (Bochkareva et al. 2019). It seems difficult to develop and implement effective interventions to improve adherence without such information. So, the aim of this research was to study medication adherence in patients with stable CAD at the primary care level.
Material and methods
The study was conducted in a large outpatient healthcare facility of Moscow city as part of the Pharmacoepidemiology Quality Improvement Program of Pharmacotherapy of Stable CAD in Primary Care. The study was approved by the Ethics Committee of this medical institution. At the first stage, 2000 medical records of cardiologic patients treated at the facility were randomly selected. Out of this sample, 805 outpatient records were included into a retrospective analysis in line with the following criteria: age 30 years and older, verified CAD, non-participation in any ongoing clinical trial. The following data were obtained: demographics, medical histories including any documented cardiovascular behavioural risk factors, available results of laboratory tests (lipid profile and gly-cemic status), pharmacotherapy prescribed to patients by cardiologists, and additional medication maintenance statuses. The information on the number of visits to the cardiologist and general practitioner over the twelve-month period was also collected.
At the second stage, the assessment of medication adherence in coronary patients was conducted. The 8-item Mo-risky Medication Adherence Scale (MMAS-8) was used
to measure adherence. The patients were interviewed via telephone. Patient self-report tools (questionnaires, scales) are broadly used for exploring adherence to cardiovascular medications because they are inexpensive, simple, and quite accurate (Shi et al. 2010; Culig and Leppee 2014; Nguyen et al. 2014). One of such self-report tools is Mo-risky scale (4-item or 8-item), which was originally developed and validated in patients with arterial hypertension. The 8-item version has better validation parameters compared with the 4-item scale: internal consistency reliability (described by Cronbach's alpha) 0.83 vs. 0.61, sensitivity 0.93 vs. 0.81, specificity 0.53 vs. 0.44. It also has strong correlation with validation criteria (Morisky et al. 1986, 2008). Interpretation of MMAS-8 was performed in a standard way. The patients were asked to answer questions 1-7 as "yes" (0 points) or "no" (1 point), except question 5, which was scored the opposite. Question 8 had a five-point Likert response scale and was scored 1 point for the answer "never". Summing up the points, adherence was assessed as low (less than 6 points), moderate (6-7 points) or high (8 points). The simplified dichotomous interpretation of MMAS-8 (adherent - 8 points, non-adherent - less than 8 points) was also used (Tan et al. 2014).
The data from the medical records and questionnaires were transferred to patients' case report forms. The study database was constructed in MS Excel. Statistical data processing was performed using IBM SPSS Statistics V16.0 (IBM, Armonk, NY, USA). Continuous variables were expressed as mean (M), standard deviation (SD), first (Q1), second (median, Q2) and third (Q3) quartiles. Categorical variables were expressed as frequencies and percentages. Significance of the differences between the groups was estimated by standard statistical tests (two-sided). Independent /-tests for two independent samples were used for continuous variables distributed approximately according to the normal law; Wilcoxon rank-sum tests were used for continuous variables not distributed approximately according to the normal law. Kolmogor-ov-Smirnov normality tests were used to check the normality of distribution. Chi-square tests were used for categorical variables. The level of statistical significance was set atp < 0.05.
Results and discussion
Data from 386 patients with established stable CAD who gave full and unambiguous replies to all MMAS-8 questions were included into the analysis. According to MMAS-8 scoring, 188 coronary patients had high medication adherence, 135 - moderate, and 63 - low (Fig. 1).
It is notable that in the outpatient cardiovascular registry "PROFILE" the researchers also measured medication adherence (n = 130) by MMAS-8 tool. The results revealed that 40.8% of patients had high adherence, 36.9% - moderate, and 22.3% - low (Lukina et al. 2018). But the population was different in some variables, like age and medical history profile from the one described in this study.
Figure 1. Medication adherence in patients with stable coronary artery disease (n = 386).
The detailed analysis of non-adherent patients' responses to specific questions MMAS-8 revealed signs of unintentional non-adherence due to forgetfulness (Fig. 2A), which could be expected with regard to the age profile of the study population (mean age 68.9 ± 9.9 years; share of patients > 65 years - 66.8%). However, quite a large number of patients turned out to have intentional non-adherence due to feeling worse (34.2%) or better (31.7%) when taking the prescribed medications (Fig. 2B). Almost one-third of the participants felt hassled about sticking to their treatment regimens. The investigators of the "PROFILE" registry mentioned above reported 23.5% and 23.6% of the patients were prone to breaking the recommended treatment plan when feeling worse or better, respectively (Lukina et al. 2018).
Besides, it seemed interesting to analyze the responses of moderately adherent patients, who were kind of "one step" away from being highly adherent. What prevented them from making this "step"? It turned out the intentional non-adherence was the first to "be blamed" for that (Fig. 3). It is important to pay specific attention
to this finding because it makes the primary direction of possible interventions to improve medication adherence obvious. Analyzing the type of non-adherence is crucial as it should be addressed in different ways, like educational and physician-focused activities to modify intentional non-adherence or behavioral patient-focused strategies to target unintentional non-adherence.
The dichotomous interpretation of MMAS-8 was applied for further analysis. The patients with moderate and low adherence formed the group of non-adherents (51.3%). Such a distribution partly matches available Russian data concerning outpatients with arterial hypertension and CAD, treated at Moscow primary care facilities. According to that data, 61.1% of patients had poor medication adherence (< 3 points by 4-item Morisky scale). Yet, the target population was younger, and prevalence of CAD was less than 50% (Fofanova et al. 2017).
The category of patient-related factors of non-adherence is most well studied by now. But there is still no single opinion on this issue. Among possible predictors of poor adherence to cardiovascular medications, the following were mentioned: younger and older age, male sex, low level of income, smoking, forgetfulness due to cognitive disorders, distrust of a healthcare provider, lack of faith in successful treatment outcome, etc. (Warren et al. 2013; Kolandaivelu et al. 2014; Khatib et al. 2019).
First, the groups of adherent and non-adherent patients were compared in respect to the demographic and medical history data (Table 1). The number of male subjects was similar in both groups. No statistically significant differences were identified in prevalence of concomitant conditions, so the groups were comparable in respect to the medical history profiles.
Unfortunately, it appeared to be difficult to analyze prevalence of cardiovascular risk factors in adherent and
A 50n 403020100
B
Forgot to take medications
Figure 2. Reasons of unintentional (A) and intentional (B) non-adherence among non-adherent patients (n = 198).
A 30-1
20-
10-
^
jp y Jf J>
cf y * $ <f
Forgot to take medications
B 40—i
30-
20-
10-
Stopped medications feeling
Figure 3. Reasons of unintentional (A) and intentional (B) non-adherence among moderately adherent patients (n = 135).
Table 1. Demographics and Medical History Profile of Adherent and Non-adherent Patients with Stable Coronary Artery Disease.
Variable Adherent (n = 188) Non-adherent (n = 198) P
Gender:
Male, n (%) 66 (35.1) 72 (36.4) 0.797
Female, n (%) 122 (64.9) 126 (63.6)
Age (years), 69.9 ± 8.9 70.4 ± 9.2 0.909
M ± sd (Ql, Q2, Q3) (64, 70, 77) (63, 70, 78)
Myocardial infarction, n (%) 86 (45.7) 87 (43.9) 0.721
Revascularization, n (%) 80 (42.6) 75 (37.9) 0.349
Stable angina, n (%) 128 (68.1) 142 (71.7) 0.437
Arterial hypertension, n (%) 174 (92.6) 185 (93.4) 0.734
Chronic heart failure, n (%) 168 (89.4) 175 (88.4) 0.760
Atrial fibrillation, n (%) 54 (28.7) 66 (33.3) 0.385
Diabetes, n (%) 44 (23.4) 44 (22.2) 0.782
Dyslipidemia, n (%) 31 (16.5) 37 (18.7) 0.571
Chronic kidney disease, n (%) 40 (21.3) 33 (16.7) 0.248
Table 2. Pharmacotherapy Patterns of Adherent and Non-adherent Patients with Stable Coronary Artery Disease.
Variable Adherent (n = 188) Non-adherent (n = 198) P
Beta-blockers, n (%) 144 (76.6) 156 (78.8) 0.605
Calcium channel blockers, n (%) 87 (46.3) 91 (46.0) 0.950
Long-term nitrates, n (%) 18 (9.6) 10 (5.1) 0.087
Antiplatelet agents, n (%) 135 (71.8) 133 (67.2) 0.323
Oral anticoagulants, n (%) 58 (30.9) 78 (39.4) 0.079
Statins, n (%) 159 (84.6) 166 (83.8) 0.843
ACEf inhibitors, n (%) 93 (49.5) 82 (41.4) 0.112
ARBst, n (%) 82 (43.6) 92 (46.5) 0.574
ACEf inhibitors or ARBst n (%) 175 (93.1) 173 (87.4) 0.060
Fixed dose combinations, n (%) 22 (11.7) 11 (5.6) 0.048
Number of pills taken, 5.64 ± 1.52 5.99 ± 1.62 0.029
M ± SD (Ql, Q2, Q3) (2, 6, 10) (2, 6, 12)
Note: ^angiotensin-converting enzyme; ^angiotensin II receptor blockers.
non-adherent patients due to the low registration rate of such information in medical records. Only a smoking status was an exception. So, 3-time higher prevalence of smoking was identified in the group of non-adherent patients (13.6 vs. 5.3%; p = 0.009). The possible explanation here could be the "healthy adherer effect", which describes the association of better medication adherence with a patient's healthier lifestyle (Gehi et al. 2007; Ladova et al. 2014).
Another key factor of medication adherence is the quality of prescribed pharmacological treatment (Lukina et al. 2017). Therefore, the next step was to compare the phar-macotherapy patterns of adherent and non-adherent coronary patients in regard with prescription rates of relevant medications (Table 2). The positive aspect was that almost all the patients were recommended drugs to prevent thrombosis. Prescription rates of oral anticoagulants (OACs) correlated with prevalence of atrial fibrillation. The tendency (p = 0.079) to higher rates of OACs in non-adherent patients was revealed. It might be explained by high costs
of these medicines (above 70% of patients were on novel OACs) and a risk of bleeding. Statins were prescribed to 84.2% (n = 325) of participants with similar rates in adherents and non-adherents. Renin-angiotensin-aldosterone system (RAAS) inhibitors were recommended to 90.2% (n = 348) of the patients. No statistical significance was identified in prescription rates of angiotensin-converting enzyme (ACE) inhibitors and angiotensin II receptor blockers (ARBs) between the groups. However, there was an obvious tendency (p = 0.060) to the association of RAAS inhibitors intake with better adherence, possibly, due to a rather favorable safety profile of these medications.
The first line medications - beta-blockers - were justifiably top recommended (77.7%, n = 300) among anti-anginal drug therapies. Long-term nitrates were used only in 7.3% (n = 28) of cases. And calcium channel blockers were prescribed to 46.1% (n = 178) of patients. The patterns of antianginal pharmacotherapy were similar in adherent and non-adherent subjects. The only exception was
nitrates having the tendency (p = 0.087) to association with better adherence.
The proven strategy to improve a patient's adherence is to prescribe fixed dose combinations (Castellano et al. 2014; Fuller et al. 2017). Unfortunately, such an approach was applied only to 8.5% (n = 33) of participants. This must be admitted as a negative sign considering the results of comparison between the groups. The use of fixed dose combinations was associated with higher adherence (p = 0.048). Besides, the number of pills taken by a patient was less in the adherent group (p = 0.029), which partly confirmed the role of polypharmacy as an important predictor of non-adherence. Furthermore, the study by Khatib et al. (2019) demonstrated that the number of medicines taken by coronary patients (n = 503) was an independent predictor of intentional non-adherence (odds ratio 1.18; 95% CI 1.07-1.31).
Thus, the groups of adherent and non-adherent patients with stable CAD turned out to be similar in demographics, medical history profiles and pharmacotherapy patterns, which made it especially interesting to compare the results of cardiovascular risk factors management between these groups. In that regard, the available clinical and laboratory data were analyzed (Table 3). The groups were similar in body mass index. Mean figures of blood pressure also were comparable, although this might be explained by the fact that the patients followed physician recommendations more precisely shortly before the visit to the cardiologist (Feinstein 1990). The studied groups were similar in the glycemic status and some parameters of the lipid profile (triglycerides, high-density lipoprotein cholesterol). However, the levels of total cholesterol and low-density lipoprotein (LDL) cholesterol were higher (p < 0.001) in non-adherent patients.
The final task of this research was to calculate the number of visits to the cardiologist and general practitioner over the twelve-month period and define the prevalence of additional medication maintenance in adherent and non-adherent coronary patients (Table 4). So, the participants from the both groups were visiting cardiologists with the same frequency, probably due to comparable severity of their cardiological medical histories. But non-adherent patients needed to visit general practitioners more often (p = 0.003), which might be a sign of more frequent complaints and episodes of feeling unwell. The status of additional medication maintenance provided no better adherence. The study by Fofanova et al. (2017) revealed no association either between the additional medication maintenance status and adherence in outpatients with arterial hypertension and CAD.
Table 3. Modifiable Risk Factors in Adherent and Non-adherent Patients with Stable Coronary Artery Disease.
Variable Adherent (n = 188) Non-adherent (n = 198) P
Body mass index (kg/m2), 29.6 ± 4.8 29.3 ± 4.6 0.788
M ± SD (Q1, Q2, Q3) (26.2, 29.3, 32.6) (26.3, 29.2, 32.1)
Systolic blood pressure (mm Hg), 135 ± 18 134 ± 20 0.588
M ± SD (Q1, Q2, Q3) (120, 130, 141) (120, 130, 150)
Diastolic blood pressure (mm Hg), 79 ± 9 78 ± 10 0.380
M ± SD (Q1, Q2, Q3) (70, 80, 80) (70, 80, 80)
Total cholesterol (mmol/L), 4.7 ± 1,2 5.2 ± 1.4 <0.001
M ± SD (Q1, Q2, Q3) (3.7, 4.5, 5.4) (3.6, 5.0, 5.8)
LDL^ cholesterol (mmol/L), 2.4 ± 0.9 2.9 ± 1.2 <0.001
M ± SD (Q1, Q2, Q3) (1.8, 2.2, 2.8) (1.9, 2.5, 3.5)
Triglycerides (mmol/L), 1.7 ± 0.9 1.9 ± 1.5 0.755
M ± SD (Q1, Q2, Q3) (1.0, 1.4, 2.0) (1.0, 1.4, 2.1)
HDL* cholesterol (mmol/L), 1.4 ± 0.3 1.3 ± 0.3 0.706
M ± SD (Q1, Q2, Q3) (1.1, 1.4, 1.6) (1.2, 1.4, 1.5)
Glycosylated hemoglobin (%), 6.9 ± 1.4 6.9 ± 1.2 0.677
M ± SD (Q1, Q2, Q3) (6.1, 6.6, 7.3) (6.3, 6.6, 7.1)
Fasting plasma glucose (mmol/L), 6.7 ± 3.1 6.1 ± 1.5 0.710
M ± SD (Q1, Q2, Q3) (4.7, 5.3, 7.5) (5.1, 5.6, 6.6)
Note: ^low-density lipoprotein; ^high-density lipoprotein.
Table 4. Use of Primary Care Facility Resources by Adherent and Non-adherent Patients with Stable Coronary Artery Disease.
Variable Adherent Non-adherent P
(n = 188) (n = 198)
Visits to cardiologist, median (Q1, Q3) 3.5 (2, 7) 4 (2, 8) 0.376
Visits to general practitioner, median (Q1, Q3) 3 (1, 7.25) 5 (2, 9) 0.003
Share of patients that visited general practitioner, 146 (77.7) 175 (88.4) 0.005
n (%)
Share of patients that had additional medication 96 (51.1) 101 (51.0) 0.992
maintenance, n (%)
Conclusion
The prevalence of medication non-adherence in patients with stable CAD at a primary care setting was more than 50%. Patient's age and sex, medical history profile and pharmacotherapy pattern were not associated with better or worse adherence. High adherence was related to the use of fixed dose combinations and to fewer cardiovascular medications taken by the patient. Smoking and poorer control of blood lipids (total cholesterol and LDL cholesterol) prevailed in non-adherent patients, who also caused a higher load on general practitioners. An additional medication maintenance status had no influence on medication adherence of coronary patients. Further local research is needed to address this serious problem.
Conflict of interest
The authors have neither funding nor support to report.
The authors have no competing interests to declare.
References
■ Bitton A, Choudhry NK, Matlin OS, Swanton K, Shrank WH (2013) The impact of medication adherence on coronary artery disease costs and outcomes: a systematic review. The American Journal of Medicine 126(4): 357.e7-357.e27. https://doi.org/10.1016/j.am-jmed.2012.09.004 [PubMed]
■ Bochkareva EV, Butina EK, Kim IV, Kontsevaya AV, Drapkina OM, Leon D, McKee M (2019) Adherence to antihypertensive medication in Russia: a scoping review of studies on levels, determinants and intervention strategies published between 2000 and 2017. Archives of
Public Health 77: 1-43. https://doi.org/10.1186/s13690-019-0366-9 [PubMed] [PMC]
■ Boitsov SA, Pogosova NV, Bubnova MG, Drapkina OM, Gavrilova NE, Eganyan RA, Kalinina AM, Karamnova NS, Kobalava ZhD, Koncevaja AV, Kuharchuk VV, Luk'janov MM, Maslennikova GJa, Marcevich SJa, Metel'skaja VA, Meshkov AN, Oganov RG, Popo-vich MV, Sokolova OJu, Suhareva OJu, Tkacheva ON, Shal'nova SA, Shestakova MV, Jufereva JuM, Javelov IS (2018) Cardiovascular prevention 2017. National guidelines. Russian Journal of Cardiology [Rossiiskii Kardiologicheskii Zhurnal] (6): 7-122. https://doi. org/10.15829/1560-4071-2018-6-7-122 [in Russian]
■ Castellano JM, Sanz G, Peñalvo JL, Bansilal S, Fernández-Ortiz A, Alvarez L, Guzmán L, Linares JC, García F, D'Aniello F, Arnáiz JA, Varea S, Martínez F, Lorenzatti A, Imaz I, Sánchez-Gómez LM, Roncaglioni MC, Baviera M, Smith Jr SC, Taubert K, Pocock S, Brotons C, Farkouh ME, Fuster V (2014) A polypill strategy to improve adherence: results from the FOCUS project. Journal of the American College of Cardiology 64(20): 2071-2082. https://doi. org/10.1016/j.jacc.2014.08.021 [PubMed]
■ Chen HY, Saczynski JS, Lapane KL, Kiefe CI, Goldberg RJ (2015) Adherence to evidence-based secondary prevention pharmacother-apy in patients after an acute coronary syndrome: A systematic review. Heart & Lung: The Journal of Critical Care 44(4): 299-308. https://doi.org/10.1016/j.hrtlng.2015.02.004 [PubMed]
■ Chowdhury R, Khan H, Heydon E, Shroufi A, Fahimi S, Moore C, Stricker B, Mendis S, Hofman A, Mant J, Franco OH (2013) Adherence to cardiovascular therapy: a meta-analysis of prevalence and clinical consequences. European Heart Journal 34(38): 2940-2948. https://doi.org/10.1093/eurheartj/eht295 [PubMed]
■ Culig J, Leppée M (2014) From Morisky to Hill-bone; self-reports scales for measuring adherence to medication. Collegium Antropo-logicum 38(1): 55-62. [PubMed]
■ De Geest S, Sabaté E (2003) Adherence to long-term therapies: evidence for action. European Journal of Cardiovascular Nursing: Journal of the Working Group on Cardiovascular Nursing of the European Society of Cardiology 2(4): 1-323. https://doi.org/10.1016/ S1474-5151(03)00091-4 [PubMed]
■ Du L, Cheng Z, Zhang Y, Li Y, Mei D (2017) The impact of medication adherence on clinical outcomes of coronary artery disease: A meta-analysis. European Journal of Preventive Cardiology 24(9): 962-970. https://doi.org/10.1177/2047487317695628 [PubMed]
■ Feinstein AR (1990) On white-coat effects and the electronic monitoring of compliance. Archives of Internal Medicine 150(7): 13771378. https://doi.org/10.1001/archinte.150.7.1377 [PubMed]
■ Fofanova TV, Ageev FT, Smirnova MD, Deev AD (2017) Adherence to therapy in the outpatient setting: the ability to identify and assess the effectiveness of therapy. Cardiology [Kardiologiia] 57(7): 35-42. https://doi.org/10.18087/cardio.2017.7.10004 [PubMed] [in Russian]
■ Fuller RH, Perel P, Navarro-Ruan T, Nieuwlaat R, Haynes RB, Huffman MD (2018) Improving medication adherence in patients with cardiovascular disease: a systematic review. Heart (British Cardiac Society) 104(15): 1238-1243. https://doi.org/10.1136/heart-jnl-2017-312571 [PubMed]
■ Gehi AK, Ali S, Na B, Whooley MA (2007) Self-reported medication adherence and cardiovascular events in patients with stable coronary heart disease: the heart and soul study. Archives of Internal Medicine 167(16): 1798-1803. https://doi.org/10.1001/archinte.167.16.1798 [PubMed] [PMC]
■ Khatib R, Marshall K, Silcock J, Forrest C, Hall AS (2019) Adherence to coronary artery disease secondary prevention medicines: exploring modifiable barriers. Open Heart 6(2): e000997. https://doi. org/10.1136/openhrt-2018-000997 [PubMed] [PMC]
■ Kolandaivelu K, Leiden BB, O'Gara PT, Bhatt DL (2014) Non-adherence to cardiovascular medications. European Heart Journal 35(46): 3267-3276. https://doi.org/10.1093/eurheartj/ehu364 [PubMed]
■ Ladova K, Vlcek J, Vytrisalova M, Maly J (2014) Healthy adherer effect - the pitfall in the interpretation of the effect of medication adherence on health outcomes. Journal of Evaluation in Clinical Practice 20(2): 111-116. https://doi.org/10.1111/jep.12095 [PubMed]
■ Lenzi J, Rucci P, Castaldini I, Protonotari A, Di Pasquale G, Di Mar-tino M, Perrone E, Forti P, Fantini MP (2015) Does age modify the relationship between adherence to secondary prevention medications and mortality after acute myocardial infarction? A nested case-control study. European Journal of Clinical Pharmacology 71(2): 243250. https://doi.org/10.1007/s00228-014-1793-8 [PubMed]
■ Lukina YV, Kutishenko NP, Martsevich SY (2017) Treatment adherence: modern view on a well known issue. Cardiovascular Therapy and Prevention [Kardiovaskulyarnaja Terapiya i Profilaktika] 16(1): 91-95. https://doi.org/10.15829/1728-8800-2017-1-91-95 [in Russian]
■ Lukina YV, Dmitrieva NA, Kutishenko NP, Kiseleva NV, Martsevich SY (2018) The relationship and interinfluence of aspects of therapy safety and compliance in patients with cardiovascular diseases (by the data from outpatient registry "PROFILE"). Cardiovascular Therapy and Prevention [Kardiovaskulyarnaya Terapiya i Profilaktika] 17(5): 72-78. https://doi.org/10.15829/1728-8800-2018-5-72-78 [in Russian]
■ Morisky DE, Green LW, Levine D M (1986) Concurrent and predictive validity of a self-reported measure of medication adherence. Medical Care 24(1): 67-74. https://doi.org/10.1097/00005650-198601000-00007 [PubMed]
■ Morisky DE, Ang A, Krousel-Wood M, Ward HJ (2008) Predictive validity of a medication adherence measure in an outpatient setting. Journal of Clinical Hypertension (Greenwich, Conn.) 10(5): 348-354. https://doi.org/10.1111/j.1751-7176.2008.07572.x [PubMed] [PMC]
■ Nguyen TM, La Caze A, Cottrell N (2014) What are validated self-report adherence scales really measuring?: a systematic review. British Journal of Clinical Pharmacology 77(3): 427-445. https://doi. org/10.1111/bcp.12194 [PubMed] [PMC]
■ Oganov RG, Simanenkov VI, Bakulin IG, Bakulina NV, Barbarash OL, Boytsov SA, Boldueva SA, Garganeeva NP, Doshchitsin VL, Ka-rateev AE, Kotovskaya YV, Lila AM, Lukyanov MM, Morozova TE, Pereverzev AP, Petrova MM, Pozdnyakov YM, Syrov AV, Tarasov AV, Tkacheva ON, Shalnova SA (2019) Comorbidities in clinical practice. Algorithms for diagnostics and treatment. Cardiovascular Therapy and Prevention [Kardiovaskulyarnaya Terapiya i Profilaktika] 18(1): 5-66. https://doi.org/10.15829/1728-8800-2019-1-5-66 [in Russian]
■ Shi L, Liu J, Fonseca V, Walker P, Kalsekar A, Pawaskar M (2010) Correlation between adherence rates measured by MEMS and self-reported questionnaires: a meta-analysis. Health and Quality of Life Outcomes 8: 1-99. https://doi.org/10.1186/1477-7525-8-99 [PubMed] [PMC]
■ Tan X, Patel I, Chang J (2014) Review of the four item Morisky Medication Adherence Scale (MMAS-4) and eight item Morisky Medication Adherence Scale (MMAS-8). Innovations in Pharmacy 5(3): Article 165. https://doi.org/10.24926/iip.v5i3.347
■ Warren JR, Falster MO, Fox D, Jorm L (2013) Factors influencing adherence in long-term use of statins. Pharmacoepidemiology and Drug Safety 22(12): 1298-1307. https://doi.org/10.1002/pds.3526 [PubMed]
Author Contributions
■ Sergey K. Zyryanov, Doctor Habilitated of Medical Sciences, Professor, Head of the Department of Pharmacology and Clinical Pharmacology, Medical Institute, RUDN University, e-mail: sergey.k.zyryanov@gmail.com, ORCID ID https://orcid.org/0000-0002-6348-6867. The author was the research program supervisor and contributed to the concept of the study.
■ Sergey B. Fitilev, Doctor Habilitated of Medical Sciences, Professor, Professor of the Department of Pharmacology and Clinical Pharmacology, Medical Institute, RUDN University, e-mail: fitilevsb@yandex.ru, ORCID ID https://orcid.org/0000-0001-8395-419X. The author contributed to the concept and design of the study, analysis and interpretation of the study results, and final editing of the article.
■ Alexander V. Vozzhaev, Candidate of Biological Sciences, Associate Professor of the Department of Pharmacology and Clinical Pharmacology, Medical Institute, RUDN University, e-mail: vozzhaev-av@rudn.ru, ORCID ID https://orcid.org/0000-0002-2687-5986. The author had the primary role in data collection, statistical data processing, analysis and interpretation of the study results, prepared the draft version of the article, and translated the final version of the article into English.
■ Irina I. Shkrebniova, Candidate of Medical Sciences, Associate Professor of the Department of Pharmacology and Clinical Pharmacology, Medical institution, RUDN University, Candidate of Medical Sciences, Associate Professor, e-mail: ishkrebneva@yandex.ru, ORCID ID https://orcid.org/0000-0002-0070-3115. The author contributed to optimization of data collection tools, contributed to interpretation of the results of the study and scientific editing of the article.
■ Natalya N. Shindryaeva, Doctor of Medical Sciences, Professor,Head Physician of City Polyclinic #2 of Moscow Healthcare Department, e-mail: mont76@mail.ru, ORCID ID https://orcid.org/0000-0001-6560-2756. The author was the scientific consultant and provided assistance in organization of the study conduct and clinical data collection.
■ Dmitry A. Klyuev, post-graduate student at the Department of Pharmacology and Clinical Pharmacology, Medical Institute, RUDN University, e-mail: dmitrijkluev070496@gmail.com, ORCID ID https://orcid.org/0000-0003-2400-3938. The author contributed to the data collection and statistical processing.
■ Liusine N. Stepanyan, post-graduate student at the Department of Pharmacology and Clinical Pharmacology, Medical Institute, RUDN University, e-mail: lyusine.stepanyan@yandex.ru, ORCID ID https://orcid.org/ 00000002-6012-8917. The author contributed to optimization of data collection tools and data collection.
■ Nikolay N. Landyshev, student of Medical Institute, RUDN University, e-mail: nikolay.landyshev@gmail.com, ORCID ID https://orcid.org/0000-0002-9289-6849. The author contributed to optimization of study database and data collection.
■ Yana G. Voronko, student of Medical Institute, RUDN University, e-mail: yana9voronko@yandex.ru, ORCID ID https://orcid.org/0000-0003-0779-5742. The author contributed to the data collection.
