Abstract
To assess the practices of physicians in 12 European countries in the primary prevention of cardiovascular disease (CVD).
In 2009, 806 physicians from 12 European countries answered a questionnaire, delivered electronically or by post, regarding their assessment of patients with cardiovascular risk factors, and their use of risk calculation tools and clinical practice guidelines (ClinicalTrials.gov number: NCT00882336). Approximately 60 physicians per country were selected (participation rate varied between 3.1% in Sweden and 22.8% in Turkey).
Among participating physicians, 85.2% reported using at least one clinical guideline for CVD prevention. The most popular were the ESC guidelines (55.1%). Reasons for not using guidelines included: the wide choice available (47.1%), time constraints (33.3%), lack of awareness of guidelines (27.5%), and perception that guidelines are unrealistic (23.5%). Among all physicians, 68.5% reported using global risk calculation tools. Written charts were the preferred method (69.4%) and the most commonly used was the SCORE equation (35.4%). Reasons for not using equations included time constraints (59.8%), not being convinced of their usefulness (21.7%) and lack of awareness (19.7%). Most physicians (70.8%) believed that global risk-equations have limitations; 89.8% that equations overlook important risk factors, and 66.5% that they could not be used in elderly patients. Only 46.4% of physicians stated that their local healthcare framework was sufficient for primary prevention of CVD, while 67.2% stated that it was sufficient for secondary prevention of CVD.
A high proportion of physicians reported using clinical guidelines for primary CVD prevention. However, time constraints, lack of perceived usefulness and inadequate knowledge were common reasons for not using CVD prevention guidelines or global CVD risk assessment tools.
Introduction
In recent decades, there has been a significant reduction in cardiovascular disease (CVD) event rates in several European countries, largely due to interventions to reduce cardiovascular risk factors both in individual patients and in the general population.1–4 There has also been a significant improvement in the care of patients during the acute phase of coronary events.5–7 However, CVD remains a leading cause of death in Europe, and there is still a need for improved treatment and risk factor management.8,9
Several sets of guidelines outline recommended treatments for patients with CVD risk factors.10–13 In addition, there are several tools for assessing the risk of CVD in individual patients, use of which is widely advocated in clinical guidelines.13–15 However, guidelines are not always followed precisely8,9,16 nor risk assessment tools used as intended.17–19 Several barriers to the use of guidelines have been identified, including the existence of too many different guidelines, lack of familiarity with the available guidelines, disagreement with their recommendations, and limited time and resources.20 Although several studies have analysed these issues in Europe, these have been small,19,21 considered only a single risk factor,21,22 or provided data for only a single country.23 Currently, therefore, there is no clear overall picture of the barriers to effective control of CVD risk factors in Europe. In addition, little is known about the way physicians perceive institutional support for implementing primary CVD prevention.
A better understanding of physicians’ attitudes and behaviours will be key for designing and evaluating interventions to overcome barriers preventing thorough implementation of guidelines for CVD risk factor reduction. Therefore, the European Study on Cardiovascular Risk Prevention and Management in Usual Daily Practice (EURIKA) was set up with the aim of assessing the status of primary CVD prevention in clinical settings across Europe. This report presents data from EURIKA on the overall use and awareness of clinical guidelines for CVD risk factor reduction and risk assessment tools, as well as opinions about their usefulness. Opinions about local healthcare frameworks were also assessed.
Methods
Study design and participating countries
The design and rationale of the EURIKA study (ClinicalTrials.gov number: NCT00882336) has been reported in detail elsewhere.24 Briefly, EURIKA was conducted in 12 European countries (Austria, Belgium, France, Germany, Greece, Norway, Russia, Spain, Sweden, Switzerland, Turkey, and the UK) selected to represent the spectrum of healthcare services across the continent. Data collection was started in May 2009 and completed in January 2010. The study was approved by the appropriate healthcare authorities and ethics committees in each participating country.
Selection of physicians
OneKey database served as the sampling frame for the current study. This is a database containing demographic and professional information related to primary care physicians and specialists, doctors in public and private hospitals and clinics, other key professionals in hospital and public health organizations, retail and hospital pharmacists, dentists, veterinarians, and paramedical professionals from 60 countries (http://crm.cegedim.com/solutions/data/Pages/default.aspx) and is the property of Cegedim Dendrite. Data are collected through different information channels, notably via directories of health centres, official websites, registries, and addresses of health administrations and professional organizations in the public and pharmaceutical sectors. A total of 399,298 physicians are included in the OneKey database for the 12 countries considered (9848 in Austria, 12,588 in Belgium, 69,173 in France, 74,963 in Germany, 11,699 in Greece, 6181 in Norway, 54,592 in Russia, 59,266 in Spain, 8740 in Sweden, 8093 in Switzerland, 39,825 in Turkey, and 44,330 in UK). Of the total, 226,290 were men (56.7%) and 173,008 women (43.3%).
The database was used to select a random sample of physicians stratified by age, sex, and speciality. Specialities chosen for analysis were primary care, cardiology, internal medicine, and endocrinology. The number of physicians selected for each sex and age strata were proportional to their distribution in the OneKey database. A preliminary survey was conducted among practicing physicians in each country to determine the characteristics of the local healthcare system and the participation of each type of medical specialist in CVD prevention. Based on the results, different proportions of physicians in each speciality were chosen in each country. Physicians were selected from the OneKey database to match the expected distribution of their country and contacted by telephone; between 231 (Turkey) and 2037 (Switzerland) telephone calls were made in each country. In each country, recruitment was stopped as soon as approximately 60 physicians had agreed to participate (range from 55 in France to 77 in Belgium). The proportion of contacted physicians who agreed to participate was 7.4% in Austria, 13.8% in Belgium, 10.8% in France, 8.0% in Germany, 14.5% in Greece, 8.5% in Norway, 6.3% in Spain, 3.1% in Sweden, 3.4% in Switzerland, 22.8% in Turkey, and 6.6% in the UK.
Data collection
Data were collected prospectively by means of a questionnaire, delivered electronically or by post. Information captured included academic training, work setting, and other demographic characteristics, as well as whether or not global cardiovascular risk assessment tools and clinical guidelines were usually used, standard clinical procedures, and barriers to the use of risk assessment and clinical guidelines. The questionnaire comprised 22 items, most of which prompted a choice from a specified list of responses, but allowed open answers in an ‘other’ category, where appropriate. Questionnaires were translated into the local language of each country.
Statistical analyses
Statistical analyses were performed on the locked data files using appropriate software (SAS version 9.1; SAS Institute Inc, Cary, NC, USA) and were expressed as number (n), percentage (%), 95% confidence interval (CI), mean, and standard deviation.
Results
The physicians’ demographic information is presented in Table 1. Of the 806 participating physicians, 63.4% were men and the mean age was 43.7 years. The majority of physicians worked in primary care (63.8%), while 18.2% were internal medicine specialists, 11.7% were cardiologists, and 3.0% were endocrinologists and diabetes specialists. The most common working environment was with fewer than five other physicians, and most physicians treated 50–99 patients per week. Almost two-thirds of physicians (65.1%) worked in an urban setting.
Characteristics of participating physicians
| n | percent (95% CI) | |
|---|---|---|
| Total | 806 | |
| Gender | ||
| male | 511 | 63.4 (60.1–66.7) |
| female | 295 | 36.6 (33.3–39.9) |
| Age (years) | 47.3 ± 9.6 | |
| Specialty | ||
| Primary care | 514 | 63.8 (60.4–67.1) |
| Cardiology | 94 | 11.7 (9.4–13.9) |
| Internal medicine | 147 | 18.2 (15.6–20.9) |
| Diabetes or endocrinology | 24 | 3.0 (1.8–4.2) |
| Other | 27 | 3.3 (2.1–4.6) |
| Main work setting | ||
| Urban | 525 | 65.1 (61.8–68.4) |
| Suburban | 121 | 15.0 (12.5–17.5) |
| Rural | 159 | 19.7 (17.0–22.5) |
| Did not specify | 1 | 0.1 (0.0–0.4) |
| Number of physicians in working environment | ||
| <5 | 374 | 46.4 (43.0–49.9) |
| 5–9 | 142 | 17.6 (15.0–20.3) |
| 10–19 | 127 | 15.8 (13.2–18.3) |
| >19 | 162 | 20.1 (17.3–22.9) |
| Did not specify | 1 | 0.1 (0.0–0.4) |
| Number of patients treated per week | ||
| <50 | 125 | 15.5 (13.0–18.0) |
| 50–99 | 278 | 34.5 (31.2–37.8) |
| 100–199 | 256 | 31.8 (28.5–35.0) |
| >199 | 147 | 18.2 (15.6–20.9) |
| n | percent (95% CI) | |
|---|---|---|
| Total | 806 | |
| Gender | ||
| male | 511 | 63.4 (60.1–66.7) |
| female | 295 | 36.6 (33.3–39.9) |
| Age (years) | 47.3 ± 9.6 | |
| Specialty | ||
| Primary care | 514 | 63.8 (60.4–67.1) |
| Cardiology | 94 | 11.7 (9.4–13.9) |
| Internal medicine | 147 | 18.2 (15.6–20.9) |
| Diabetes or endocrinology | 24 | 3.0 (1.8–4.2) |
| Other | 27 | 3.3 (2.1–4.6) |
| Main work setting | ||
| Urban | 525 | 65.1 (61.8–68.4) |
| Suburban | 121 | 15.0 (12.5–17.5) |
| Rural | 159 | 19.7 (17.0–22.5) |
| Did not specify | 1 | 0.1 (0.0–0.4) |
| Number of physicians in working environment | ||
| <5 | 374 | 46.4 (43.0–49.9) |
| 5–9 | 142 | 17.6 (15.0–20.3) |
| 10–19 | 127 | 15.8 (13.2–18.3) |
| >19 | 162 | 20.1 (17.3–22.9) |
| Did not specify | 1 | 0.1 (0.0–0.4) |
| Number of patients treated per week | ||
| <50 | 125 | 15.5 (13.0–18.0) |
| 50–99 | 278 | 34.5 (31.2–37.8) |
| 100–199 | 256 | 31.8 (28.5–35.0) |
| >199 | 147 | 18.2 (15.6–20.9) |
Values are percent 95% CI or mean ± SD.
Characteristics of participating physicians
| n | percent (95% CI) | |
|---|---|---|
| Total | 806 | |
| Gender | ||
| male | 511 | 63.4 (60.1–66.7) |
| female | 295 | 36.6 (33.3–39.9) |
| Age (years) | 47.3 ± 9.6 | |
| Specialty | ||
| Primary care | 514 | 63.8 (60.4–67.1) |
| Cardiology | 94 | 11.7 (9.4–13.9) |
| Internal medicine | 147 | 18.2 (15.6–20.9) |
| Diabetes or endocrinology | 24 | 3.0 (1.8–4.2) |
| Other | 27 | 3.3 (2.1–4.6) |
| Main work setting | ||
| Urban | 525 | 65.1 (61.8–68.4) |
| Suburban | 121 | 15.0 (12.5–17.5) |
| Rural | 159 | 19.7 (17.0–22.5) |
| Did not specify | 1 | 0.1 (0.0–0.4) |
| Number of physicians in working environment | ||
| <5 | 374 | 46.4 (43.0–49.9) |
| 5–9 | 142 | 17.6 (15.0–20.3) |
| 10–19 | 127 | 15.8 (13.2–18.3) |
| >19 | 162 | 20.1 (17.3–22.9) |
| Did not specify | 1 | 0.1 (0.0–0.4) |
| Number of patients treated per week | ||
| <50 | 125 | 15.5 (13.0–18.0) |
| 50–99 | 278 | 34.5 (31.2–37.8) |
| 100–199 | 256 | 31.8 (28.5–35.0) |
| >199 | 147 | 18.2 (15.6–20.9) |
| n | percent (95% CI) | |
|---|---|---|
| Total | 806 | |
| Gender | ||
| male | 511 | 63.4 (60.1–66.7) |
| female | 295 | 36.6 (33.3–39.9) |
| Age (years) | 47.3 ± 9.6 | |
| Specialty | ||
| Primary care | 514 | 63.8 (60.4–67.1) |
| Cardiology | 94 | 11.7 (9.4–13.9) |
| Internal medicine | 147 | 18.2 (15.6–20.9) |
| Diabetes or endocrinology | 24 | 3.0 (1.8–4.2) |
| Other | 27 | 3.3 (2.1–4.6) |
| Main work setting | ||
| Urban | 525 | 65.1 (61.8–68.4) |
| Suburban | 121 | 15.0 (12.5–17.5) |
| Rural | 159 | 19.7 (17.0–22.5) |
| Did not specify | 1 | 0.1 (0.0–0.4) |
| Number of physicians in working environment | ||
| <5 | 374 | 46.4 (43.0–49.9) |
| 5–9 | 142 | 17.6 (15.0–20.3) |
| 10–19 | 127 | 15.8 (13.2–18.3) |
| >19 | 162 | 20.1 (17.3–22.9) |
| Did not specify | 1 | 0.1 (0.0–0.4) |
| Number of patients treated per week | ||
| <50 | 125 | 15.5 (13.0–18.0) |
| 50–99 | 278 | 34.5 (31.2–37.8) |
| 100–199 | 256 | 31.8 (28.5–35.0) |
| >199 | 147 | 18.2 (15.6–20.9) |
Values are percent 95% CI or mean ± SD.
In total, 687 physicians (85.2%) reported that they usually followed at least one set of clinical guidelines for primary CVD prevention (Table 2). More than half (55.1%) of the physicians reported following the European Society of Cardiology (ESC) Guidelines on CVD Prevention in Clinical Practice, almost one-third (29.3%) used the European Society of Cardiology/European Society of Hypertension (ESC/ESH) Guidelines for the Management of Arterial Hypertension and 16.6% followed local guidelines (Figure 1). The US-based third Adult Treatment Panel (ATP III) guidelines were used by 8.7% of physicians, and the seventh US Joint National Committee (JNC7) guidelines by 8.4%.
Proportion of physicians using clinical guidelines for CVD prevention. CVD, cardiovascular disease; ESC, European Society of Cardiology Guidelines on CVD Prevention in Clinical Practice; ESC/ESH, European Society of Cardiology/European Society of Hypertension Guidelines for the Management of Arterial Hypertension; ATP III, Third Adult Treatment Panel for Treatment of High Blood Cholesterol in Adults; JNC7, Joint National Committee for Detection, Evaluation and Treatment of High Blood Pressure; Local, local guidelines. More than one answer was permitted per physician.
Reasons for not using clinical guidelines among non users
| n | Percent (95%CI) | |
|---|---|---|
| Do you use clinical cardiovascular guidelines for your patients? | ||
| Yes | 687 | 85.2 (82.8–87.7) |
| No | 102 | 12.7 (10.4–15.0) |
| Did not specify | 17 | 2.1 (1.1–3.1) |
| If no, why not?* | 102 | |
| There are too many guidelines | 48 | 47.1 (37.2–56.9) |
| Time constraints | 34 | 33.3 (24.0–42.6) |
| Do not know them | 28 | 27.5 (18.6–36.3) |
| Guidelines are not realistic or not adapted to everyday practice | 24 | 23.5 (15.2–31.9) |
| Poor acceptance by the patient | 14 | 13.7 (6.9–20.5) |
| Guidelines are confusing | 11 | 10.8 (4.7–16.9) |
| Do not agree with reccomendations | 5 | 4.9 (0.6–9.2) |
| Did not specify | 1 | 1.0 (0.0–2.9) |
| n | Percent (95%CI) | |
|---|---|---|
| Do you use clinical cardiovascular guidelines for your patients? | ||
| Yes | 687 | 85.2 (82.8–87.7) |
| No | 102 | 12.7 (10.4–15.0) |
| Did not specify | 17 | 2.1 (1.1–3.1) |
| If no, why not?* | 102 | |
| There are too many guidelines | 48 | 47.1 (37.2–56.9) |
| Time constraints | 34 | 33.3 (24.0–42.6) |
| Do not know them | 28 | 27.5 (18.6–36.3) |
| Guidelines are not realistic or not adapted to everyday practice | 24 | 23.5 (15.2–31.9) |
| Poor acceptance by the patient | 14 | 13.7 (6.9–20.5) |
| Guidelines are confusing | 11 | 10.8 (4.7–16.9) |
| Do not agree with reccomendations | 5 | 4.9 (0.6–9.2) |
| Did not specify | 1 | 1.0 (0.0–2.9) |
n = 102 physicians answered to this question. More than one answer is permitted.
Reasons for not using clinical guidelines among non users
| n | Percent (95%CI) | |
|---|---|---|
| Do you use clinical cardiovascular guidelines for your patients? | ||
| Yes | 687 | 85.2 (82.8–87.7) |
| No | 102 | 12.7 (10.4–15.0) |
| Did not specify | 17 | 2.1 (1.1–3.1) |
| If no, why not?* | 102 | |
| There are too many guidelines | 48 | 47.1 (37.2–56.9) |
| Time constraints | 34 | 33.3 (24.0–42.6) |
| Do not know them | 28 | 27.5 (18.6–36.3) |
| Guidelines are not realistic or not adapted to everyday practice | 24 | 23.5 (15.2–31.9) |
| Poor acceptance by the patient | 14 | 13.7 (6.9–20.5) |
| Guidelines are confusing | 11 | 10.8 (4.7–16.9) |
| Do not agree with reccomendations | 5 | 4.9 (0.6–9.2) |
| Did not specify | 1 | 1.0 (0.0–2.9) |
| n | Percent (95%CI) | |
|---|---|---|
| Do you use clinical cardiovascular guidelines for your patients? | ||
| Yes | 687 | 85.2 (82.8–87.7) |
| No | 102 | 12.7 (10.4–15.0) |
| Did not specify | 17 | 2.1 (1.1–3.1) |
| If no, why not?* | 102 | |
| There are too many guidelines | 48 | 47.1 (37.2–56.9) |
| Time constraints | 34 | 33.3 (24.0–42.6) |
| Do not know them | 28 | 27.5 (18.6–36.3) |
| Guidelines are not realistic or not adapted to everyday practice | 24 | 23.5 (15.2–31.9) |
| Poor acceptance by the patient | 14 | 13.7 (6.9–20.5) |
| Guidelines are confusing | 11 | 10.8 (4.7–16.9) |
| Do not agree with reccomendations | 5 | 4.9 (0.6–9.2) |
| Did not specify | 1 | 1.0 (0.0–2.9) |
n = 102 physicians answered to this question. More than one answer is permitted.
Overall, 102 physicians (12.7%) reported that they did not usually follow clinical guidelines for CVD risk factor management (Table 2). When asked for the reason, 47.1% replied that there were too many guidelines, while 33.3% claimed that time constraints prevented their use. In total, 27.5% reported that they did not know the guidelines, while 23.5% answered that guidelines were not realistic. Poor acceptance by patients and a belief that guidelines are confusing were also commonly stated reasons. Fewer than 5% of physicians did not agree with guideline recommendations.
When asked whether they usually used global risk assessment tools, 552 (68.5%) reported that they did, 69.4% used charts and 32.8% used computer software tools (Table 3). Risk assessment was commonly used to determine lipid-lowering treatment, lifestyle advice, antihypertensive treatment, and antiplatelet treatment.
Use of global CVD risk factor assessment tools
| n | percent (95% CI) | |
|---|---|---|
| Do you usually calculate the total cardiovascular risk in the middle age patients? | ||
| Yes | 552 | 68.5 (65.3–71.7) |
| No | 244 | 30.3 (27.1–35.5) |
| Did not specify | 10 | 1.2 (0.5–2.0) |
| If yes, what is your preferred method?* | ||
| Charts | 383 | 69.4 (65.5–73.2) |
| Software | 181 | 32.8 (28.9–36.7) |
| Others | 36 | 6.5 (4.5–8.6) |
| Did not specify | 4 | 0.7 (0.0–1.4) |
| If yes, what do you use the risk calculation for?* | ||
| To decide on antihypertensive treatment | 390 | 70.7 (66.8–74.5) |
| To decide on lipid lowering treatment | 489 | 88.6 (85.9–91.2) |
| To decide on anti platelet treatment | 226 | 40.9 (36.8–45.1) |
| To decide on lifestyle advice | 393 | 71.2 (67.4–75.0) |
| Other | 20 | 3.6 (2.1–5.2) |
| Did not specify | 3 | 0.5 (0.0–1.2) |
| If no, why not?** | ||
| Time constraint | 146 | 59.8 (53.6–66.0) |
| Risk assessment is of little usefulness | 53 | 21.7 (16.5–26.9) |
| Don't know how to use | 48 | 19.7 (14.6–24.7) |
| Don't know how to proceed after risk assessment | 10 | 4.1 (1.6–6.6) |
| Other | 31 | 12.7 (8.5–16.9) |
| Did not specify | 4 | 1.6 (0.0–3.2) |
| n | percent (95% CI) | |
|---|---|---|
| Do you usually calculate the total cardiovascular risk in the middle age patients? | ||
| Yes | 552 | 68.5 (65.3–71.7) |
| No | 244 | 30.3 (27.1–35.5) |
| Did not specify | 10 | 1.2 (0.5–2.0) |
| If yes, what is your preferred method?* | ||
| Charts | 383 | 69.4 (65.5–73.2) |
| Software | 181 | 32.8 (28.9–36.7) |
| Others | 36 | 6.5 (4.5–8.6) |
| Did not specify | 4 | 0.7 (0.0–1.4) |
| If yes, what do you use the risk calculation for?* | ||
| To decide on antihypertensive treatment | 390 | 70.7 (66.8–74.5) |
| To decide on lipid lowering treatment | 489 | 88.6 (85.9–91.2) |
| To decide on anti platelet treatment | 226 | 40.9 (36.8–45.1) |
| To decide on lifestyle advice | 393 | 71.2 (67.4–75.0) |
| Other | 20 | 3.6 (2.1–5.2) |
| Did not specify | 3 | 0.5 (0.0–1.2) |
| If no, why not?** | ||
| Time constraint | 146 | 59.8 (53.6–66.0) |
| Risk assessment is of little usefulness | 53 | 21.7 (16.5–26.9) |
| Don't know how to use | 48 | 19.7 (14.6–24.7) |
| Don't know how to proceed after risk assessment | 10 | 4.1 (1.6–6.6) |
| Other | 31 | 12.7 (8.5–16.9) |
| Did not specify | 4 | 1.6 (0.0–3.2) |
n = 552 and **n = 244 physicians answered to this question.
& ** More than one answer is permitted.
Use of global CVD risk factor assessment tools
| n | percent (95% CI) | |
|---|---|---|
| Do you usually calculate the total cardiovascular risk in the middle age patients? | ||
| Yes | 552 | 68.5 (65.3–71.7) |
| No | 244 | 30.3 (27.1–35.5) |
| Did not specify | 10 | 1.2 (0.5–2.0) |
| If yes, what is your preferred method?* | ||
| Charts | 383 | 69.4 (65.5–73.2) |
| Software | 181 | 32.8 (28.9–36.7) |
| Others | 36 | 6.5 (4.5–8.6) |
| Did not specify | 4 | 0.7 (0.0–1.4) |
| If yes, what do you use the risk calculation for?* | ||
| To decide on antihypertensive treatment | 390 | 70.7 (66.8–74.5) |
| To decide on lipid lowering treatment | 489 | 88.6 (85.9–91.2) |
| To decide on anti platelet treatment | 226 | 40.9 (36.8–45.1) |
| To decide on lifestyle advice | 393 | 71.2 (67.4–75.0) |
| Other | 20 | 3.6 (2.1–5.2) |
| Did not specify | 3 | 0.5 (0.0–1.2) |
| If no, why not?** | ||
| Time constraint | 146 | 59.8 (53.6–66.0) |
| Risk assessment is of little usefulness | 53 | 21.7 (16.5–26.9) |
| Don't know how to use | 48 | 19.7 (14.6–24.7) |
| Don't know how to proceed after risk assessment | 10 | 4.1 (1.6–6.6) |
| Other | 31 | 12.7 (8.5–16.9) |
| Did not specify | 4 | 1.6 (0.0–3.2) |
| n | percent (95% CI) | |
|---|---|---|
| Do you usually calculate the total cardiovascular risk in the middle age patients? | ||
| Yes | 552 | 68.5 (65.3–71.7) |
| No | 244 | 30.3 (27.1–35.5) |
| Did not specify | 10 | 1.2 (0.5–2.0) |
| If yes, what is your preferred method?* | ||
| Charts | 383 | 69.4 (65.5–73.2) |
| Software | 181 | 32.8 (28.9–36.7) |
| Others | 36 | 6.5 (4.5–8.6) |
| Did not specify | 4 | 0.7 (0.0–1.4) |
| If yes, what do you use the risk calculation for?* | ||
| To decide on antihypertensive treatment | 390 | 70.7 (66.8–74.5) |
| To decide on lipid lowering treatment | 489 | 88.6 (85.9–91.2) |
| To decide on anti platelet treatment | 226 | 40.9 (36.8–45.1) |
| To decide on lifestyle advice | 393 | 71.2 (67.4–75.0) |
| Other | 20 | 3.6 (2.1–5.2) |
| Did not specify | 3 | 0.5 (0.0–1.2) |
| If no, why not?** | ||
| Time constraint | 146 | 59.8 (53.6–66.0) |
| Risk assessment is of little usefulness | 53 | 21.7 (16.5–26.9) |
| Don't know how to use | 48 | 19.7 (14.6–24.7) |
| Don't know how to proceed after risk assessment | 10 | 4.1 (1.6–6.6) |
| Other | 31 | 12.7 (8.5–16.9) |
| Did not specify | 4 | 1.6 (0.0–3.2) |
n = 552 and **n = 244 physicians answered to this question.
& ** More than one answer is permitted.
Among EURIKA participating physicians (n = 806), 35.4% reported using ESC Systematic Coronary Risk Evaluation (SCORE) system and 21.1% the risk assessment tables contained in the ESC/ESH Guidelines for the Management of Arterial Hypertension (Figure 2). The risk assessment system based on the Framingham study was used by 11.4% of physicians, while 11.9% used locally calibrated Framingham-study based equations.
Proportion of physicians using global CVD risk assessment tools. CVD, cardiovascular disease; ESC, European Society of Cardiology; ESH, European Society of Hypertension. More than one answer was permitted per physician.
Among those physicians who did not usually use risk assessment tools (n = 244), the most commonly stated reason was time constraints (59.8%), while 21.7% of physicians believed risk assessment tools were of little use, and 19.7% stated that they did not know how to use them (Table 3). Only 4.1% of physicians stated that they did not know how to proceed after risk assessment.
Overall, 571 physicians (70.8%) believed that risk assessment tools have limitations (Table 4). The most commonly stated limitations were that the tools miss important risk factors (89.8%), do not allow risk assessment in elderly patients (66.5%), and assess risk over too long a period (46.8%). One-third of physicians (34.5%) believed that risk assessment tools overestimate total CVD risk. The proportion of physicians who believed that risk assessment tools have limitations was greater among those who used them (76.6%) than among those who did not (63.0%) (data not shown).
Limitation of risk assessment tools
| n | percent (95% CI) | |
|---|---|---|
| Do you believe that global risk assessment tools have limitations? | ||
| Yes | 571 | 70.8 (67.7–74.0) |
| No | 218 | 27 (24.0–30.1) |
| Did not specify | 17 | 2.1 (1.1–3.1) |
| If yes, what are the limitations?* | ||
| Miss other important risk factors | 513 | 89.8 (87.4–92.3) |
| Does not allow calculation in elderly | 380 | 66.5 (62.7–70.4) |
| Assess risk over a too long period | 267 | 46.8 (42.7–50.9) |
| Overestimation of the risk | 197 | 34.5 (30.6–38.4) |
| n | percent (95% CI) | |
|---|---|---|
| Do you believe that global risk assessment tools have limitations? | ||
| Yes | 571 | 70.8 (67.7–74.0) |
| No | 218 | 27 (24.0–30.1) |
| Did not specify | 17 | 2.1 (1.1–3.1) |
| If yes, what are the limitations?* | ||
| Miss other important risk factors | 513 | 89.8 (87.4–92.3) |
| Does not allow calculation in elderly | 380 | 66.5 (62.7–70.4) |
| Assess risk over a too long period | 267 | 46.8 (42.7–50.9) |
| Overestimation of the risk | 197 | 34.5 (30.6–38.4) |
*n = 571 physicians answered to this question. More than one answer is permitted. Missing replies wer considered negative.
Limitation of risk assessment tools
| n | percent (95% CI) | |
|---|---|---|
| Do you believe that global risk assessment tools have limitations? | ||
| Yes | 571 | 70.8 (67.7–74.0) |
| No | 218 | 27 (24.0–30.1) |
| Did not specify | 17 | 2.1 (1.1–3.1) |
| If yes, what are the limitations?* | ||
| Miss other important risk factors | 513 | 89.8 (87.4–92.3) |
| Does not allow calculation in elderly | 380 | 66.5 (62.7–70.4) |
| Assess risk over a too long period | 267 | 46.8 (42.7–50.9) |
| Overestimation of the risk | 197 | 34.5 (30.6–38.4) |
| n | percent (95% CI) | |
|---|---|---|
| Do you believe that global risk assessment tools have limitations? | ||
| Yes | 571 | 70.8 (67.7–74.0) |
| No | 218 | 27 (24.0–30.1) |
| Did not specify | 17 | 2.1 (1.1–3.1) |
| If yes, what are the limitations?* | ||
| Miss other important risk factors | 513 | 89.8 (87.4–92.3) |
| Does not allow calculation in elderly | 380 | 66.5 (62.7–70.4) |
| Assess risk over a too long period | 267 | 46.8 (42.7–50.9) |
| Overestimation of the risk | 197 | 34.5 (30.6–38.4) |
*n = 571 physicians answered to this question. More than one answer is permitted. Missing replies wer considered negative.
Physicians were also asked about the communication strategies that they used with patients with lifestyle-related CVD risk factors (Table 5). The most commonly used communication strategies included ensuring that patients understood the relationship between lifestyle and disease (82.8%), and speaking to patients using language that they could easily understand (75.9%). Most physicians (74.8%) acknowledged that changing lifelong habits can be difficult for patients. The least frequently employed communication strategies were offering courses (23.0%), and involving other healthcare staff such as nurses (31.8%) or dieticians, social workers, and psychologists (39.7%) in the treatment.
Communication strategies usually used for the management of behavioural risk factors
| n | percent (95% CI) | |
|---|---|---|
| What communication strategies do you employ when discussing CVD risk with patients? | ||
| Ensure that the patient understand the relationship between lifestyle and disease | 667 | 82.8 (80.1–85.4) |
| Speak to patient using language they can easily understand | 612 | 75.9 (73.0–78.9) |
| Acknowledge that changing life-long habits can be difficult and that gradual sustained change is often more useful | 603 | 74.8 (71.8–77.8) |
| Spend sufficient time with patient | 595 | 73.8 (70.8–76.9) |
| Develop a 'sympathetic alliance' with the patient | 573 | 71.1 (68.0–74.2) |
| Listen carefully, and recognize the strengths and weakness of the patients' attitude to illness and lifestyle | 554 | 68.7 (65.5–71.9) |
| Accept the patients views of his or her own disease, and allow expression of worries | 516 | 64.0 (60.7–67.3) |
| Involve the patient in identifying barriers to change | 427 | 53.0 (49.5–56.4) |
| Involve family members in treatment | 362 | 44.9 (41.5–48.4) |
| Gain commitment to lifestyle change | 357 | 44.3 (40.9–47.7) |
| Involve other healthcare staff (dieticians, social workers and psychologists) | 320 | 39.7 (36.3–43.1) |
| Involve other healthcare staff (nurses) | 256 | 31.8 (28.5–35.0) |
| Offer specific courses to patient | 185 | 23.0 (20.0–25.9) |
| Did not specify | 11 | 1.4 (0.6–2.2) |
| None of the above | 1 | 0.1 (0.0–0.4) |
| n | percent (95% CI) | |
|---|---|---|
| What communication strategies do you employ when discussing CVD risk with patients? | ||
| Ensure that the patient understand the relationship between lifestyle and disease | 667 | 82.8 (80.1–85.4) |
| Speak to patient using language they can easily understand | 612 | 75.9 (73.0–78.9) |
| Acknowledge that changing life-long habits can be difficult and that gradual sustained change is often more useful | 603 | 74.8 (71.8–77.8) |
| Spend sufficient time with patient | 595 | 73.8 (70.8–76.9) |
| Develop a 'sympathetic alliance' with the patient | 573 | 71.1 (68.0–74.2) |
| Listen carefully, and recognize the strengths and weakness of the patients' attitude to illness and lifestyle | 554 | 68.7 (65.5–71.9) |
| Accept the patients views of his or her own disease, and allow expression of worries | 516 | 64.0 (60.7–67.3) |
| Involve the patient in identifying barriers to change | 427 | 53.0 (49.5–56.4) |
| Involve family members in treatment | 362 | 44.9 (41.5–48.4) |
| Gain commitment to lifestyle change | 357 | 44.3 (40.9–47.7) |
| Involve other healthcare staff (dieticians, social workers and psychologists) | 320 | 39.7 (36.3–43.1) |
| Involve other healthcare staff (nurses) | 256 | 31.8 (28.5–35.0) |
| Offer specific courses to patient | 185 | 23.0 (20.0–25.9) |
| Did not specify | 11 | 1.4 (0.6–2.2) |
| None of the above | 1 | 0.1 (0.0–0.4) |
CVD, Cardiovascular disease. More than one answer is permitted.
Communication strategies usually used for the management of behavioural risk factors
| n | percent (95% CI) | |
|---|---|---|
| What communication strategies do you employ when discussing CVD risk with patients? | ||
| Ensure that the patient understand the relationship between lifestyle and disease | 667 | 82.8 (80.1–85.4) |
| Speak to patient using language they can easily understand | 612 | 75.9 (73.0–78.9) |
| Acknowledge that changing life-long habits can be difficult and that gradual sustained change is often more useful | 603 | 74.8 (71.8–77.8) |
| Spend sufficient time with patient | 595 | 73.8 (70.8–76.9) |
| Develop a 'sympathetic alliance' with the patient | 573 | 71.1 (68.0–74.2) |
| Listen carefully, and recognize the strengths and weakness of the patients' attitude to illness and lifestyle | 554 | 68.7 (65.5–71.9) |
| Accept the patients views of his or her own disease, and allow expression of worries | 516 | 64.0 (60.7–67.3) |
| Involve the patient in identifying barriers to change | 427 | 53.0 (49.5–56.4) |
| Involve family members in treatment | 362 | 44.9 (41.5–48.4) |
| Gain commitment to lifestyle change | 357 | 44.3 (40.9–47.7) |
| Involve other healthcare staff (dieticians, social workers and psychologists) | 320 | 39.7 (36.3–43.1) |
| Involve other healthcare staff (nurses) | 256 | 31.8 (28.5–35.0) |
| Offer specific courses to patient | 185 | 23.0 (20.0–25.9) |
| Did not specify | 11 | 1.4 (0.6–2.2) |
| None of the above | 1 | 0.1 (0.0–0.4) |
| n | percent (95% CI) | |
|---|---|---|
| What communication strategies do you employ when discussing CVD risk with patients? | ||
| Ensure that the patient understand the relationship between lifestyle and disease | 667 | 82.8 (80.1–85.4) |
| Speak to patient using language they can easily understand | 612 | 75.9 (73.0–78.9) |
| Acknowledge that changing life-long habits can be difficult and that gradual sustained change is often more useful | 603 | 74.8 (71.8–77.8) |
| Spend sufficient time with patient | 595 | 73.8 (70.8–76.9) |
| Develop a 'sympathetic alliance' with the patient | 573 | 71.1 (68.0–74.2) |
| Listen carefully, and recognize the strengths and weakness of the patients' attitude to illness and lifestyle | 554 | 68.7 (65.5–71.9) |
| Accept the patients views of his or her own disease, and allow expression of worries | 516 | 64.0 (60.7–67.3) |
| Involve the patient in identifying barriers to change | 427 | 53.0 (49.5–56.4) |
| Involve family members in treatment | 362 | 44.9 (41.5–48.4) |
| Gain commitment to lifestyle change | 357 | 44.3 (40.9–47.7) |
| Involve other healthcare staff (dieticians, social workers and psychologists) | 320 | 39.7 (36.3–43.1) |
| Involve other healthcare staff (nurses) | 256 | 31.8 (28.5–35.0) |
| Offer specific courses to patient | 185 | 23.0 (20.0–25.9) |
| Did not specify | 11 | 1.4 (0.6–2.2) |
| None of the above | 1 | 0.1 (0.0–0.4) |
CVD, Cardiovascular disease. More than one answer is permitted.
Fewer than half of all physicians (46.4%) stated that their local healthcare framework was sufficient for the primary prevention of CVD, while 67.2% stated that it was sufficient for the secondary prevention of CVD (Table 6). Among those physicians who did not believe that the framework for primary CVD prevention was sufficient and those who did not believe that the framework for secondary CVD prevention was sufficient, the most commonly stated reasons were staff shortages, insufficient budget and lack of physician incentives. Approximately 20% of both groups of physicians (22.4% and 19.9%, respectively) also stated that managers showed a lack of interest.
Barriers to CVD prevention
| n | percent (95% CI) | |
|---|---|---|
| Is your local healthcare framework sufficiently structured for: | ||
| The primary prevention of CVD? | ||
| Did not specify | 22 | 2.7 (1.6–3.9) |
| Yes | 374 | 46.4 (43.0–49.9) |
| No | 410 | 50.9 (47.4–54.3) |
| If no, what are the reasons?* | 410 | |
| Low budget | 153 | 37.3 (32.6–42.0) |
| Staff shortage/overloaded | 254 | 62.0 (57.2–66.7) |
| Lack of interest by managers | 92 | 22.4 (18.4–26.5) |
| No incentives to physicians | 130 | 31.7 (27.2–36.2) |
| Other | 41 | 10.0 (7.1–12.9) |
| Did not specify | 8 | 2.0 (0.6–3.3) |
| The secondary prevention of CVD? | ||
| Did not specify | 23 | 2.9 (1.7–4.0) |
| Yes | 542 | 67.2 (64.0–70.5) |
| No | 241 | 29.9 (26.7–33.1) |
| If no, what are the reasons?** | 241 | |
| Low budget | 107 | 44.4 (38.1–50.7) |
| Staff shortage/overloaded | 149 | 61.8 (55.6–68.0) |
| Lack of interest by managers | 48 | 19.9 (14.8–25.0) |
| No incentives to physicians | 65 | 27.0 (21.3–32.6) |
| Other | 21 | 8.7 (5.1–12.3) |
| Did not specify | 7 | 2.9 (0.8–5.0) |
| n | percent (95% CI) | |
|---|---|---|
| Is your local healthcare framework sufficiently structured for: | ||
| The primary prevention of CVD? | ||
| Did not specify | 22 | 2.7 (1.6–3.9) |
| Yes | 374 | 46.4 (43.0–49.9) |
| No | 410 | 50.9 (47.4–54.3) |
| If no, what are the reasons?* | 410 | |
| Low budget | 153 | 37.3 (32.6–42.0) |
| Staff shortage/overloaded | 254 | 62.0 (57.2–66.7) |
| Lack of interest by managers | 92 | 22.4 (18.4–26.5) |
| No incentives to physicians | 130 | 31.7 (27.2–36.2) |
| Other | 41 | 10.0 (7.1–12.9) |
| Did not specify | 8 | 2.0 (0.6–3.3) |
| The secondary prevention of CVD? | ||
| Did not specify | 23 | 2.9 (1.7–4.0) |
| Yes | 542 | 67.2 (64.0–70.5) |
| No | 241 | 29.9 (26.7–33.1) |
| If no, what are the reasons?** | 241 | |
| Low budget | 107 | 44.4 (38.1–50.7) |
| Staff shortage/overloaded | 149 | 61.8 (55.6–68.0) |
| Lack of interest by managers | 48 | 19.9 (14.8–25.0) |
| No incentives to physicians | 65 | 27.0 (21.3–32.6) |
| Other | 21 | 8.7 (5.1–12.3) |
| Did not specify | 7 | 2.9 (0.8–5.0) |
CVD, Cardiovascular disease. * & ** more than one answer is permitted.
Barriers to CVD prevention
| n | percent (95% CI) | |
|---|---|---|
| Is your local healthcare framework sufficiently structured for: | ||
| The primary prevention of CVD? | ||
| Did not specify | 22 | 2.7 (1.6–3.9) |
| Yes | 374 | 46.4 (43.0–49.9) |
| No | 410 | 50.9 (47.4–54.3) |
| If no, what are the reasons?* | 410 | |
| Low budget | 153 | 37.3 (32.6–42.0) |
| Staff shortage/overloaded | 254 | 62.0 (57.2–66.7) |
| Lack of interest by managers | 92 | 22.4 (18.4–26.5) |
| No incentives to physicians | 130 | 31.7 (27.2–36.2) |
| Other | 41 | 10.0 (7.1–12.9) |
| Did not specify | 8 | 2.0 (0.6–3.3) |
| The secondary prevention of CVD? | ||
| Did not specify | 23 | 2.9 (1.7–4.0) |
| Yes | 542 | 67.2 (64.0–70.5) |
| No | 241 | 29.9 (26.7–33.1) |
| If no, what are the reasons?** | 241 | |
| Low budget | 107 | 44.4 (38.1–50.7) |
| Staff shortage/overloaded | 149 | 61.8 (55.6–68.0) |
| Lack of interest by managers | 48 | 19.9 (14.8–25.0) |
| No incentives to physicians | 65 | 27.0 (21.3–32.6) |
| Other | 21 | 8.7 (5.1–12.3) |
| Did not specify | 7 | 2.9 (0.8–5.0) |
| n | percent (95% CI) | |
|---|---|---|
| Is your local healthcare framework sufficiently structured for: | ||
| The primary prevention of CVD? | ||
| Did not specify | 22 | 2.7 (1.6–3.9) |
| Yes | 374 | 46.4 (43.0–49.9) |
| No | 410 | 50.9 (47.4–54.3) |
| If no, what are the reasons?* | 410 | |
| Low budget | 153 | 37.3 (32.6–42.0) |
| Staff shortage/overloaded | 254 | 62.0 (57.2–66.7) |
| Lack of interest by managers | 92 | 22.4 (18.4–26.5) |
| No incentives to physicians | 130 | 31.7 (27.2–36.2) |
| Other | 41 | 10.0 (7.1–12.9) |
| Did not specify | 8 | 2.0 (0.6–3.3) |
| The secondary prevention of CVD? | ||
| Did not specify | 23 | 2.9 (1.7–4.0) |
| Yes | 542 | 67.2 (64.0–70.5) |
| No | 241 | 29.9 (26.7–33.1) |
| If no, what are the reasons?** | 241 | |
| Low budget | 107 | 44.4 (38.1–50.7) |
| Staff shortage/overloaded | 149 | 61.8 (55.6–68.0) |
| Lack of interest by managers | 48 | 19.9 (14.8–25.0) |
| No incentives to physicians | 65 | 27.0 (21.3–32.6) |
| Other | 21 | 8.7 (5.1–12.3) |
| Did not specify | 7 | 2.9 (0.8–5.0) |
CVD, Cardiovascular disease. * & ** more than one answer is permitted.
Discussion
Although 85% of physicians in our survey reported using at least one set of clinical guidelines for primary CVD prevention, 30% stated that they did not regularly use global risk assessment tools, despite the fact that their use is widely promoted in clinical guidelines. These findings highlight a gap between the awareness of guidelines and their implementation in routine practice, particularly with regard to use of risk assessment tools.
Reports from previous studies on guideline awareness and use have shown variable results. For example, Mosca et al.17 found that, despite 90–100% awareness of CVD prevention guidelines among physicians, only 50–60% incorporated them into clinical practice and Graham et al.19 reported that physicians’ use of CVD prevention guidelines in different European countries varied between 60% and 97%. In 2008, Doroodchi et al.18 studied CVD prevention guideline adherence for a variety of clinical indications and reported their use as between 36.5% and 76.5%, depending on clinical cases. Our results are comparable with these earlier studies, although differences in design, physician recruitment criteria and questionnaires make comparison across studies difficult. However, despite generally high awareness of CVD prevention guidelines, control of cardiovascular risk factors has been shown to be relatively poor,25 a situation that could be improved eventually by better guideline implementation, as well as better patient compliance and the development of different therapies.
Among the 13% of physicians who reported not using clinical CVD prevention guidelines, the most commonly stated reasons were that too many guidelines exist, time constraints, lack of knowledge, and a belief that guidelines are not realistic. This is largely in agreement with previous findings.18,19,21 Thus, there are arguments for simplification of guidelines to improve knowledge and understanding, increase physicians’ confidence in treatment recommendations, and reduce time requirements for their use. Concerted educational efforts and support for implementation may help alleviate this situation.
More than 30% of physicians do not regularly use risk assessment tools, despite the fact that they are widely promoted in clinical guidelines. Our results are in agreement with previous reports, which have also shown low usage of global risk assessment tools. For instance, Sposito et al.26 reported an average of 48% of physicians using CVD risk assessment tools, a figure that Graham et al.19 also obtained in a European survey. Several other studies have also reported similar risk assessment tool utilization rates.17,18,27
The most common reasons physicians stated for not using risk factor assessment tools were time constraints, not being convinced of their utility, and lack of knowledge. Interestingly, most physicians also stated that risk assessment tools have limitations, including missing important risk factors, overestimating risk, not allowing risk assessment in elderly patients, and assessing risk over too great a period. Thus, in addition to increasing awareness, implementation of risk assessment could be improved by addressing these issues in well-designed trials, allowing integration of an increased set of risk factors that provide clear incremental value, increasing upper age limits and allowing flexibility in the period of time over which risk is assessed.28 However, the challenge will be to incorporate these changes without greatly increasing the complexity, in order not to increase time requirements and difficulty in using these tools in practice.
Control of many of the risk factors for CVD involves lifestyle changes by the patient, which are often difficult to achieve. The ESC Guidelines on Cardiovascular Disease Prevention in Clinical Practice outline a variety of communication strategies aimed at improving patient understanding and compliance.12 It is important to use easily comprehensible language and take sufficient time to ensure that patients appreciate the link between behaviour and disease. Previous studies have demonstrated both a lack of understanding of cardiovascular risk factors by patients and overestimation by physicians of the general public’s understanding thereof.29 Our survey has demonstrated that physicians appreciate the importance of effective communication with patients, but also underlines the fact that time constraints make treatment difficult. It has previously been suggested that practice may be improved by allowing nurses to discuss more thoroughly with patients the importance of lifestyle changes to reduce the risk of CVD.29,30 However, more than half of all physicians included in our study stated that healthcare frameworks were insufficient for providing for the needs of patients in the primary prevention of CVD, largely due to lack of budget and resources. The situation is better for secondary prevention, although still far from satisfactory with 30% of surveyed physicians stating that healthcare provision was insufficient in this setting. Treatment of CVD in secondary care is likely to be seen as a higher priority, and patient motivation and treatment adherence is likely to be higher. In contrast, half of all physicians stated that the local healthcare framework was insufficient for primary prevention, highlighting an area of possible improvement.
A strength of this study is the use of a uniform assessment procedure across a wide variety of European countries. However, a limitation of the study is that physicians may report better practice than they actually use, thus providing a more favourable representation of physicians’ attitudes and practices than exists in reality. Although the OneKey database is the largest available database of practicing physicians in Europe, it may not be statistically representative of all physicians in Europe. Nevertheless, the large number of practitioners included in this study, the coverage of all relevant medical specialities and work settings, the continuous updating of the database and the random selection of study patients suggest that the EURIKA study is informative of the status of primary prevention of CVD in the 12 participating countries. The low participation rate is another limitation of the study, as physicians may be selected and not be fully representative of their country. One possibility is that physicians who are more motivated or more competent in CVD care agreed to participate in the survey; in this case, the results of the EURIKA study would provide more favourable representation of clinical practice. However, some other reasons might have driven physicians’ decisions to participate in the survey, so that the overall balance within these potential biases is unpredictable. Nevertheless, the results of the current study should be interpreted with caution in view of the limited number of physicians included per country.
In conclusion, we have found that most physicians report using CVD guidelines, although use of CVD risk assessment tools is poor. However, time constraints, lack of perceived usefulness, and inadequate knowledge were common reasons stated for not using CVD prevention guidelines or global CVD risk assessment tools. Furthermore, physicians stated that healthcare frameworks are insufficient for providing for the needs of patients in the primary prevention of CVD, largely due to lack of budget and staff resources. Eventually, CVD prevention may be improved by addressing the concerns of physicians highlighted in this study.
Funding
This work was supported by AstraZeneca.
Competing interests
JPJ Halcox and J Dallongeville have received speaker and consulting fees from AstraZeneca. F Turbach received research grant from Astrazeneca, Pfizer, Abbott et Scheringh Plough. PG Steg reports receiving research grants from Servier; speaking or consulting for Astellas, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Daiichi-Sankyo, Endotis, GlaxoSmithKline, Menarini, Medtronic, Merck Sharp & Dohme, Otsuka, Pierre Fabre, Roche, sanofi-aventis, Servier, The Medicines Company; and being a stockholder in Aterovax. EL Massó-González and O Sazova are employees of AstraZeneca. The remaining authors declare that they have no competing interests.
Acknowledgements
The authors would like to thank the physicians and patients who participated in the EURIKA survey. Editorial support was provided by Dr Stephen Sweet from Oxford PharmaGenesis Ltd. EURIKA was run by an independent academic steering committee.
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