Start Submission Become a Reviewer

Reading: Prevalence of Cardioprotective Medication Use in Coronary Heart Disease Patients in South Am...

Download

A- A+
Alt. Display

Review

Prevalence of Cardioprotective Medication Use in Coronary Heart Disease Patients in South America: Systematic review and Meta-Analysis

Authors:

A. Marzà-Florensa ,

Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, NL
X close

E. Drotos,

Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht; Department of Health Promotion, Care and Public Health Research Institute, Maastricht University, Maastricht, NL
X close

P. Gulayin,

Instituto de Efectividad Clínica y Sanitaria, Buenos Aires, AR
X close

D. E. Grobbee,

Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, NL
X close

V. Irazola,

Instituto de Efectividad Clínica y Sanitaria, Buenos Aires, AR
X close

K. Klipstein-Grobusch,

Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, NL
X close

I. Vaartjes

Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, NL
X close

Abstract

Background: Coronary heart disease (CHD) is the most common cause of death globally, and clinical guidelines recommend cardioprotective medications for patients with established CHD. Suboptimal use of these medications has been reported, but information from South America is scarce.

Methods: We conducted a systematic review on prevalence of secondary prevention medication in South America. We pooled prevalence estimates, analysed time-trends and guideline compliance, and identified factors associated with medication use with meta-regression models.

Results: 73 publications were included. Medication prevalence varied by class: beta-blockers 73.4%(95%CI 66.8%–79.1%), ACEI/ARBs 55.8%(95%CI 49.7%–61.8), antiplatelets 84.6%(95%CI 79.6%–88.5%), aspirin 85.1%(95%CI 79.7%–89.3%) and statins 78.9%(95%CI 71.2%–84.9%). The use of beta-blockers, ACEI/ARBs and statins increased since 1993. Ten publications reported low medication use and nine reported adequate use. Medication use was lower in community, public and rehabilitation settings compared to tertiary centres.

Conclusion: Cardioprotective medication use has increased, but could be further improved particularly in community settings.

How to Cite: Marzà-Florensa A, Drotos E, Gulayin P, Grobbee DE, Irazola V, Klipstein-Grobusch K, et al.. Prevalence of Cardioprotective Medication Use in Coronary Heart Disease Patients in South America: Systematic review and Meta-Analysis. Global Heart. 2022;17(1):37. DOI: http://doi.org/10.5334/gh.1124
  Published on 08 Jun 2022
 Accepted on 09 May 2022            Submitted on 08 Oct 2021

Introduction

Coronary heart disease (CHD) is the main cause of death and one of the most important causes of disability worldwide and in South America [1]. Cardioprotective medications, including antiplatelet, anti-hypertensive, lipid-lowering and hypoglycaemic medication, are effective in preventing CHD morbidity and mortality, [2, 3, 4] and their long-term use in patients with established CHD is recommended by international guidelines [2, 5].

Despite guideline recommendations, research shows that the use of these medications in secondary prevention of CHD patients is suboptimal [3, 6, 7]. This gap between guideline recommendations and clinical use has been described in high-income countries [4, 8, 9, 10, 11], but information from middle-and and low income countries, including the South American region [12], is limited. Meta-analyses have been conducted to explore this problem in North America, Europe [9], and China [13], and there is high variability by region [14] in the use of guideline-recommended medications for CHD secondary prevention. To date, an overview and general picture of secondary prevention medication and its determinants in South America is lacking. Therefore, the aim of this systematic review is to summarize evidence on the prevalence of cardioprotective medication use for secondary prevention of CHD in South America. The secondary aims of this work are to summarize the findings on guideline compliance, examine time trends and identify potential factors associated with use of medication in patients with established CHD.

Methods

Search strategy

This review was registered with PROSPERO (registration number CRD42020206657) and conducted in accordance with the PRISMA guidelines [15] (Supplementary File 1). We conducted a systematic search on April 28th, 2021 on the following databases: PubMed, Embase, Cochrane, LILACS and SciELO. The search strategy contained information on the CHD diagnosis of the patients, the country where the study was performed and the most common classes of cardioprotective medications in the outpatient clinic setting. Studies published between 2000 and 2021 in English, Spanish or Portuguese and conducted in South America (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Guyana, Paraguay, Peru, Suriname, Uruguay and Venezuela) reporting the prevalence of cardioprotective medications in CHD patients in outpatient settings were included. Broad terms were included for the diagnosis of CHD: ‘coronary artery disease,’ ‘myocardial infarction,’ ‘ST-elevation myocardial infarction,’ ‘non-ST-elevation myocardial infarction,’ ‘acute coronary syndrome,’ ‘angina pectoris,’ ‘acute coronary syndrome,’ ‘coronary atherosclerosis,’ interventions such as ‘coronary artery bypass graft’ and ‘percutaneous coronary intervention,’ and commonly used acronyms for these terms. For details on the search strategy and PROSPERO registration see Supplementary File 2.

Eligibility criteria

The classes of cardioprotective medications taken into account were anti-platelet drugs, lipid-lowering drugs, antihypertensive agents (beta-blockers, ACE-inhibitors, ARBs, diuretics, and nitrates), oral hypoglycaemics and insulin. Intervention studies (randomized clinical trials and non-randomized interventions) and observational studies (cross-sectional, cohort and case-control studies) were included. Case reports, case series, reviews, as well as publication types other than original articles were excluded.

Study selection

The publications resulting from the search were screened by the above eligibility criteria on their titles and abstracts using the platform Rayyan CQRI [16]. Screening was conducted by two reviewers (ED, AMF). Each reviewer screened half of the articles, and an additional 10% of the articles was screened by the other reviewer to prevent interpersonal bias. The reviewers discussed discrepancies and unclear decisions until consensus was reached. The publications that fulfilled the inclusion and exclusion criteria were screened on their full-text following the same strategy.

Data extraction

Relevant data was extracted from the selected publications. Data extraction was performed using the electronic data capture system REDCap©[17] by two reviewers (ED, AMF). Each reviewer extracted data from the articles that the other reviewer had previously screened to minimize potential bias. Collected data included information on authors, publication year, publication title, name of the study, language, period in which the study was conducted, country, study design; participants characteristics including specific diagnosis like CHD, acute coronary syndrome (ACS), coronary artery bypass graft (CABG) or percutaneous coronary intervention (PCI); percentage of women, age range, mean age, socioeconomic status (including percentage of participants in the highest income and education categories as well as percentage of employment), and cardiovascular risk factors (blood pressure, body mass index, lipids and glucose levels), care setting information (type of hospital or healthcare centre, e.g. primary care, academic hospital, tertiary hospital, rehabilitation, and whether the centre was public or private), and urbanicity.

Outcome data included the prevalence of medication per medication class. In the case of drug intervention studies, we extracted data on medication prevalence at baseline. In publications with an observational design that reported medication prevalence at multiple time-points, we extracted data from the earliest time-point in order to facilitate comparison with intervention studies. If not reported directly, medication prevalence was calculated when possible.

Secondary outcome data included guideline compliance (report of compliance or non-compliance), time trends (starting year of the study) and determinants associated with use of medication in patients with established CHD (outcomes reported in stratified analysis or coefficients reported in regression models).

Quality assessment

A tool for the quality assessment of studies reporting prevalence estimates was adapted from the previous work by Zhao et al. [13], and Li et al. [18] (Supplementary File 3A). For overall risk of bias assessment, we summarized risk of bias as follows: for risk of bias in each domain (study design, study population, participation rate, participants’ characteristics and outcome) 2 points were given for low risk, 1 point for moderate risk and 0 points for high or unclear risk. Publications with a score lower than 6 out of 10 were excluded. The remaining publications were classified as: moderately low risk of bias (6–7 points), low risk of bias (8–9 points) and very low risk of bias (10 points). Reviewers ED and AMF assessed the quality of articles for which they extracted the data, and additionally they assessed the quality of 10% of the articles examined by the other reviewer. Discrepancies were discussed until consensus was achieved.

Data analysis

Data analysis was conducted using R Studio [19]. Data on medication prevalence is expressed in percentages by class of medication. We reported the prevalence of each kind of medication separately. We presented separate categories for those articles reporting general classes of medications instead of specific drugs. In the case of antiplatelet drugs, we additionally showed the estimates of articles reporting the prevalence of aspirin, clopidogrel, and not-specified antiplatelet drugs combined because of the shared indication for these medications, and to be able to explore the use of this medication class in general.

Meta-analysis was performed using a mixed model from the R package ‘metafor’ for each class of medication [20]. The results were expressed as pooled prevalence with 95% confidence intervals (CI) and random effects, and displayed in forest plots by care setting. Heterogeneity was quantified with the I2 test. The same statistical package was used in a sensitivity analysis to analyse potential differences in prevalence between studies conducted in Brazil and in other countries.

In order to explore time trends in medication use, mixed meta-regression models were fitted with the starting year of the study as covariate for each medication class. The reported prevalence of medication and the model prediction were plotted against the year the studies commenced in bubble plots to illustrate time-trends in medication use.

Meta-regression models were performed to discern potential factors contributing to medication use. A mixed meta-regression model was run for each class of medication, including the following covariates: the proportion of women included, time of outcome measurement since the start of the study, diagnosis of the patients included in the study, urban region, and care setting. The full models were reduced and simpler models were compared against the full models and among them with the AIC fitting statistic. Models with the lowest AIC were selected. Results were expressed as odds ratios (OR) and 95% CI.

Results

Study selection

The search strategy resulted in 7388 publications: 2660 in LILACS, 1810 in Embase, 1538 in SciELO, 729 in Cochrane and 651 in PubMed. After removing 1606 duplicates, 5782 publications were screened on their title and abstract. 4405 publications did not fulfil the inclusion criteria and were excluded, resulting in 1377 publications eligible for full-text screening. During a full-text screening, 1218 articles were excluded (Figure 1). Of the remaining 159 publications, 86 did not reach the quality threshold during the quality assessment, and therefore 73 publications were finally included in the review.

Study selection flow-chart. 7388 articles were found in 5 databases. 1377 publications were eligible for full-text screening. 159 publications were eligible for data extraction. 86 publications were excluded because of high risk of bias. 73 publications were included in the syste matic review.
Figure 1 

Study selection flow-chart.

Study characteristics

Table 1 describes the main characteristics of the included studies. All articles included were published between 2000 and 2020, referring to studies conducted between 1993 and 2017. Most studies were conducted in Brazil [3, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58]. Six studies were conducted in Argentina [59, 60, 61, 62, 63, 64], four in Chile [65, 66, 67, 68], four in Colombia [69, 70, 71, 72], three in Uruguay [73, 74, 75] and two were multi-country studies conducted in Argentina, Brazil, Chile and Colombia [12] and Brazil and Suriname [76]. The most common language of the articles was English (58 articles), followed by Spanish (11 articles) and Portuguese (4 articles).

Table 1

Characteristics of the studies included in the review.


PUBLICATION STUDY DURATION COUNTRY STUDY DESIGN N CARE SETTING DIAGNOSIS CATEGORY URBAN SETTING % WOMEN AGE SOCIOECONOMIC STATUS

Castillo y Costa, 2018 NA-2015 Argentina Cohort 210 MI, CABG, PCI Unclear 17.0 59.0 (9); 61.0 (9.0)

Fernandes, 2012 2003–2004 Brazil RCT 45 PCI 38.0 62.7 (9.9), 26–89

Gurfinkel, 2004 2001-NA Argentina RCT 301 ACS Urban 59 (8.7), 59 (7.9)

Ladeia, 2003 1995–1997 Brazil Cross-sectional 104 CHD Urban 32.7 60.9 (8.1) Education: 10.6

Lima-Filho, 2010 2001–2002 Brazil Cohort 70 PCI 22.9 57.6 (13.9), 59.4 (7.6)

Lorenzo, 2014 2008–2010 Brazil Cohort 228 CHD Urban 46.1 63.15 (12.26)

Baptista, 2012 2009–2011 Brazil Cohort 97 Academic or Tertiary Hospital CABG 33.3 63.5 (9.4), 42–81

Bohatch, 2015 2011–2013 Brazil Cohort 230 Academic or Tertiary Hospital CABG 24.3

Brasil, 2013 NA-NA Brazil Cross-sectional 710 Academic or Tertiary Hospital CHD Urban 57.4 (4.1)

Breda, 2008 2008–2005 Brazil RCT 50 Academic or Tertiary Hospital CABG Urban 42.0 62.1 (12)

Chaves, 2004 2001–2002 Brazil RCT 96 Academic or Tertiary Hospital CHD Urban 51.0 65.07 (12.49)

Chaves, 2019 2015–2017 Brazil RCT 115 Academic or Tertiary Hospital CABG, PCI Urban 28.7 63.9 (10. 9), 63 (12.1) Employmnent: 40

Cruz, 2009 2004–2005 Brazil Cross-sectional 103 Academic or Tertiary Hospital CHD 67.9 (12.3)

Dayan, 2018 2006–2014 Uruguay retrospective 282 Academic or Tertiary Hospital CABG 26.6 65.58 (9.5), 61.75 (9.6)

Feguri, 2017 2014–2016 Brazil RCT 574 Academic or Tertiary Hospital CABG Urban 33.0 62.12 (9.63), 60.93 (8.91)

Fernandez, 2011 2006–2007 Colombia RCT 400 Academic or Tertiary Hospital PCI Urban 45.0 58.0 (9.0)

Furuya, 2014 2011–2012 Brazil RCT 60 Academic or Tertiary Hospital PCI Urban 43.0 56.9 (10.8), 34–85 Employment: 35.0

Gomes, 2011 2002–2006 Brazil Cohort 504 Academic or Tertiary Hospital PCI Urban 35.9 63.7 (11.0)

Hueb, 2004 1995–2000 Brazil RCT 611 Academic or Tertiary Hospital CHD Urban 15.0 60.25 (9.26), 58.92 (6.04)

Kimura, 2018 2007–2013 Brazil Cohort 520 Academic or Tertiary Hospital CABG Urban 72.1

Liberato, 2016 2010–2011 Brazil Cross-sectional 190 Academic or Tertiary Hospital ACS Urban 36.1 64.9, 32–93 Employment: 31.0

Nazzal, 2013 2008–2008 Chile Registry 416 Academic or Tertiary Hospital ACS Urban 23.4 Income: 20.0

Neira, 2013 2011–2011 Chile Cross-sectional 202 Academic or Tertiary Hospital CHD Urban 29.7 58.9 (9.8), 60.6 (8.5) Education: 17.4
Employment: 45.0

Nery, 2015 2009–2012 Brazil RCT 61 Academic or Tertiary Hospital ACS Urban 27.9 59.5 (9.4)

Neves, 2012 NA-NA Brazil descriptive, cross-sectional study 20 Academic or Tertiary Hospital CHD 0.0

Noriega, 2008 NA-NA Chile Non-randomized intervention 64 Academic or Tertiary Hospital CABG, PCI 20.3 64.0 (11.0), 63 (12.0)

Oliveira, 2019 2013–2015 Brazil Retrospective cohort 536 Academic or Tertiary Hospital ACS Urban 36.0 65.6 Education: 49.2; Income: 34.0

Pantoni, 2016 NA-NA Brazil Non-randomized intervention 27 Academic or Tertiary Hospital CABG Urban 44.4 60.0 95% CI 51–68), 63.0 (95% CI 55–70), 61.0 (95% CI 53–73)

Pellegrini, 2014 2002–2007 Brazil Cohort 611 Academic or Tertiary Hospital ACS Rural 28.6 61.4 (11.6)

Pesaro, 2012 2006–2009 Brazil RCT 78 Academic or Tertiary Hospital CHD Urban 38.5 64.0 (12.0), 65.0 (12.0), 61.0 (12.0)

Portal, 2003 1998–1999 Brazil RCT 39 Academic or Tertiary Hospital CHD 43.6 62,7 (10.7), 61.6 (11.1)

Ribeiro, 2015 2007–2008 Brazil Cross-sectional 153 Academic or Tertiary Hospital PCI Urban 49.0 61.9 (11.9)

Ribeiro, 2018 2014–2016 Brazil Cohort 169 Academic or Tertiary Hospital Urban 16.0 63.7 (9.6)

Rossi, 2014 2006–2006 Argentina Cohort 125 Academic or Tertiary Hospital ACS Urban 34.4 56.0 (9.0), 60.0 (9.0)

Rueda-Clausen, 2010 2005–2006 Colombia Cross-sectional 34 Academic or Tertiary Hospital CHD Urban 23.5 64.0, 61.0

Saffi, 2013 2008–2010 Brazil RCT 74 Academic or Tertiary Hospital CHD 26.0 60.9(10.6), 63.4 (8.56), 59.9(11.8), 62.7(10.9) Income: 58.0

Santos, 2015 2007–2010 Brazil Cohort 198 Academic or Tertiary Hospital PCI 30.3 55.0 (8.0), 52.0 (7.0), 54.0 (10.0)

Scherr, 2010 1997–2002 Brazil Non-randomized intervention 2337 Academic or Tertiary Hospital CHD Urban 39.2 64.3 (10.7), 64.5 (10.9)

Silva, 2005 1995–1998 Brazil RCT 210 Academic or Tertiary Hospital CHD Urban 32.4 60.2 (10), 28–87

Silveira, 2007 2002–2003 Brazil RCT 24 Academic or Tertiary Hospital CABG 37.5 58.5 (9.4)

Silveira, 2008 1998–2005 Brazil Cohort 310 Academic or Tertiary Hospital CHD Unclear 39.0

Simon, 2019 2014–2015 Brazil RCT 48 Academic or Tertiary Hospital ACS 35.4

Siniawski, 2019 2014–2017 Argentina Cross-sectional 351 Academic or Tertiary Hospital ACS, CABG Urban 26.5 63.3 (12.4), 60.0 (87)

Smidt, 2009 2002–2007 Brazil Registry 611 Academic or Tertiary Hospital ACS 36.6 60.9 (10.3), 31–81

Souza Groia Veloso, 2020 NA-NA Brazil, Suriname Cross-sectional 148 Academic or Tertiary Hospital CHD Unclear 29.7 Median 61.0 (IQR 54–68)

Souza, 2013 2008–2010 Brazil Registry 103 Academic or Tertiary Hospital ACS Urban 16.5 62.6 (9.3), 63.3 (11.3)

Uchoa, 2015 NA-NA Brazil Cohort 67 Academic or Tertiary Hospital CHD, CABG Urban 25.0 61.2 (10.0), 68.6 (9.0)

Vilar, 2015 2009–2010 Brazil Cross-sectional 155 Academic or Tertiary Hospital CHD 18.7 60.0 (9.0)

Villacorta, 2012 2006–2008 Brazil Cohort 209 Academic or Tertiary Hospital PCI Urban 26 Median 62.0 [IQR 17.0]

Abreu-Silva, 2011 2008–2010 Brazil Registry 535 Other PCI 32.0 67.0 (10.4)

Alvarez, 2016 1993–2013 Argentina Cross-sectional 866 Other ACS 24.0 62.7 (11.1)

Berwanger, 2013 NA-NA Brazil Cross-sectional 681 Other ACS

Fernandez, 2009 2003–2006 Colombia Cohort 395 Other CHD 32.7 64.4 (12.9), 66.8 (10.9)

Finimundi, 2007 NA-NA Brazil RCT 40 Other ACS Urban 43.0 60.1 (2.2), 63.21 (2.21)

Gaedke, 2015 NA-NA Brazil Cohort 138 Other ACS Urban 44.4 62.5 (11.1) Education: 54.8
Income: 33.3

Gowdak, 2007 1998–2004 Brazil Cohort 119 Other CHD Urban 57.4 (5.9), 58.3 (8.6)

Mattos, 2012 2010–2011 Brazil Registry 2475 Other ACS 32.2 64 (8.0), 65 (9.0), 66 (8.0)

Mendis, 2005 2002–2003 Brazil Cross-sectional 836 Other CHD Both 56.0 (10.0)

Vazquez, 2011 2008–2009 Uruguay Cohort 154 Other ACS 21.4

Vesga, 2006 NA-NA Colombia Cross-sectional 71 Other CHD Urban 28.2 58.4 (7.9)

Avezum, 2017 2003–2009 Argentina, Brazil, Chile, Colombia Cross-sectional 910 Primary Care/Community CHD Urban and rural 61.3 62.20 (11.60)

Vianna, 2012 2008–2008 Brazil Cross-sectional 295 Primary Care/Community ACS Urban

Birck, 2019 2008–2010 Brazil Cross-sectional 405 Primary Care/Community CHD Urban 36.5 61.6 (9.4) Education: 48.6, Income: 38.3

Stockins, 2011 2005–2006 Chile Cohort 233 Publi Hospital ACS 30.6 68.0

Aguiar, 2010 1999–2007 Brazil Cohort 377 Public Hospital ACS 37.9 62.3 (9.3)

Carvalho, 2007 1992–2000 Brazil Retrospective cohort 381 Rehabilitation 19.4

Gambogi, 2009 2004–2006 Uruguay Cohort 900 Rehabilitation Both 25.3 57.9 (9.9), 61.3 (7.7) Education: 9.5
Employment: 44.6

Garlet, 2017 2015–2016 Brazil Cross-sectional 102 Rehabilitation CHD 31.4 61.7 (10.0), 64.5 (9.0)

Lelys, 2019 2015–2017 Brazil Cross-sectional 115 Rehabilitation CHD 28.7 59.9(8.6); 57.2 (9.0) Employment: 40.0

Pantoni, 2014 2006–2008 Brazil Non-randomized intervention 28 Rehabilitation CABG Urban 32.1 56.0

Fuchs, 2009 2005–2006 Brazil Cross-sectional 39 Rehbilitation CHD Urban 10.3 63.7(95% CI 56.6–73.9)

Castro, 2018 2018–NA Brazil Cohort 525 Secondary Hospital ACS Urban 39.8 61.6 (11.9)

Trivi, 2018 2010–2011 Argentina Cohort 438 Secondary Hospital ACS 24.2 59.2 (7.9)

Age is expressed in percentage (standard deviation) unless indicated otherwise; multiple values are given if age was reported by subgroups in the publication. Socioeconomic status indicates the percentage of participants included in the highest category of education or income, or percentage of employed participants. Abbreviations: RCT (randomized controlled trial), ACS (acute coronary syndrome), CABG (coronary artery bypass graft), CHD (coronary heart disease), PCI (percutaneous coronary intervention).

In terms of study design, most publications reported on cohort studies (23 articles), cross-sectional studies (20 publications), and baseline data of randomized clinical trials (17 articles). The number of participants included in each study ranged from 20 to 2475, with a mean of 328 (SD 424). Most studies were conducted in urban areas (39 studies). Regarding the clinical setting, the majority of studies were conducted in academic or tertiary hospitals (42 articles), six in rehabilitation centres, three in primary care or community settings, two in secondary level hospitals, two in public hospitals, and 12 in other settings.

Participants’ characteristics

The most common diagnosis of the patients included was coronary heart disease (26 articles), followed by ACS (21 articles), PCI (9 articles), CABG (9 articles) and some articles included patients with more than one diagnosis [5]. Most articles included a majority of male participants. The mean percentage of female participants was 32.0% (SD 11.4%).

12 articles provided information on the socioeconomic status (SES) of participants. Educational attainment was reported by seven publications and the proportion of participants with highest educational attainment ranged from of 9.5% to 49.2%. The percentage of employed participants was reported by five articles and ranged from 33.5% to 45.0%; and the proportion of participants in the highest income category (reported in six articles) varied from 20.0% to 58.0%.

Regarding the risk factors of the study populations, 54 articles reported the prevalence of hypertension (range 45.0–96.0%) and 42 articles provided prevalence values for dyslipidaemia (36.0–96.8%). The prevalence of diabetes was reported in 56 articles (range 7.7% to 100%). The prevalence of overweight was reported in 11 of the included studies (range 28.2% to 93.5%), 5 articles reported the prevalence of obesity (range 15.0%–33.7%); and 16 articles included mean or median BMI values, ranging from 26.1 to 29.0 kg/m2.

Quality assessment

The risk of bias varied by domain of the quality assessment tool: study population was the field in which more articles had a high risk of bias (13.7%), whereas most articles had low risk of bias in the fields of study design (79.5%) and participation rate (72.6%) (Figure 2). The results of the quality assessment of all included publications are displayed in Supplementary Figure 1, and of included and excluded publications in Supplementary File 3B. Supplementary file 3C details the reasons for exclusion of publications with a risk of bias score lower than six.

Risk of bias results. For the fields study population, participant’s characteristics and outcome, most articles had moderate risk of bias. For the fields study design, and participation rate, most articles had low risk of bias.
Figure 2 

Risk of bias results.

Prevalence of medication

The prevalence of medication for each study as well as the pooled prevalence estimate per medication is displayed in Figures 3, 4, 5 and summarized in Table 2. The prevalence of beta-blockers was reported in 53 studies, with a pooled estimate of 73.4% (95%CI 66.8% – 79.1%) (Figure 3). 44 articles reported the prevalence of ACEI/ARB use, with a pooled estimate of 55.8% (95%CI 49.7% – 61.8%) (Figure 3). The overall prevalence of antiplatelet drugs (including aspirin, clopidogrel, and articles that didn’t specify the antiplatelet drug) was retrieved from 51 studies, and the pooled prevalence estimate was 84.6% (95%CI 79.6% – 88.5%) (Figure 4). The prevalence of aspirin specifically was retrieved from 44 studies and their pooled estimate was 85.1% (95%CI 79.7% – 89.3%) (Figure 4). The prevalence of statins was reported in 50 articles and the estimated pooled prevalence was 78.9% (95%CI 71.2% – 84.9%) (Figure 5). Total heterogeneity in the meta-analysis models high, ranging from 97.8% (ACEI/ARBs model) to 99.0% (antiplatelet model). No significant differences were observed in the prevalence of any medication classes between Brazil and other countries.

The prevalence of beta-blockers was reported by 53 articles. The pooled prevalence of beta-blockers was 73.4% (95% CI 66.8% – 79.15%). The prevalence of ACEI/ARBs was reported by 44 studies and the pooled estimate was 55.8% (95% CI 49.7% – 61.8%).
Figure 3 

Pooled prevalence of anti-hypertensive medication use.

The prevalence of aspirin was reported by 44 articles. The pooled prevalence of aspirin was 85.1% (95% CI 79.7% – 89.3%). The prevalence of aspirin, clopidogrel or other antiplatelet drugs was reported by 51 studies and the pooled estimate was 84.6% (95% CI 79.6% – 88.5%).
Figure 4 

Pooled prevalence of antiplatelet medication use.

Table 2

Summary of the meta-analysis results.

Pooled prevalence results are expressed in percentage and 95% confidence interval.


VARIABLE NUMBER OF STUDIES POOLED PREVALENCE

Beta-blockers 53 73.4 (66.8–79.1)

ACE inhibitors 44 55.8 (49.7–61.8)

Aspirin 44 85.1 (79.7–89.3)

Aspirin, clopidogrel or antiplatelet drugs 51 84.6 (79.6–88.5)

Statins 50 78.9 (71.2–84.9)

Insulin 9 11.6 (7.0–18.8)

Antihypertensives (without specification) 8 46.5 (33.7–59.8)

Diuretics 8 30.1 (24.3–36.6)

Calcium chanel blockers 6 34.0 (19.4–52.5)

Nitrates 6 36.7 (24.1–51.5)

Antiplatelet (without specification) 14 75.1 (55.5–87.9)

Clopidogrel 13 50.0 (22.9–78.1)

Dual antiplatelet therapy 3 80.0 (55.3–92.8)

Lipid-lowering drugs (without specification) 2 34.4 (9,1–73.4)

High-intensity statins 2 24.1 (6.4-59.8)

Fibrates 2 73.1 (69.5–76.5)

The prevalence of statins was reported by 50 articles. The pooled prevalence of statins was 78.9% (95% CI 71.2% – 84.9%).
Figure 5 

Pooled prevalence of statins.

The pooled prevalence estimates of medications and medication classes reported by fewer articles is displayed in supplementary Figures 3, 4, 5 and summarized in Table 2. This includes antihypertensive drugs (without specification), diuretics, nitrates, antiplatelet drugs (without specification), calcium channel blockers, clopidogrel, dual antiplatelet therapy, lipid-lowering drugs (without specification), high-intensity statins, and fibrates.

Time trends

The prevalence of beta-blockers, ACEI/ARBs and statins use significantly increased with time. The use of all antiplatelet drugs and aspirin in particular remained relatively stable over time and the association between use of these medications and year of the study was not significant (Figure 6). The changes in use for other classes of medications were not significant, and they are shown in Supplementary Figure 6. There were too few observations in the lipid-lowering drugs (without specification), high-intensity statins, and fibrates to analyse time-trends.

The prevalence of medication use of beta-blockers, ACEI/ARBs and statins increased in the years 1993–2017. The prevalence of antiplatelet drugs and aspirin in particular remained stable in this time.
Figure 6 

Time trends in medication use.

Each circle represents a study and the size of the circle is proportional to the number of participants in the study.

Guideline compliance

From the publications included, 19 articles reported whether the prevalence of medication use was adequate. Half of them reported that medication use was low [59, 62, 76, 77, 78] or insufficient compared to guideline recommendations [2, 12, 48, 61, 79]. Other articles reported that cardioprotective medication use was adequate or high [32, 60, 66, 67, 73, 75], or in line with guideline recommendations [38, 65, 80].

Determinants of medication use

Determinants reported in publications

Variables independently associated with medication use included sex, age, socio-economic status, residency, prevalence of cardiovascular risk factors, diagnosis category of CHD patients and health care setting.

It was a common finding that prevalence of cardioprotective medication use was lower in women [2, 12, 48, 77], and younger individuals [12, 81]. For example, male patients had an OR ranging from 1.29, (95%CI 1.11–1.49) to 1.54 (95%CI 1.06 – 2.24) for statin use compared to females [12, 77]; and patients aged 60 or older presented an OR ranging from 1.42 (95%CI,1.05–1.92) for the use of antiplatelet drugs [12] and 1.94 (1.07–3.50) for the use of cardioprotective medication in general [81].

The presence of cardiovascular risk factors associated with higher medication use. The odds of medication use were higher for overweight (OR of ACEI/ARB use 2.56, 95%CI 1.74–3.77), obese (OR of ACEI/ARB use 2.96, 95%CI 2.00–4.38) and diabetic patients (OR of statin use 1.60, 95%CI 1.08–2.37) [12]. Higher use of aspirin was identified among current [77], and former smokers [81], with OR of 1.83 (95%CI 1.35–2.50) and 1.41 (95%CI 1.03–1.93) respectively compared to non-smokers. High blood pressure was associated with higher use of beta-blockers and ACEI (OR 1.36, 95%CI 1.21–1.52, and 1.74, 95%CI 1.55–1.95 respectively), and high cholesterol with higher use of statins (OR 4.34, 95%CI 3.77–4.99) [77].

A few articles identified the diagnosis category of CHD patients as determinant for medication use. Having a previous PCI was an independent determinant for higher use of antiplatelet drugs (OR 2.00, 95%CI 1.30–2.31) [2], and previous PCI or CABG were associated with higher use of statins (OR 2.37, 95%CI 2.07–2.72) [77]. One publication reported that patients who attended public centres (OR 1.99, 95%CI 1.54–2.59), or centres that are a combination of public and private (1.96, 95%CI 1.51–2.53) had higher odds of cardioprotective medication use compared to those attending private centres [3].

Lower SES [2, 12, 48, 81] and living in rural areas [12] were also associated with lower medication use. In particular, participants from the wealthiest group had an OR of medication use of 2.54 (95%CI 1.08 – 5.95) for use of cardioprotective medication in general, to 5.94 (95%CI 2.80 – 12.6) for statin use compared to the least wealthy group [12, 48]; and urban dwellers had an OR of 1.41 (95%CI 1.04–1.92) for use of ACEI/ARB compared to participants from a rural location [12].

Meta-regression results

The health care setting, i.e. type of centre where the study had been conducted had a significant effect on medication prevalence for beta-blockers, statins, overall antiplatelet drugs and aspirin: the odds of medication use were lower in studies conducted in primary care and community settings compared to academic and tertiary centres. Further, the odds of overall antiplatelet drugs use were lower in public centres, and the odds of aspirin use were lower in cardiac rehabilitation settings, compared to academic and tertiary centres (Table 3).The use of ACEI/ARBs was not significantly associated with any of the covariates in the meta-regression models. The remaining medications or medication classes presented too few observations and thus meta-regression models could not be fit.

Table 3

Results of the meta-regression models showing factors independently associated with medication use.

Results are expressed in odds ratios and 95% confidence intervals. Sex was treated as a numerical variable (percentage of women included in the study). The reference category for setting was ‘Academic/Tertiary Hospital,’ and the reference category for diagnosis was ‘coronary heart disease.’ Abbreviations: acute coronary syndrome (ACS), coronary artery bypass graft (CABG), coronary heart disease (CHD), percutaneous coronary intervention (PCI percutaneous coronary intervention). *p = 0.05.


VARIABLE BETA-BLOCKERS ACEI ARB STATIN ANTIPLATELET DRUGS (OVERALL) ASPIRIN

Intercept 2.56 (0.89–7.37)* 0.84 (0.36–1.94) 3.55 (1.04–12.17) 6.57 (2.60–16.57)* 6.06 (2.18–16.87) *

Sex 1.01 (0.98–1.04) 1.01 (0.98–1.04) 1.01 (0.97–1.04) 1.00 (0.97–1.03) 1.00 (0.97–1.03)

Setting

    Primary care/community 0.18 (0.04–0.96) * 0.11 (0.02–0.62)* 0.12 (0.03–0.40)* 0.19 (0.04–0.96) *

    Public centre 0.35 (0.07–1.69) 0.28 (0.09–0.86)*

    Cardiac rehabilitation 0.92 (0.30–2.78) 1.87 (0.53–6.61) 0.38 (0.14 – 1.04) 0.07 (0.02–0.33) *

    Other 0.71 (0.27–1.88) 0.67 (0.27–1.64) 0.73 (0.37–1.44) 1.03 (0.36–2.92)

Diagnosis

ACS 1.41 (0.78–2.54) 1.65 (0.90–3.03) 1.73 (0.89–3.37)

PCI 0.70 (0.21–2.28) 0.92 (0.30 – 2.82) 0.94 (0.29–3.08)

CABG 1.21 (0.43–3.37) 0.85 (0.27 – 2.65)

CABG, PCI 0.58 (0.11–2.97) 1.26 (0.38 –4.21) 1.31 (0.37–4.64)

The percentage of women included in the study and the previous CHD diagnosis category of the patient were not significantly associated with the use of any medication class. The number of publications reporting on age, SES and cardiovascular risk factors in a comparable format was low and thus they couldn’t be included in the meta-regression.

Discussion

Summary of main findings

The current systematic review shows large variation in the use of cardioprotective medication among CHD patients, ranging from 55.8% for the use of ACEI/ARB drugs to 85.0% for the use of aspirin. A similar number of studies reported suboptimal and adequate guideline compliance. Time-trend analysis for the period 1993 to 2017 showed an increase in the use cardioprotective medication, with the exception of all antiplatelet drugs and aspirin. Use of beta-blockers, statins, overall antiplatelet drugs and aspirin in community settings was lower compared to academic and tertiary centres. The use of antiplatelet drugs in public centres and of aspirin in rehabilitation centres was also lower compared with tertiary centres.

Prevalence of medication

The prevalence of cardioprotective medication that we observe in South America varies per medication class and shows a general underuse of medications. We observe differences in the prevalence of medication use reported in Europe and North America [8, 9, 82]. When comparing prevalence estimates found in this review, we observed that the prevalence of antiplatelet drugs and beta-blockers was higher than the estimates found for the PURE study [8] (55.4% of antiplatelet use and 45.4% of beta-blocker use in Europe and Canada) and a systematic review by Naderi et al. (65% of antiplatelet use and 62% of beta-blocker use) [9]. The prevalence of ACEI/ARBs we observed was higher than reported in the PURE Study (46.8% in Europe and North America), but lower than in the review by Naderi et al. (70%) [9]. Prevalence estimates from the international EUROASPIRE IV [82] registry were higher than the ones observed in our review for all medication classes (93.8% for antiplatelets, 82.6% for beta-blockers,58.9% for ACE inhibitors and 27% for insulin) except oral hypoglycaemics (oral sulphonylurea 24.9%) and lipid lowering drugs (fibrates 1.8%). The prevalence of statin use we observed was higher than reported in the PURE study (56.7% in Europe and North America), similar to the prevalence estimate described in a systematic review (76%) [9] and lower than the estimate from EUROASPIRE IV (85%) [82].

However, direct comparison with these studies is challenging because they were conducted in different contexts, regions and time periods and other definitions of medication use. The PURE study was conducted entirely in community settings in high-, low- and middle-income countries and regions. The review by Naderi et al. [9] included studies from high income countries in Europe, North America and Australia, and their definition of medication use was limited to prescription refills. EUROASPIRE IV included a majority of secondary and tertiary level centers and was conducted from 2012–2013, while the present review also included research from community settings and studies that started since 1993 [82].

Guideline compliance

Most publication included in this review reported that the prevalence of medication use is suboptimal, while others articles find it to be in compliance with guidelines. Despite some publications considering treatment rates high or adequate, we still find that a notable proportion of the patients do not receive guideline-recommended medications: for example, one third of CHD patients were not receiving beta-blockers and almost one fourth were not receiving statins, although these medications are recommended by guidelines. This low use of antihypertensives may be explained by individuals having adequate blood pressure levels or contraindications, despite guidelines recommending these drugs for all CHD patients. Statins, however, are generally well-tolerated drugs and they are recommended to all CHD patients regardless of cholesterol levels. Therefore, the fact that a substantial proportion of patients does not use them may respond to factors other than possible contraindications. Challenges to adhere to guidelines identified by clinicians include difficulties to change usual practice, time pressure and case complexity among others [83].

Articles considering the prescription rates adequate still noted that medication use decreased with time after diagnosis [59, 75], indicating that there is room to improve medication adherence and secondary prevention of CHD [59]. It is noteworthy that some publications report that achievement of cardiovascular risk factor targets was inadequate despite high levels of medication use [59, 66, 67, 75, 80], which may be attributed to the use of suboptimal doses [59].

Time trends

We observed increased use of most cardioprotective medications. These trends are in line with large surveys conducted in Europe that report an increase of the use of cardioprotective medications from 1999 and 2004 to 2013 [84, 85]. These changes may be attributed in part to the implementation of evidence-based clinical guidelines and public health policies in many South American countries. Mendis et al. [77] previously highlighted the lack of clinical guidelines as a potential factor contributing to the low treatment rates in the PREMISE study. Guideline recommendations have changed in the last decades. For example, the target LDL cholesterol level recommended in guidelines by scientific societies has become lower, from 100 mg/dl in guidelines from 2008 and 2001 [86, 87], to 75mg/dl [88], and finally to 70mg/dl in the most recent guidelines [89, 90, 91, 92]. The recommendation to prescribe statins to CHD patients changed accordingly, and the most recent recommendations from scientific societies and clinical guidelines recommend statin use in CHD patients regardless of their cholesterol level. These changes may promote a higher intake of statin use, which is in line with research showing a decrease in cholesterol levels globally and also in the South American region [93].

The gradual investment and unfolding of public healthcare systems with wide coverage, such as the Sistema Unico de Saúde (SUS) in Brazil, has promoted the use of medication by a growing primary care network and provision of drugs free of charge [48]. Although there are still barriers to medication access, the growing coverage of public healthcare systems has allowed more CHD patients to access recommended medications.

Determinants of medication use

Participant’s characteristics

Several studies found female sex and younger age to be independent predictors for lower use of medication [48, 94, 95, 96, 97]. Women often receive less prescriptions and have lower adherence to medication, which has been attributed to physician and patients factors like presentation of distinct symptoms, and underestimation of disease severity and fear of side effects [48, 94, 95, 96, 97]. In our meta-regression models, however, the percentage of women included in the study was not significantly associated with higher use of any medication class. The lower use of medication in younger patients may be explained by better medication adherence in patients with known risk factors, like older age [12].

High SES showed an independent and strong association with medication use in many studies. Lack of affordability could be a reason for this difference [76], however many cardioprotective medications have a low cost (like aspirin), and in Argentina, Chile and Brazil, four of the medication classes (antiplatelets, beta-blockers ACEI, and statins) studied in this review are available free of charge [12, 73, 79]. Some studies remark that although medications may be affordable, not all of them are always available in the public sector [48, 76, 77, 81]. The higher medication use in the higher SES patients may be explained by affordability and access to private healthcare [8, 48, 77, 81].

Health care setting

Our meta-regression showed that health care setting was independently associated with the use of beta-blockers, antiplatelet drugs, aspirin and statin. In particular, in comparison to tertiary centers, the use of these medications was lower in primary care and community settings; use of antiplatelet drugs was lower in public centres; and use of aspirin was lower in cardiac rehabilitation settings. These findings are in line with previous results showing medication prevalence to be lower in studies conducted in primary care settings compared to those conducted in tertiary hospitals [8, 59, 81].

This may in part be due to overestimation of medication utilization prevalence in tertiary level clinical settings. First, patients attending tertiary centres may be older or more severely ill. Academic and tertiary hospitals have more capacity to provide active follow-up to patients compared to settings where care may be more fragmented. Therefore, patients attending academic or tertiary level centres for follow-up after an event may receive more specialized advice and prescriptions than patients attending a primary care facility, resulting higher rates of medication use [59, 81]. Furthermore, it has been suggested that studies conducted in tertiary care settings may not include individuals without access to care [4]. Therefore, it is important to consider the setting of the study when interpreting results from research on prevalence of medication use.

Strengths

This review summarizes evidence of a large number of studies from South America published in English, Spanish and Portuguese between 2000 and 2021. The comprehensive search included any article that contained data on any of the cardioprotective medication assessed in secondary prevention of CHD: anti-platelet drugs, lipid-lowering drugs, antihypertensive agents (beta-blockers, ACE-inhibitors, ARBs, diuretics, and nitrates), oral hypoglycaemics and insulin. We included articles from regional and international databases, and from a variety of settings (public and private centres, academic/tertiary centres and community settings, and urban and rural areas). In light of the results of this review, which show that care setting may influence the estimation of medication use prevalence, it is especially valuable that our review includes studies conducted in various settings.

In total, this review pooled the prevalence of cardioprotective medication use of 23,938 participants. Most of the articles included in this review had a low or moderate risk of bias, indicating low risk of bias for the pooled estimate.

Limitations

This study aimed to summarize evidence on the prevalence of cardioprotective medication use for secondary prevention of CHD in South America. However, most studies were conducted in a limited number of countries and predominantly in Brazil, the largest and most populous country in South America. Our sensitivity analysis showed no prevalence differences between Brazil and the rest of the included countries, though no information was available for Bolivia, Ecuador, Guyana, Paraguay, Peru or Venezuela. As a result, the prevalence estimates of our study may not be generalizable for these countries. Furthermore, we were not able to run meta-regression for several medication classes because there were few observations. We pooled prevalence estimates from the included articles, but the heterogeneity in the from the meta-analysis models was high. We thus presented the results by subgroups and undertook meta-regression models to identify factors associated with medication use, which slightly reduced the heterogeneity of estimates.

Conclusion

The current systematic review shows large variation in the use of cardioprotective medication among CHD patients in South America, ranging from 55.8% for the use of ACEI/ARB drugs to 85.1% for the use of aspirin. Medication use was lower in community settings, and it was often considered suboptimal in relation to clinical guidelines. The use of most cardioprotective medication classes has increased in the last decades though efforts should be made to further increase the use of these medications among CHD patients, particularly in community settings.

Additional Files

The additional files for this article can be found as follows:

Supplementary Figures

Figures 1 to 6. DOI: https://doi.org/10.5334/gh.1124.s1

Supplementary Files

Supplementary Files 1 to 3. DOI: https://doi.org/10.5334/gh.1124.s2

Acknowledgements

The research team is very grateful to Felix Weijdema from Utrecht University Library and Daniel Comandé from Instituto de Efectividad Clínica y Sanitaria Library for their support in drafting the search strategy and using literature databases. The team would also like to Dr. Thomas Debray for his methodological advice on meta-analysis and meta-regression modelling.

Competing Interests

KKG is deputy editor for the journal Global Heart. DEG is editor in chief for the journal Global Heart. All other authors have no competing interests.

Author Contributions

AMF, IV and KKG conceived and planned the research. AMF and ED screened the publications, assessed their quality and extracted the relevant data. AMF analyzed the data and wrote the manuscript with the support of ED. All authors contributed to the interpretation of results and discussion. IV and KKG had an equal contribution to this manuscript.

References

  1. Institute for Health Metrics and Evaluation (IHME). Global Burden of Disease Compare [Internet]. GBD Compare Data Visualization; 201AD. http://www.healthdata.org/gbd/2019 (accessed 9 June 2021). 

  2. Gaedke MA, Soares Dias da Costa J, Fernandes Manenti E, et al. Use of medicines recommended for secondary prevention of acute coronary syndrome. Rev Saúde Púbilca. 2015; 49(88). DOI: https://doi.org/10.1590/S0034-8910.2015049005978 

  3. Berwanger O, Alberto L, Fernando J, et al. Special Article Evidence-Based Therapy Prescription in High-Cardiovascular Risk Patients: The REACT Study. Arq Bras Cardiol. 2013; 100(3): 212–20. DOI: https://doi.org/10.5935/abc.20130062 

  4. Ergatoudes C, Thunström E, Rosengren A, et al. Long-term secondary prevention of acute myocardial infarction (SEPAT) – guidelines adherence and outcome. BMC Cardiovasc Disord [Internet]. 2016; 16(1): 1–8. DOI: https://doi.org/10.1186/s12872-016-0400-6 

  5. Herdy AH, López-Jiménez F, Terzic CP, et al. South american guidelines for cardiovascular disease prevention and rehabilitation. Arq Bras Cardiol. 2014; 103(2): 1–31. DOI: https://doi.org/10.5935/abc.2014S003 

  6. Gaziano TA, Bitton A, Anand S, Abrahams-Gessel S, Murphy A. Growing Epidemic of Coronary Heart Disease in Low- and Middle-Income Countries. Curr Probl Cardiol. 2010; 35(2): 72–115. DOI: https://doi.org/10.1016/j.cpcardiol.2009.10.002 

  7. Perel P, Avezum A, Huffman M, et al. Reducing Premature Cardiovascular Morbidity and Mortality in People With Atherosclerotic Vascular Disease The World Heart Federation Roadmap for Secondary Prevention of Cardiovascular Disease. Glob Heart. 2015; 10(2): 99–110. DOI: https://doi.org/10.1016/j.gheart.2015.04.003 

  8. Yusuf S, Islam S, Chow CK, et al. Use of secondary prevention drugs for cardiovascular disease in the community in high-income, middle-income, and low-income countries (the PURE Study): A prospective epidemiological survey. Lancet. 2011; 378(9798): 1231–43. DOI: https://doi.org/10.1016/S0140-6736(11)61215-4 

  9. Naderi SH, Bestwick JP, Wald DS. Adherence to Drugs That Prevent Cardiovascular Disease: Meta-analysis on 376,162 Patients. AJM. 2012; 125(9): 882–887.e1. DOI: https://doi.org/10.1016/j.amjmed.2011.12.013 

  10. Kotseva K, Wood D, De Backer G, De Bacquer D, Pyörälä K, Keil U. Cardiovascular prevention guidelines in daily practice: A comparison of EUROASPIRE I, II, and III surveys in eight European countries. Lancet. 2009; 373(9667): 929–40. DOI: https://doi.org/10.1016/S0140-6736(09)60330-5 

  11. Maddox TM, Chan PS, Spertus JA, et al. Variations in coronary artery disease secondary prevention prescriptions among outpatient cardiology practices: Insights from the NCDR (National Cardiovascular Data Registry). J Am Coll Cardiol. 2014; 63(6): 539–46. DOI: https://doi.org/10.1016/j.jacc.2013.09.053 

  12. Avezum A, Oliveira GBF, Lanas F, et al. Secondary CV Prevention in South America in a Community Setting: The PURE Study. Glob Heart. 2017; 12(4): 305–13. DOI: https://doi.org/10.1016/j.gheart.2016.06.001 

  13. Zhao M, Klipstein-grobusch K, Wang X, et al. Prevalence of cardiovascular medication on secondary prevention after myocardial infarction in China between 1995–2015: A systematic review and meta-analysis. PLoS ONE. 2017; Ci: 1–16. DOI: https://doi.org/10.1371/journal.pone.0175947 

  14. Zhao M, Cooney MT, Klipstein-Grobusch K, et al. Simplifying the audit of risk factor recording and control: A report from an international study in 11 countries. Eur J Prev Cardiol. 2016; 23(11): 1202–10. DOI: https://doi.org/10.1177/2047487316647827 

  15. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009; 62(10): 1006–12. DOI: https://doi.org/10.1016/j.jclinepi.2009.06.005 

  16. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan — a web and mobile app for systematic reviews. Systematic Reviews; 2016. DOI: https://doi.org/10.1186/s13643-016-0384-4 

  17. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez NJGC. Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009; 42(2): 377–81. DOI: https://doi.org/10.1016/j.jbi.2008.08.010 

  18. Li H, Oldenburg B, Chamberlain C, et al. Diabetes prevalence and determinants in adults in China mainland from 2000 to 2010: A systematic review. Diabetes Res Clin Pract. 2012; 98(2): 226–35. DOI: https://doi.org/10.1016/j.diabres.2012.05.010 

  19. RStudio Team. RStudio: Integrated Development Environment for R. Boston, MA: RStudio, Inc.; 2016. 

  20. Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010; 36(3): 1–48. DOI: https://doi.org/10.18637/jss.v036.i03 

  21. Ribeiro D, Silva RP, Barboza DRMM, Lima-Júnior RCP, Ribeiro RA. Clinical correlation between N-terminal pro-b-type natriuretic peptide and angiographic coronary ather-osclerosis. Clinics. 2014; 69(6): 405–12. DOI: https://doi.org/10.6061/clinics/2014(06)07 

  22. Finimundi HC, Caramori PA, Parker JD. Effect of Diuretic Therapy on Exercise Capacity in Patients With Chronic Angina and Preserved Left Ventricular Function. 2007; 49(5): 275–9. DOI: https://doi.org/10.1097/FJC.0b013e3180385ad7 

  23. Hueb W, Soares P, Gersh B, et al. The Medicine, Angioplasty, or Surgery Study (MASS-II): A Randomized, Controlled Clinical Trial of Three Therapeutic Strategies for Multivessel Coronary Artery Disease. J Am Coll Cardiol. 2004; 43(10). DOI: https://doi.org/10.1016/j.jacc.2003.08.065 

  24. Scherr C, Cunha AB da, Magalhães CK, Abitibol RA, Barros M, Cordovil I. Life-habit intervention in a public institution. Arq Bras Cardiol. 2010; 94(6): 730–7. DOI: https://doi.org/10.1590/S0066-782X2010005000058 

  25. Silveira AD, Ribeiro RA, Rossini APW, et al. Association of anemia with clinical outcomes in stable coronary artery disease. Pathophysiol Nat Hist. 2005; 19(1): 21–6. DOI: https://doi.org/10.1097/MCA.0b013e3282f27c0a 

  26. Gomes VO, Blaya P, Lasevitch R, et al. Original Article Impact of Chronic Kidney Disease on the Efficacy of Drug-Eluting Stent: Long-term Follow-up Study. Arq Bras Cardiol. 2011; 96(5): 346–51. DOI: https://doi.org/10.1590/S0066-782X2011005000045 

  27. Villacorta AS, Junior HV, Batista MJS, et al. Original Article High On-Treatment Platelet Reactivity Predicts Cardiac Events in Patients with Drug-Eluting Stents. Arq Bras Cardiol. 2013; 100(3): 221–7. DOI: https://doi.org/10.5935/abc.20130044 

  28. Pesaro AEP, Serrano CV, Fernandes JL, et al. Pleiotropic effects of ezetimibe/simvastatin vs. high dose simvastatin. Int J Cardiol. 2012; 158(3): 400–4. DOI: https://doi.org/10.1016/j.ijcard.2011.01.062 

  29. Santos VF, Braga F, Almeida Junior GLG, et al. Resistência ao AAS na doença coronariana estável. Int j Cardiovasc Sci. 2015; 28(5): 363–9. 

  30. Kimura-medorima ST, Paula A, Lazaro B, et al. P-wave duration is a predictor for long-term mortality in post-CABG patients. 2018; 1–13. DOI: https://doi.org/10.1371/journal.pone.0199718 

  31. Breda JR, Silvia A, Ragognetti C, et al. Effect of ventral cardiac denervation in the incidence of atrial fibrilation after coronary artery bypass graft surgery. 2008; 23(2): 204–8. 

  32. Ribeiro MS, Nascimento TCDC, Murakami B, Bergamasco EC, Lopes CT, Santos ER. Desfechos clínicos dos pacientes submetidos à intervenção coronária percutânea com stent bioabsorvível eluidor de everolimus. Medicina (B Aires). 2018; 51(4): 237–46. DOI: https://doi.org/10.11606/issn.2176-7262.v51i4p237-246 

  33. Saffi MAL, Polanczyk CA, Rabelo-Silva ER. Lifestyle interventions reduce cardiovascular risk in patients with coronary artery disease: A randomized clinical trial. Eur J Cardiovasc Nurs. 2014; 13(5): 436–43. DOI: https://doi.org/10.1177/1474515113505396 

  34. Souza CF, El Mouallem AM, de Brito Júnior FS, Abizaid AA, Almeida BO, Almeida AG, Nascimento TC, Perin MA, Caixeta A. Safety and efficacy of biolimus-eluting stent with biodegradable polymer: insights from EINSTEIN (Evaluation of Next-generation drug-eluting STEnt IN patients with coronary artery disease) Registry. Einstein (Sao Paulo). 2013 Jul–Sep; 11(3): 350–6. DOI: https://doi.org/10.1590/s1679-45082013000300015. PMID: 24136763; PMCID: PMC4878595. 

  35. Vilar CP, Cotrim HP, Florentino GSA, Bragagnoli G, Schwingel PA, Barreto CPV. Doença hepática gordurosa não alcoólica em pacientes com doença coronariana de uma área do nordeste do Brazil. Arq Gastroenterol. 2015; 52(2): 111–6. DOI: https://doi.org/10.1590/S0004-28032015000200007 

  36. Nery RM, Zanini M, Lima B De, Bühler P. Tai Chi Chuan improves functional capacity after myocardial infarction: A randomized clinical trial. Am Heart J. 2015; 169(6): 854–60. DOI: https://doi.org/10.1016/j.ahj.2015.01.017 

  37. Liberato ACS, Rodrigues RCM, São-João TM, Costa Alexandre NM, Bueno Jayme Gallani MC. Satisfaction with medication in coronary disease treatment: Psychometrics of the Treatment Satisfaction Questionnaire for Medication. Rev Latino-Am Enfermagen. 2016; 24: e2705. DOI: https://doi.org/10.1590/1518-8345.0745.2705 

  38. Mattos LA, Berwanger O, Silva E, et al. Clinical Outcomes at 30 days in the Brazilian Registry of Acute Coronary Syndromes (ACCEPT). Arquivos Brasileiros de Cardiologia. 2012; 100(1): 6–13. 

  39. Furuya RK, Arantes EC, Dessotte CAM, et al. Randomized controlled trial of an educational programme to improve self-care in Brazilian patients following percutaneous coronary intervention. John Wiley Sons Ltd. 2014; 895–908. DOI: https://doi.org/10.1111/jan.12568 

  40. de Oliveira LMSM, Costa IMNB de C, da Silva DG, et al. Readmission of patients with acute coronary syndrome and determinants. Arq Bras Cardiol. 2019; 113(1): 42–9. DOI: https://doi.org/10.5935/abc.20190104 

  41. Simon S, Coronel C, Silveira de Almeida A, Marcadenti A. Left lateral intercostal region versus subxiphoid position for pleural drain during elective coronary artery bypass graft surgery: Randomized clinical trial. Sao Paulo Med J. 2019; 137(1): 66–74. DOI: https://doi.org/10.1590/1516-3180.2018.040940119 

  42. Chaves GSS, Lima G, Ghisi DM, Britto RR, Grace SL. Maintenance of Gains, Morbidity, and Mortality at 1 Year Following Cardiac Rehabilitation in a Middle-Income Country: A Wait-List Control Crossover Trial. J Am Heart Assoc. 2019; 8: e011228. DOI: https://doi.org/10.1161/JAHA.118.011228 

  43. Carvalho T De, Curi ALH, Andrade DF, Singer J da M, Benetti M, Mansur AJ. Cardiovascular Rehabilitation of Patients With Ischemic Heart Disease Undergoing Medical Treatment, Percutaneous Transluminal Coronary Angioplasty, and Coronary Artery Bypass Grafting. Arq Bras Cardiol. 2006; 88(1): 65–70. 

  44. Uchôa CHG, De Jesus Danzi-Soares N, Nunes FS, et al. Impact of OSA on cardiovascular events after coronary artery bypass surgery. Chest. 2015; 147(5): 1352–60. DOI: https://doi.org/10.1378/chest.14-2152 

  45. Lima-Filho GL, Foss MC. Predictors of restenosis after percutaneous coronary intervention using bare-metal stents. A comparison between patients with and without dysglycemia Predictors of restenosis after percutaneous coronary intervention using bare-metal stents. A comparison. Braz J Med Biol Res. 2010; 43(6): 572–9. DOI: https://doi.org/10.1590/S0100-879X2010007500051 

  46. Cruz LN, Camey SA, Fleck MP, Polanczyk CA. World Health Organization quality of life instrument-brief and Short Form-36 in patients with coronary artery disease: Do they measure similar quality of life concepts? Psychol Heal Med. 2009; 14(5): 619–28. DOI: https://doi.org/10.1080/13548500903111814 

  47. Pantoni F, Caruso R, Mezzalira D, Arena R, Amaral-Neto O. Continuous Positive Airway Pressure During Exercise Improves Walking Time in Patients Undergoing Inpatient Cardiac Rehabilitation After Coronary Artery Bypass Graft Surgery. J Cardiopulm Rehabil Prev. 2016; 36: 20–7. DOI: https://doi.org/10.1097/HCR.0000000000000144 

  48. Birk MG, Carvalho Goulart A, Lotufo PA, Martins Benseñor I. Secondary prevention of coronary heart disease: A cross-sectional analysis on the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Sao Paulo Med J. 2019; 137(3): 223–33. DOI: https://doi.org/10.1590/1516-3180.2018.0531140319 

  49. Lorenzo A De, Oliveira G, Naue VM, Lima RSL. Influence of typical angina versus inducible myocardial ischemia in the contemporary management of stable coronary artery disease. Ther Adv Cardiovasc Dis. 2014; 8(4): 145–54. DOI: https://doi.org/10.1177/1753944714531066 

  50. Bohatch Júnior MS, Matkovski PD, Di Giovanni FJ, Fenili R, Varella EL, Dietrich A. Incidence of postoperative atrial fibrillation in patients undergoing on-pump and off-pump coronary artery bypass grafting. Braz J Cardiovasc Srug. 2015; 30(3): 316–24. DOI: https://doi.org/10.5935/1678-9741.20150040 

  51. Feguri GR, Ruiz P, Lima L De, Borges DDC, Toledo LR. Preoperative carbohydrate load and intraoperatively infused omega-3 polyunsaturated fatty acids positively impact nosocomial morbidity after coronary artery bypass grafting: A double- blind controlled randomized trial. Nutr J. 2017; 16(24): 1–7. DOI: https://doi.org/10.1186/s12937-017-0245-6 

  52. Garlet AB, Machado Cardoso D, Daros dos Santos T, Nunes Pereira S. Relationship between functional class and left ventricular ejection fraction in patients with coronary heart disease candidates for cardiac rehabilitation. Sci Med. 2017; 27(3). 

  53. Lelis JD, Chaves G, Lima G, Ghisi DM, Grace SL, Britto RR. Validity of the Incremental Shuttle Walk Test to Assess Exercise Safety When Initiating Cardiac Rehabilitation in Low-Resource Settings. J Cardiopulm Rehabil Prev. 2019; 99(April 2017): E1–7. DOI: https://doi.org/10.1097/HCR.0000000000000412 

  54. Alexandre A, Aguiar F De, Mourilhe-rocha R, Esporcatte R, Amorim LC. Original Article Long-Term Analysis in Acute Coronary Syndrome: Are there any Differences in Morbidity and Mortality? 2010; 705–12. 

  55. Fernandes RWA, Dantas JM, et al. Original Article Impact of Stenting and Oral Sirolimus on Endothelium-Dependent and Independent Coronary Vasomotion. Arquivos brasileiros de cardiologia. 2011; 290–9. 

  56. Fuchs ARCN, Meneghelo RS, Stefanini E, et al. Exercise may cause myocardial ischemia at the anaerobic threshold in cardiac rehabilitation programs. Brazilian Journal of Medical and Biological Research. 2009; 42: 272–8. DOI: https://doi.org/10.1590/S0100-879X2009000300008 

  57. Abreu-Silva EO, Costa RA, Abizaid AS, et al. Stents Farmacológicos Liberadores de Everolimus XienceTM V no Tratamento de Pacientes com Lesões Coronárias Complexas na Prática Diária: Resultados Iniciais do Registro Brasileiro BRAVO. Rev Bras Cardiol Invasiva. 2011; 19(4): 357–66. DOI: https://doi.org/10.1590/S2179-83972011000400005 

  58. Baptista VC, Palhares LC, Paulo P, et al. Six-minute walk test as a tool for assessing the quality of life in patients undergoing coronary artery bypass grafting surgery. Revista Brasileira de Cirurgia Cardiovascular. 2012; 27(2): 231–9. DOI: https://doi.org/10.5935/1678-9741.20120039 

  59. Rossi E, Perman G, Michelangelo H, et al. Adherencia a la prevención secundaria de la enfermedad coronaria. Med (Buenos Aires). 2014; 74: 99–103. 

  60. Trivi M, Lakowsky A, Zeballos C, et al. Registro prospectivo de tratamiento antitrombótico en síndromes coronarios agudos (EPICOR) Prospective Registry of Antithrombotic Therapy in Acute Coronary Syndromes (EPICOR). Rev Argent Cardiol. 2019; 87(1). 

  61. Siniawski D, Masson W, Rossi E, Damonte J, Halsband A, Pizarro R. Elegibilidad para la indicación de inhibidores de PCSK9 según las recomendaciones de diferentes sociedades científicas. Med. 2019; 79(2): 104–10. 

  62. Alvarez J, Migliaro G, Leiva G, et al. Angioplastia primaria en diabéticos vs. no diabéticos con infarto agudo de miocardio: predictores de mortalidad. Rev Gastroenterol México. 2016; 86(1): 11–7. DOI: https://doi.org/10.1016/j.acmx.2015.08.006 

  63. Gurfinkel EP, Leon R, Fuente D, Mendiz O. Flu vaccination in acute coronary syndromes and planned percutaneous coronary interventions (FLUVACS) Study One-year follow-up. Eur Heart J. 2004; 25: 25–31. DOI: https://doi.org/10.1016/j.ehj.2003.10.018 

  64. Castillo Costa YB, Mauro VM, Charask AA, Fairman E, Buhezo H, Barrero C. Use of high-intensity statin strategy. Are the guidelines followed? Rev Argent Cardiol. 2018; 86(1): 45–7. DOI: https://doi.org/10.7775/rac.v86.i1.11430 

  65. Stockins B, Albornoz F, Martínez D, et al. Resultados chilenos del registro intenacional de factores de riesgo y tratamiento de angina inestable e infarto al miocardio sin supradesnivel del segmento ST: ACCORD (ACcute CORonary syndrome Descriptive study). Rev Med Chile. 2011; 139: 19–26. DOI: https://doi.org/10.4067/S0034-98872011000100003 

  66. Nazzal A, Mercadal E, Garcés E, Yovaniniz P, Sanhueza P. Prevención secundaria post infarto agudo de miocardio en hospitales públicos: implementación y resultados de las garantías GES. Rev Med Chile. 2013; 141: 977–86. DOI: https://doi.org/10.4067/S0034-98872013000800003 

  67. Neira VV, Potthoff MN, Quiñiñir LS, et al. Logro de metas de prevención secundaria, prescripción farmacológica y eventos cardiovasculares mayores en pacientes con enfermedad coronaria. Rev Med Chile. 2013; 141: 870–8. DOI: https://doi.org/10.4067/S0034-98872013000700006 

  68. Noriega V, Pennanen C, Sánchez MP, et al. (TTA)n Polymorphism in 3-Hydroxy-3-Methylglutaryl-Coenzyme A and Response to Atorvastatin in Coronary Artery Disease Patients. Basic Clin Pharmacol Toxicol. 2008; 104: 211–5. DOI: https://doi.org/10.1111/j.1742-7843.2008.00341.x 

  69. Fernandez A, Restrepo R, Villa P, Garcés J, Montero G. Angioplasty with stent vs. coronary artery bypass grafting in multivessel disease (ACIRE). Colomb J Cardiol. 2009; 16(2). 

  70. Fernandez A, Aboodi MS, Milewski K, Delgado JA, Rodríguez A, Granada JF. Comparison of Adverse Cardiovascular Events and Bleeding Complications of Loading Dose of Clopidogrel 300 mg Versus 600 mg in Stable Patients Undergoing Elective Percutaneous Intervention (from the CADICE Study). AJC. 2011; 107(1): 6–9. DOI: https://doi.org/10.1016/j.amjcard.2010.08.035 

  71. Rueda-Clausen CF, Lahera V, Calderón J, et al. The presence of abdominal obesity is associated with changes in vascular function independently of other cardiovascular risk factors. Int J Cardiol. 2010; 139(1): 32–41. DOI: https://doi.org/10.1016/j.ijcard.2008.09.005 

  72. Vesga BE, Echeverri D. Resistencia al ácido acetil salicílico en pacientes con enfermedad coronaria. Rev Colomb Cardiol. 2006; 13(1): 13–22. 

  73. Gambogi RA, Baldizzoni M, Albornoz H, et al. Prevención secundaria en pacientes revascularizados coronarios en Uruguay: descripción de un programa, evaluación del control de los factores de riesgo y efecto en la mortalidad. Clin Invest Ateriosc. 2010; 22(2): 59–69. DOI: https://doi.org/10.1016/j.arteri.2009.12.001 

  74. Dayan V, Perez D, Silva E, Soca G, Estigarribia J. CABG and preoperative use of beta-blockers in patients with stable angina are associated with better cardiovascular survival. Brazilian J Cardiovasc Surg. 2018; 33(1): 47–53. DOI: https://doi.org/10.21470/1678-9741-2017-0138 

  75. Vazquez H, Burdiat G, Alonso P, Sandoya E, Tejada J. Control del riesgo cardiovascular postinternación en pacientes coronarios. Rev Urug Cardiol. 2011; 26: 108–14. 

  76. de Souza Groia Veloso RC, Cruzeiro M, Menezes Dias B, Moreira Reis AM. Profile of use and access to statins in patients with coronary arterial disease in an outpatient clinic of a teaching hospital. Curr Med Res Opin. 2020; 36(9): 1427–31. DOI: https://doi.org/10.1080/03007995.2020.1793313 

  77. Mendis S, Abegunde D, Yusuf S, Ebrahim S, Shaper G, Ghannem H. WHO study on Prevention of REcurrences of Myocardial Infarction and StrokE (WHO-PREMISE). Bull World Health Organ. 2005; 83(11): 820–31. 

  78. Castro LT De, Santos IDS, Goulart AC, et al. Elevated High-Sensitivity Troponin I in the Stabilized Phase after an Acute Coronary Syndrome Predicts All-Cause and Cardiovascular Mortality in a Highly Admixed Population: A 7-Year Cohort. Arq Bras Cardiol. 2019; 112(3): 230–7. DOI: https://doi.org/10.5935/abc.20180268 

  79. Henrique L, Gowdak W, Arantes RL, et al. Underuse of American College of Cardiology/American Heart Association Guidelines in Hemodialysis Patients Underuse of American College of Cardiology/American Heart Association Guidelines in Hemodialysis Patients. Ren Fail. 2009; 29: 559–65. DOI: https://doi.org/10.1080/08860220701395002 

  80. Ogawa Indio do Brasil C, Avezum Á, Uint L, et al. Cardiovascular prevention in coronary heart disease patients: Guidelines implementation in clinical practice. Revista Brasileira de Cirurgia Cardiovascular. 2013; 238–47. DOI: https://doi.org/10.5935/1678-9741.20130034 

  81. Vianna CA, Gonzalez DA, Matijasevich A. Utilização de ácido acetilsalicílico (AAS) na prevenção de doenças cardiovasculares: um estudo de base populacional Aspirin use in cardiovascular disease prevention: a population-based study. Cad Saúde Pública. 2012; 28(6): 1122–32. DOI: https://doi.org/10.1590/S0102-311X2012000600011 

  82. Smedt D De, Backer T De, Petrovic M, et al. Chronic medication intake in patients with stable coronary heart disease across Europe: Evidence from the daily clinical practice. Results from the ESC EORP European Survey of Cardiovascular Disease Prevention and Diabetes (EUROASPIRE IV) Registry. Int J Cardiol. 2020; 300: 7–13. DOI: https://doi.org/10.1016/j.ijcard.2019.09.015 

  83. Barth JH, Misra S, Aakre KM, et al. Why are clinical practice guidelines not followed? The European Federation of Clinical Chemistry and Laboratory Medicine and European Union of Medical Specialists joint working group on Guidelines. Clin Chem Lab Med. 2016; 54(7): 1133–9. DOI: https://doi.org/10.1515/cclm-2015-0871 

  84. Kotseva K, De Bacquer D, Jennings C, et al. Time Trends in Lifestyle, Risk Factor Control, and Use of Evidence-Based Medications in Patients With Coronary Heart Disease in Europe: Results From 3 EUROASPIRE Surveys, 1999–2013. Glob Heart. 2017; 12(4): 315–322.e3. DOI: https://doi.org/10.1016/j.gheart.2015.11.003 

  85. Achelrod D, Gray A, Preiss D, Mihaylova B. Cholesterol- and blood-pressure-lowering drug use for secondary cardiovascular prevention in 2004–2013 Europe. Eur J Prev Cardiol. 2017; 24(4): 426–36. DOI: https://doi.org/10.1177/2047487316676906 

  86. Oliveri R. Consenso SAC 2001. 1981; 7–8. 

  87. Sociedade Brasileira de Cardiologia. Diretrizes SBC 2004–2008. 

  88. Sociedade Brasileira de Cardiologia. Diretrizes SBC 2005–2009. Arquivos Brasileiros de Cardiologia. 

  89. Sociedade Brasileira de Cardiologia. Diretrizes SBC 2007–2011; 2011. 

  90. Sociedade Brasileira de Cardiologia. Diretrizes da SBC 2009–2014. Soc Bras Cardiol. 2014; 728. 

  91. Andrade MD, Macedo AVS, Piuzana ALR, et al. Diretrizes da Sociedade Brasileira de Cardiologia: Pocket Book 2013–2015. Soc Bras Cardiol. 2015; 7: 1–599. 

  92. Kreutzer G, Directiva C, El H, et al. Consenso Prevención Cardiovascular. Rev Argent Cardiol. 2012; 80(2). 

  93. Taddei C, Zhou B, Bixby H, et al. Repositioning of the global epicentre of non-optimal cholesterol. Nature. 2020; 582(7810): 73–7. DOI: https://doi.org/10.1038/s41586-020-2338-1 

  94. Koopman C, Vaartjes I, Heintjes EM, et al. Persisting gender differences and attenuating age differences in cardiovascular drug use for prevention and treatment of coronary heart disease, 1998–2010. Eur Heart J. 2013; 34(41): 3198–205. DOI: https://doi.org/10.1093/eurheartj/eht368 

  95. Manteuffel M, Williams S, Chen W, Verbrugge RR, Pittman DG, Steinkellner A. Influence of patient sex and gender on medication use, adherence, and prescribing alignment with guidelines. J Women’s Heal. 2014; 23(2): 112–9. DOI: https://doi.org/10.1089/jwh.2012.3972 

  96. Barrett E, Paige E, Welsh J, et al. Differences between men and women in the use of preventive medications following a major cardiovascular event: Australian prospective cohort study. Prev Med Reports. 2021; 22: 101342. DOI: https://doi.org/10.1016/j.pmedr.2021.101342 

  97. Redfors B. Women Are Less Likely to Get Secondary Prevention Medications and Cardiac Rehabilitation. Clinical Topics. American College of Cardiology; 2017. 

comments powered by Disqus