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

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

Global Heart
Volume 17 (2022): Issue 1
By: A. Marzà-Florensa,  E. Drotos,  P. Gulayin,  D. E. Grobbee,  V. Irazola,  K. Klipstein-Grobusch and  I. Vaartjes  
Open Access
|Jun 2022
Figures & tables

Figures & Tables

Figure 1

Study selection flow-chart.

Table 1

Characteristics of the studies included in the review.

PUBLICATIONSTUDY DURATIONCOUNTRYSTUDY DESIGNNCARE SETTINGDIAGNOSIS CATEGORYURBAN SETTING% WOMENAGESOCIOECONOMIC STATUS
Castillo y Costa, 2018NA-2015ArgentinaCohort210MI, CABG, PCIUnclear17.059.0 (9); 61.0 (9.0)
Fernandes, 20122003–2004BrazilRCT45PCI38.062.7 (9.9), 26–89
Gurfinkel, 20042001-NAArgentinaRCT301ACSUrban59 (8.7), 59 (7.9)
Ladeia, 20031995–1997BrazilCross-sectional104CHDUrban32.760.9 (8.1)Education: 10.6
Lima-Filho, 20102001–2002BrazilCohort70PCI22.957.6 (13.9), 59.4 (7.6)
Lorenzo, 20142008–2010BrazilCohort228CHDUrban46.163.15 (12.26)
Baptista, 20122009–2011BrazilCohort97Academic or Tertiary HospitalCABG33.363.5 (9.4), 42–81
Bohatch, 20152011–2013BrazilCohort230Academic or Tertiary HospitalCABG24.3
Brasil, 2013NA-NABrazilCross-sectional710Academic or Tertiary HospitalCHDUrban57.4 (4.1)
Breda, 20082008–2005BrazilRCT50Academic or Tertiary HospitalCABGUrban42.062.1 (12)
Chaves, 20042001–2002BrazilRCT96Academic or Tertiary HospitalCHDUrban51.065.07 (12.49)
Chaves, 20192015–2017BrazilRCT115Academic or Tertiary HospitalCABG, PCIUrban28.763.9 (10. 9), 63 (12.1)Employmnent: 40
Cruz, 20092004–2005BrazilCross-sectional103Academic or Tertiary HospitalCHD67.9 (12.3)
Dayan, 20182006–2014Uruguayretrospective282Academic or Tertiary HospitalCABG26.665.58 (9.5), 61.75 (9.6)
Feguri, 20172014–2016BrazilRCT574Academic or Tertiary HospitalCABGUrban33.062.12 (9.63), 60.93 (8.91)
Fernandez, 20112006–2007ColombiaRCT400Academic or Tertiary HospitalPCIUrban45.058.0 (9.0)
Furuya, 20142011–2012BrazilRCT60Academic or Tertiary HospitalPCIUrban43.056.9 (10.8), 34–85Employment: 35.0
Gomes, 20112002–2006BrazilCohort504Academic or Tertiary HospitalPCIUrban35.963.7 (11.0)
Hueb, 20041995–2000BrazilRCT611Academic or Tertiary HospitalCHDUrban15.060.25 (9.26), 58.92 (6.04)
Kimura, 20182007–2013BrazilCohort520Academic or Tertiary HospitalCABGUrban72.1
Liberato, 20162010–2011BrazilCross-sectional190Academic or Tertiary HospitalACSUrban36.164.9, 32–93Employment: 31.0
Nazzal, 20132008–2008ChileRegistry416Academic or Tertiary HospitalACSUrban23.4Income: 20.0
Neira, 20132011–2011ChileCross-sectional202Academic or Tertiary HospitalCHDUrban29.758.9 (9.8), 60.6 (8.5)Education: 17.4
Employment: 45.0
Nery, 20152009–2012BrazilRCT61Academic or Tertiary HospitalACSUrban27.959.5 (9.4)
Neves, 2012NA-NABrazildescriptive, cross-sectional study20Academic or Tertiary HospitalCHD0.0
Noriega, 2008NA-NAChileNon-randomized intervention64Academic or Tertiary HospitalCABG, PCI20.364.0 (11.0), 63 (12.0)
Oliveira, 20192013–2015BrazilRetrospective cohort536Academic or Tertiary HospitalACSUrban36.065.6Education: 49.2; Income: 34.0
Pantoni, 2016NA-NABrazilNon-randomized intervention27Academic or Tertiary HospitalCABGUrban44.460.0 95% CI 51–68), 63.0 (95% CI 55–70), 61.0 (95% CI 53–73)
Pellegrini, 20142002–2007BrazilCohort611Academic or Tertiary HospitalACSRural28.661.4 (11.6)
Pesaro, 20122006–2009BrazilRCT78Academic or Tertiary HospitalCHDUrban38.564.0 (12.0), 65.0 (12.0), 61.0 (12.0)
Portal, 20031998–1999BrazilRCT39Academic or Tertiary HospitalCHD43.662,7 (10.7), 61.6 (11.1)
Ribeiro, 20152007–2008BrazilCross-sectional153Academic or Tertiary HospitalPCIUrban49.061.9 (11.9)
Ribeiro, 20182014–2016BrazilCohort169Academic or Tertiary HospitalUrban16.063.7 (9.6)
Rossi, 20142006–2006ArgentinaCohort125Academic or Tertiary HospitalACSUrban34.456.0 (9.0), 60.0 (9.0)
Rueda-Clausen, 20102005–2006ColombiaCross-sectional34Academic or Tertiary HospitalCHDUrban23.564.0, 61.0
Saffi, 20132008–2010BrazilRCT74Academic or Tertiary HospitalCHD26.060.9(10.6), 63.4 (8.56), 59.9(11.8), 62.7(10.9)Income: 58.0
Santos, 20152007–2010BrazilCohort198Academic or Tertiary HospitalPCI30.355.0 (8.0), 52.0 (7.0), 54.0 (10.0)
Scherr, 20101997–2002BrazilNon-randomized intervention2337Academic or Tertiary HospitalCHDUrban39.264.3 (10.7), 64.5 (10.9)
Silva, 20051995–1998BrazilRCT210Academic or Tertiary HospitalCHDUrban32.460.2 (10), 28–87
Silveira, 20072002–2003BrazilRCT24Academic or Tertiary HospitalCABG37.558.5 (9.4)
Silveira, 20081998–2005BrazilCohort310Academic or Tertiary HospitalCHDUnclear39.0
Simon, 20192014–2015BrazilRCT48Academic or Tertiary HospitalACS35.4
Siniawski, 20192014–2017ArgentinaCross-sectional351Academic or Tertiary HospitalACS, CABGUrban26.563.3 (12.4), 60.0 (87)
Smidt, 20092002–2007BrazilRegistry611Academic or Tertiary HospitalACS36.660.9 (10.3), 31–81
Souza Groia Veloso, 2020NA-NABrazil, SurinameCross-sectional148Academic or Tertiary HospitalCHDUnclear29.7Median 61.0 (IQR 54–68)
Souza, 20132008–2010BrazilRegistry103Academic or Tertiary HospitalACSUrban16.562.6 (9.3), 63.3 (11.3)
Uchoa, 2015NA-NABrazilCohort67Academic or Tertiary HospitalCHD, CABGUrban25.061.2 (10.0), 68.6 (9.0)
Vilar, 20152009–2010BrazilCross-sectional155Academic or Tertiary HospitalCHD18.760.0 (9.0)
Villacorta, 20122006–2008BrazilCohort209Academic or Tertiary HospitalPCIUrban26Median 62.0 [IQR 17.0]
Abreu-Silva, 20112008–2010BrazilRegistry535OtherPCI32.067.0 (10.4)
Alvarez, 20161993–2013ArgentinaCross-sectional866OtherACS24.062.7 (11.1)
Berwanger, 2013NA-NABrazilCross-sectional681OtherACS
Fernandez, 20092003–2006ColombiaCohort395OtherCHD32.764.4 (12.9), 66.8 (10.9)
Finimundi, 2007NA-NABrazilRCT40OtherACSUrban43.060.1 (2.2), 63.21 (2.21)
Gaedke, 2015NA-NABrazilCohort138OtherACSUrban44.462.5 (11.1)Education: 54.8
Income: 33.3
Gowdak, 20071998–2004BrazilCohort119OtherCHDUrban57.4 (5.9), 58.3 (8.6)
Mattos, 20122010–2011BrazilRegistry2475OtherACS32.264 (8.0), 65 (9.0), 66 (8.0)
Mendis, 20052002–2003BrazilCross-sectional836OtherCHDBoth56.0 (10.0)
Vazquez, 20112008–2009UruguayCohort154OtherACS21.4
Vesga, 2006NA-NAColombiaCross-sectional71OtherCHDUrban28.258.4 (7.9)
Avezum, 20172003–2009Argentina, Brazil, Chile, ColombiaCross-sectional910Primary Care/CommunityCHDUrban and rural61.362.20 (11.60)
Vianna, 20122008–2008BrazilCross-sectional295Primary Care/CommunityACSUrban
Birck, 20192008–2010BrazilCross-sectional405Primary Care/CommunityCHDUrban36.561.6 (9.4)Education: 48.6, Income: 38.3
Stockins, 20112005–2006ChileCohort233Publi HospitalACS30.668.0
Aguiar, 20101999–2007BrazilCohort377Public HospitalACS37.962.3 (9.3)
Carvalho, 20071992–2000BrazilRetrospective cohort381Rehabilitation19.4
Gambogi, 20092004–2006UruguayCohort900RehabilitationBoth25.357.9 (9.9), 61.3 (7.7)Education: 9.5
Employment: 44.6
Garlet, 20172015–2016BrazilCross-sectional102RehabilitationCHD31.461.7 (10.0), 64.5 (9.0)
Lelys, 20192015–2017BrazilCross-sectional115RehabilitationCHD28.759.9(8.6); 57.2 (9.0)Employment: 40.0
Pantoni, 20142006–2008BrazilNon-randomized intervention28RehabilitationCABGUrban32.156.0
Fuchs, 20092005–2006BrazilCross-sectional39RehbilitationCHDUrban10.363.7(95% CI 56.6–73.9)
Castro, 20182018–NABrazilCohort525Secondary HospitalACSUrban39.861.6 (11.9)
Trivi, 20182010–2011ArgentinaCohort438Secondary HospitalACS24.259.2 (7.9)

[i] 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).

Figure 2

Risk of bias results.

Figure 3

Pooled prevalence of anti-hypertensive medication use.

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.

VARIABLENUMBER OF STUDIESPOOLED PREVALENCE
Beta-blockers5373.4 (66.8–79.1)
ACE inhibitors4455.8 (49.7–61.8)
Aspirin4485.1 (79.7–89.3)
Aspirin, clopidogrel or antiplatelet drugs5184.6 (79.6–88.5)
Statins5078.9 (71.2–84.9)
Insulin911.6 (7.0–18.8)
Antihypertensives (without specification)846.5 (33.7–59.8)
Diuretics830.1 (24.3–36.6)
Calcium chanel blockers634.0 (19.4–52.5)
Nitrates636.7 (24.1–51.5)
Antiplatelet (without specification)1475.1 (55.5–87.9)
Clopidogrel1350.0 (22.9–78.1)
Dual antiplatelet therapy380.0 (55.3–92.8)
Lipid-lowering drugs (without specification)234.4 (9,1–73.4)
High-intensity statins224.1 (6.4-59.8)
Fibrates273.1 (69.5–76.5)
Figure 5

Pooled prevalence of statins.

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.

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.

VARIABLEBETA-BLOCKERSACEI ARBSTATINANTIPLATELET DRUGS (OVERALL)ASPIRIN
Intercept2.56 (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) *
Sex1.01 (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/community0.18 (0.04–0.96) *0.11 (0.02–0.62)*0.12 (0.03–0.40)*0.19 (0.04–0.96) *
    Public centre0.35 (0.07–1.69)0.28 (0.09–0.86)*
    Cardiac rehabilitation0.92 (0.30–2.78)1.87 (0.53–6.61)0.38 (0.14 – 1.04)0.07 (0.02–0.33) *
    Other0.71 (0.27–1.88)0.67 (0.27–1.64)0.73 (0.37–1.44)1.03 (0.36–2.92)
Diagnosis
ACS1.41 (0.78–2.54)1.65 (0.90–3.03)1.73 (0.89–3.37)
PCI0.70 (0.21–2.28)0.92 (0.30 – 2.82)0.94 (0.29–3.08)
CABG1.21 (0.43–3.37)0.85 (0.27 – 2.65)
CABG, PCI0.58 (0.11–2.97)1.26 (0.38 –4.21)1.31 (0.37–4.64)
DOI: https://doi.org/10.5334/gh.1124 | Journal eISSN: 2211-8179
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Language: English
Submitted on: Oct 8, 2021
Accepted on: May 9, 2022
Published on: Jun 8, 2022
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Coronary heart disease,
secondary prevention,
South America,
time-trends,
meta-analysis

© 2022 A. Marzà-Florensa, E. Drotos, P. Gulayin, D. E. Grobbee, V. Irazola, K. Klipstein-Grobusch, I. Vaartjes, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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