| Literature DB >> 34970913 |
Hermann Yao1, Arnaud Ekou1, Thierry Niamkey1, Sandra Hounhoui Gan1, Isabelle Kouamé1, Yaovi Afassinou2, Esther Ehouman1, Camille Touré1, Marianne Zeller3, Yves Cottin4, Roland N'Guetta1.
Abstract
Background Data in the literature on acute coronary syndrome in sub-Saharan Africa are scarce. Methods and Results We conducted a systematic review of the MEDLINE (PubMed) database of observational studies of acute coronary syndrome in sub-Saharan Africa from January 1, 2010 to June 30, 2020. Acute coronary syndrome was defined according to current definitions. Abstracts and then the full texts of the selected articles were independently screened by 2 blinded investigators. This systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses standards. We identified 784 articles with our research strategy, and 27 were taken into account for the final analysis. Ten studies report a prevalence of acute coronary syndrome among patients admitted for cardiovascular disease ranging from 0.21% to 22.3%. Patients were younger, with a minimum age of 52 years in South Africa and Djibouti. There was a significant male predominance. Hypertension was the main risk factor (50%-55% of cases). Time to admission tended to be long, with the longest times in Tanzania (6.6 days) and Burkina Faso (4.3 days). Very few patients were admitted by medicalized transport, particularly in Côte d'Ivoire (only 34% including 8% by emergency medical service). The clinical presentation is dominated by ST-elevation sudden cardiac arrest. Percutaneous coronary intervention is not widely available but was performed in South Africa, Kenya, Côte d'Ivoire, Sudan, and Mauritania. Fibrinolysis was the most accessible means of revascularization, with streptokinase as the molecule of choice. Hospital mortality was highly variable between 1.2% and 24.5% depending on the study populations and the revascularization procedures performed. Mortality at follow-up varied from 7.8% to 43.3%. Some studies identified factors predictive of mortality. Conclusions The significant disparities in our results underscore the need for a multicenter registry for acute coronary syndrome in sub-Saharan Africa in order to develop consensus-based strategies, propose and evaluate tailored interventions, and identify prognostic factors.Entities:
Keywords: acute coronary syndrome; acute myocardial infarction; sub‐Saharan Africa
Mesh:
Year: 2021 PMID: 34970913 PMCID: PMC9075216 DOI: 10.1161/JAHA.120.021107
Source DB: PubMed Journal: J Am Heart Assoc ISSN: 2047-9980 Impact factor: 6.106
Figure 1Flow chart of the study.
CVD indicates cardiovascular disease; and NCD, noncommunicable diseases.
Studies on ACS in Sub‐Saharan Africa Available on MEDLINE Based on Our Research Strategy (2010–2020)
| Author (y) | Country | Design | Population | Prevalence (%) | Age (y) | Male sex (%) | Time from symptoms to admission | Fibrinolysis/angioplasty | In‐hospital mortality (%) | Follow‐up mortality (follow‐up time) |
|---|---|---|---|---|---|---|---|---|---|---|
| Hertz (2020) | Tanzania | Prospective | 152 ACS | 22.3 | 61.2 | 59.9 | 6.6 h | N/N | … | 43.3% (30 d) |
| Ekou (2020) | Côte d’Ivoire | Cross‐sectional | 166 STEMI | … | 54.5 | 91.6 | 20 h | N/Y | 1.2 | … |
| Desta (2020) | Ethiopia | Retrospective | 151 ACS | … | 59.1 | 72.2 | 95.85 h | Y/Y | 24.5 | … |
| Ba (2019) | Mauritania | Cross‐sectional | 80 ACS | 10.2 | 62.4 | 70.0 | 34.8 h | Y/Y | 3.8 | … |
| Yao (2019) | Côte d’Ivoire | Cross‐sectional | 256 ACS | … | 53.2 | 85.9 | … | N/Y | … | … |
| Yao (2019) | Côte d’Ivoire | Prospective | 329 STEMI | … | 57 | 82.6 | 20 h | Y/Y | 14.3 | 10.4% (39 mo) |
| Kaboré (2019) | Burkina‐Faso | Prospective | 111 ACS | 4.2 | 57.6 | 77.5 | 30.55 h | Y/N | 8.1 | 16.2% (30 d) |
| Varwani (2019) | Kenya | Retrospective | 230 ACS | 60.5 | 81.7 | Y/Y | 7.8 |
7.8% (30 d) 13.9% (1 y) | ||
| Pessinaba (2018) | Togo | Cross‐sectional | 67 ACS | 3.5 | 60 | 65.7 | 81.9 h | Y/N | 10.5 | … |
| Bahiru (2018) | Kenya | Retrospective | 196 ACS | 57.5 | 65 | Y/Y | 17 | |||
| N’Guetta (2018) | Côte d’Ivoire | Prospective | 165 ACS | … | 55.6 | 93 | … | N/Y | 1.2 | 1.2% (1 y) |
| Chetty (2016) | South Africa | Cross‐sectional | 122 MI | … | 61 | 65 | … | Y/N | 1.8 (STEMI) | … |
| Kimeu (2016) | Kenya | Retrospective | 64 MI | … | 56.7 | 87.5 | Y/Y | 9.4 | ||
| N’Guetta (2016) | Côte d’Ivoire | Cross‐sectional | 425 ACS | 13.5 | 55.4 | 44.7 h | Y/Y | 10 | … | |
| Mirghani (2015) | Soudan | Cross‐sectional | 197 ACS | … | 56.8 | 56.8 | … | Y/Y | 6.6 | … |
| Meel (2015) | South Africa | Prospective | 100 STEMI | … | 52 | 70 | 2.3 h | Y/N | … | … |
| Mboup (2014) | Senegal | Prospective | 59 ACS | 4.05 | 57.1 | 79.7 | 53.2 h | Y/N | 15.25 | 18.6% (30 d) |
| Giday (2013) | Ethiopia | Retrospective | 21 ACS | … | 57.1 | 65.2 | … | N/N | 14.4 | … |
| Moses (2013) | South Africa | Retrospective | 76 NSTEMI | … | 60.5 | 21.5 | 24.21 h | N/Y | 15.8 | … |
| Kolo (2013) | Nigeria | Retrospective | 14 MI | 0.21 | 56 | 92 | … | N/N | 21.44 | |
| Yameogo (2012) | Burkina‐Faso | Cross‐sectional | 43 STEMI | 56.5 | 88 | 4.3 d | Y/N | 11.6 | … | |
| Maurin (2012) | Djibouti | Prospective | 35 STEMI | 40 | 52 | 88.6 | 23 h | Y/N | 20% |
14% (30 d) 20% (1 y) |
| Shavadia (2012) | Kenya | Cross‐sectional | 111 ACS | 5.1 | 63.3 | 75.7 | 12.9 h (STEMI) | Y/Y | 8.1 | … |
| Maharaj (2012) | South Africa | Retrospective | 161 STEMI | … | 54 | … | 3.2 h | Y/N | … | … |
| Schamroth (2012) | South Africa | Prospective | 615 ACS | … | 58 | … | 3.6 h (STEMI) | Y/Y | … |
2.4% STEMI (30 d) 1.7% NSTEMI (30 d) 5.7% (1 y) |
| Ogeng’o (2010) | Kenya | Retrospective | 120 MI | … | 56.8 | 66 | … | N/Y | 5% | … |
| Bèye (2011) | Mali | Cross‐sectional | 8 MI | 7.3 | 54.62 | 100 | … | N/N | 28.6% | … |
ACS indicates acute coronary syndrome; MI, myocardial infarction; N, no; NSTEMI, non–ST‐segment–elevation myocardial infarction; STEMI, ST‐segment–elevation myocardial infarction; and Y, yes.
Studies on ACS in Côte d’Ivoire (2010–2020)
| Author (y) | Country | Design | Population | Prevalence (%) | Age (y) | Male sex (%) | Time from symptoms to admission | Fibrinolysis/angioplasty | In‐hospital mortality (%) | Follow‐up mortality (follow‐up time) |
|---|---|---|---|---|---|---|---|---|---|---|
| Ekou (2020) | Côte d’Ivoire | Cross‐sectional | 166 STEMI | … | 54.5 | 91.6 | 20 h | N/Y | 1.2 | … |
| Yao (2019) | Côte d’Ivoire | Cross‐sectional | 256 ACS | … | 53.2 | 85.9 | … | N/Y | … | … |
| Yao (2019) | Côte d’Ivoire | Prospective | 329 STEMI | … | 57 | 82.6 | 20 h | Y/Y | 14.3 | 10.4% (39 mo) |
| N’Guetta (2018) | Côte d’Ivoire | Prospective | 165 ACS | … | 55.6 | 93 | … | N/Y | 1.2 | 1.2% (1 y) |
| N’Guetta (2016) | Côte d’Ivoire | Cross‐sectional | 425 ACS | 13.5 | 55.4 | 44.7 h | Y/Y | 10 | … |
ACS indicates acute coronary syndrome; N, no; STEMI, ST‐segment–elevation myocardial infarction; and Y, yes.
Studies on ACS in South Africa (2010–2020)
| Author (y) | Country | Design | Population | Prevalence (%) | Age (y) | Male sex (%) | Time from symptoms to admission | Fibrinolysis/angioplasty | In‐hospital mortality (%) | Follow‐up mortality (follow‐up time) |
|---|---|---|---|---|---|---|---|---|---|---|
| Chetty (2016) | South Africa | Cross‐sectional | 122 MI | … | 61 | 65 | … | Y/N | 1.8 (STEMI) | … |
| Meel (2015) | South Africa | Prospective | 100 STEMI | … | 52 | 70 | 2.3 h | Y/N | … | … |
| Moses (2013) | South Africa | Retrospective | 76 NSTEMI | … | 60.5 | 21.5 | 24.21 h | N/Y | 15.8 | … |
| Maharaj (2012) | South Africa | Retrospective | 161 STEMI | … | 54 | … | 3.2 h | Y/N | … | … |
| Schamroth (2012) | South Africa | Prospective | 615 ACS | … | 58 | … | 3.6 h (STEMI) | Y/Y | … |
2.4% STEMI (30 d) 1.7% NSTEMI (30 d) 5.7% (1 y) |
ACS indicates acute coronary syndrome; MI, myocardial infarction; N, no; NSTEMI, non–ST‐segment–elevation myocardial infarction; STEMI, ST‐segment–elevation myocardial infarction; and Y, yes.
Studies on ACS in Kenya (2010–2020)
| Author (y) | Country | Design | Population | Prevalence (%) | Age (y) | Male sex (%) | Time from symptoms to admission | Fibrinolysis/angioplasty | In‐hospital mortality (%) | Follow‐up mortality (follow‐up time) |
|---|---|---|---|---|---|---|---|---|---|---|
| Varwani (2019) | Kenya | Retrospective | 230 ACS | 60.5 | 81.7 | Y/Y | 7.8 |
7.8% (30 d) 13.9% (1 y) | ||
| Bahiru (2018) | Kenya | Retrospective | 196 ACS | 57.5 | 65 | Y/Y | 17 | |||
| Kimeu (2016) | Kenya | Retrospective | 64 MI | … | 56.7 | 87.5 | Y/Y | 9.4 | ||
| Shavadia (2012) | Kenya | Cross‐sectional | 111 ACS | 5.1 | 63.3 | 75.7 | 12.9 h (STEMI) | Y/Y | 8.1 | … |
| Ogeng’o (2010) | Kenya | Retrospective | 120 MI | … | 56.8 | 66 | … | N/Y | 5% | … |
ACS indicates acute coronary syndrome; MI, myocardial infarction; N, no; STEMI, ST‐segment–elevation myocardial infarction; and Y, yes.
Figure 2Sites and number of studies on ACS in sub‐Saharan Africa available on MEDLINE 2010 to 2020.
ACS indicates acute coronary syndrome.
Figure 3Quality bias assessment according to Loney's criteria.
Figure 4Distribution of cardiovascular risk factors in patients with acute coronary syndrome.
Data derived from 3 studies: N’Guetta et al, Shavadia et al, and Schamroth et al.