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Prevalent diabetes mellitus in heart failure patients and
disease determinants in diabetic sub-Saharan Africans with
heart failure: a protocol for a systematic review and metaanalysis
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Journal:
Manuscript ID
Article Type:
Date Submitted by the Author:
bmjopen-2015-010097
Protocol
25-Sep-2015
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Complete List of Authors:
BMJ Open
<b>Primary Subject
Heading</b>:
Cardiovascular medicine, Diabetes and endocrinology, Epidemiology
Diabetes mellitus, Heart failure < CARDIOLOGY, Prevalence, sub-Saharan
Africa
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Keywords:
Cardiovascular medicine
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Secondary Subject Heading:
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Aminde, Leopold; Clinical Research Education, Networking & Consultancy
(CRENC), Douala,
Dzudie, Anastase; Clinical Research Education, Networking & Consultancy
(CRENC), Douala, Cameroon; Department of Internal Medicine, Faculty of
Health Sciences, University of Buea, Buea, Cameroon and Department of
Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town,
South Africa
Kengne, Andre; Clinical Research Education, Networking & Consultancy
(CRENC), Douala, Cameroon; Non-communicable Diseases Research Unit,
South African Medical Research Council and Department of Medicine,
Faculty of Health Sciences, University of Cape Town, Cape Town, South
Africa
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Prevalent diabetes mellitus in heart failure patients and disease determinants in
diabetic sub-Saharan Africans with heart failure: a protocol for a systematic
review and meta-analysis
Leopold Ndemnge Aminde1, Anastase Dzudie1,2,3, Andre Pascal Kengne1,3,4
1
Clinical Research Education, Networking and Consultancy (CRENC), Douala, Cameroon;
Department of Internal Medicine, General Hospital Douala and Faculty of health Sciences,
University of Buea, Buea, Cameroon; 3Department of Medicine, Faculty of Health Sciences,
University of Cape Town, Cape Town, South Africa; 4Non-communicable Disease Research
Unit, South African Medical Research Council, Cape Town, South Africa.
2
Corresponding Author: Dr Leopold N. AMINDE, Clinical Research Education, Networking &
Consultancy (CRENC), Douala, B.P. 3480 – Douala, Cameroon. Email: amindeln@gmail.com,
Tel: 00 237 674625384.
Abstract
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Introduction: Heart failure (HF) is the final common pathway for most CVDs. Diabetes mellitus
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(DM) is a major contributor to CVD burden and an independent predictor of mortality in patients
with heart failure. However the epidemiology of DM in African patients with heart failure is less
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well described. The current proposal is for a systematic review to assess the prevalence of DM
in HF and the determinants of disease in diabetic patients with HF in SSA.
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Methods and analysis: A systematic search of published literature will be conducted for
observational studies on the prevalence of DM in HF and risk factors of HF in these patients in
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SSA. Databases including MEDLINE, Google Scholar, SCOPUS, Africa Wide Information will be
searched from January 1995 to August 2015. Screening of identified articles and data extraction
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will be conducted independently by two investigators. Risk of bias and methodological quality of
the included studies will be assessed using a Risk of Bias tool and STROBE checklist.
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Appropriate meta-analytic techniques will be used to pool prevalence estimates from studies
with similar features, overall and by major subgroups. Heterogeneity of the estimates across
studies will be assessed and quantified and publication bias investigated. This protocol is
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reported according to Preferred Reporting Items for Systematic reviews and Meta-Analysis
protocols (PRISMA-P) 2015 guidelines.
Ethics and dissemination: The proposed study will utilize published data; as such there is no
ethics requirement. The resulting manuscript will be published in a peer-reviewed journal. This
review will identify the knowledge gaps as well as inform policy makers in the region on the
contemporary burden of DM in patients with heart failure.
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Protocol registration number: This protocol was registered with the International Prospective
Register of Systematic Reviews (PROSPERO) on 22 09 2015 (registration number:
CRD42015026410).
Key words: Diabetes mellitus, heart failure, prevalence, sub-Saharan Africa
Introduction
Rationale
Sub-Saharan Africa (SSA) continues to face rapid epidemiologic transition from
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communicable diseases to chronic non-communicable diseases (NCD) owing to the
growing burden of risk factors such as high blood pressure, obesity, diabetes mellitus,
physical inactivity and unhealthy eating habits 1. NCDs are the number one cause of
death around the world
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and second leading cause of mortality in SSA 3. According to
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the 2013 Global Burden of Disease Study (GBD), cardiovascular disease (CVD)
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accounted for 38.3% of NCD deaths in SSA 4 and recent increases in global deaths due
to CVD have been attributed to population growth and ageing 5. Diabetes mellitus is a
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major contributor to CVD burden 6. Recent estimates from the International Diabetes
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Federation (IDF) suggest that global population of individuals diabetes will increase
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from 382 million in 2013 to 592 million in 2035, with the highest relative increase at
109% occurring in SSA where the number of people with diabetes will double from 19.8
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million to 41.5 million 7. Kengne and colleagues recently estimated that diabetes was
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accounting for 8.6% of total mortality in SSA in 2013 8. Several studies have shown that
the risk of developing CVD is more than twice in patients with diabetes over those
without diabetes and about 80% of the mortality in patients with diabetes occurs through
CVD 9,10.
Besides atherosclerotic CVDs, cardiac failure is a recognized CVD complication in
diabetes where it is over two times more frequent than in people without diabetes 10. Via
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several mechanisms including diabetic cardiomyopathy and coronary heart disease,
diabetes mellitus (DM) has been shown to play a significant role in the pathogenesis
and outcome of heart failure 11. Besides conventional cardiovascular risk factors leading
to the development of heart failure, individuals with diabetes are more vulnerable via the
contributing influence of diabetes-related risk factors including chronic hyperglycaemia,
insulin resistance and collagen deposition in the myocardium eventually leading to the
so called ‘diabetic cardiomyopathy’, causing abnormal left ventricular and diastolic
function
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. In an in-depth literature review on heart failure in people with diabetes, it
was suggested that the determinants of heart failure documented in other parts of the
world are similar to those in African subjects, however the contribution of diabetic
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cardiomyopathy was still somewhat discordant
. Moreover, several reports have
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shown DM to be an independent predictor of mortality in HF
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. In spite of these
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observations, the epidemiology of diabetes mellitus in patients with heart failure has
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been less well described. Hence, we propose this protocol for a systematic review and
meta-analysis to estimate the current prevalence of diabetes among individuals with
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heart failure in sub-Saharan Africa as well as the determinants of disease in those
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diabetic patients with HF. Results will provide evidence on the current burden of
diabetes in this vulnerable population and inform health authorities on major risk factors
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for which control interventions should be tailored in the region to curb this burden.
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Objectives
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To conduct a systematic review and meta-analysis to ascertain the prevalence of
diabetes mellitus among patients with heart failure as well as the determinants of
disease in diabetic sub-Saharan Africans with heart failure.
Review questions
The proposed review will strive to address the following research questions:
1) What is the prevalence of diabetes mellitus among adult sub-Saharan Africans
with heart failure as documented in studies reported between 1995 and 2015?
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2) What are the determinants of heart failure among diabetic sub-Saharan Africans
with heart failure in those studies?
Secondary research questions to be addressed to answer the objective above will
include:
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1) Among patients with heart failure across different settings in SSA, what
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proportion has got diabetes mellitus?
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2) Among diabetic patients with heart failure across SSA, what are the diabetes
specific and non-specific features associated with heart failure?
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Methods
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Eligibility criteria
Inclusion criteria
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a) Study designs: Cross-sectional, case-control and cohort studies conducted on
heart failure in SSA, with data available on prevalent diabetes and risk factors for
heart failure among patients with diabetes.
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b) Study participants: adult (age >18 years) human subjects residing in SSA,
regardless of the ethnic background.
c) The final diagnosis will be based on physician-made diagnosis or as defined by
WHO/IDF16 for DM and European society of cardiology (ESC)17 / American heart
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association (AHA)[18] / Framingham criteria19 for HF diagnosis at the time of
study (Table 1).
d) Time-period: We intend to consider all published and unpublished data done
between January 1, 1995 and August 31st, 2015 while considering changes in
definition of diabetes and heart failure over time.
e) Study settings: health facilities or community-based settings; rural or urban SSA
f) Language: All studies reported in English or French language and conducted on
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human subjects will be considered.
Exclusion criteria
a) Studies conducted among populations of African origin but residing outside
Africa.
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b) Studies lacking prevalence rates and risk factors, with absence of data to
compute them.
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c) Case series with small sample sizes (sample less than 30 participants), letters to
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editor, reviews, commentaries, editorials, and any publication with primary data.
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d) Studies in sub-groups of participants selected based on the presence of
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diabetes.
e) Duplicate publications from the same study. For studies published in more than
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one journal/conference, the most recent and comprehensive publication will be
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used.
f) Studies not performed in human subjects or published in languages other than
English and French.
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Source of information
The methods of this systematic review are in accordance to the PRISMA-P 2015
Guidelines 20. See table 2 for checklist.
Search strategy for studies identification
Electronic searches
We will search PubMed MEDLINE, Google Scholar, SCOPUS, ISI Web of Science
(Science Citation Index), Africa Wide Information, African Index Medicus (AIM) and
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AFROLIB databases from January 1, 1995 to August 31st, 2015 for published studies on
diabetes mellitus in heart failure patients in sub-Saharan Africa. This search shall be
conducted using a pre-defined comprehensive and sensitive search strategy combining
relevant terms with names of countries in SSA to obtain the maximum possible number
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of studies. This search will be guided by the African search filter which has been
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reported to have good sensitivity (and improved precision) of 74% (1.3% to 9.4%) and
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73% (5% to 28%) for Medline and EMBASE respectively
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. This search filter includes
names of each African country and shortened terms to capture studies from regions.
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Countries with official names in a language other than English will also be entered in the
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official form, and for countries that have changed names over time, both names shall be
included in the search. Table 3 depicts the main search strategy to be employed.
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Reference lists
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We will search reference lists of relevant citations for articles of interest.
Grey literature
We will contact authors and experts in the field for any relevant unpublished material.
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Study Records
Data management
All identified search results would be entered into RevMan 5 software for deduplication
of records. These shall be subsequently uploaded into Eppi-Reviewer which is an
internet based software program to facilitate collaboration between investigators during
the selection of studies to be included in the review. Prior to screening of studies,
investigators shall create standardized and pre-tested questions following the inclusion
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criteria. These questions together with abstracts and full texts of articles would be
uploaded into Eppi-Reviewer for eventual piloting of the test questions.
Screening
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Two investigators (LNA and AD) will independently select studies that meet inclusion
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criteria. Citations and abstracts will be screened for relevance, and duplicate citations
will be excluded. Titles and abstracts shall then be screened following inclusion criteria
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stipulated earlier, and the full texts of potentially eligible articles will then be obtained.
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These full texts will be screened using a standardized and pre-tested form to include
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eligible studies. Disagreements will be resolved by consensus, with consultation of a
third author (APK) when resolution cannot be achieved. Corresponding authors will be
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contacted in the event that the publication (1) is unclear and may be subject to multiple
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interpretations, or (2) has collected data but did not report data that are relevant to our
study analysis. For studies which shall be excluded, the reasons shall be documented.
A flow chat will be used to demonstrate the entire review process.
Data extraction
Two investigators (LNA and AD) will independently extract data from included studies,
using a standardized and pre-tested data extraction form. Any inconsistencies or
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disagreement shall be resolved by consensus or consultation with the third investigator
(APK).
Data items
Data will include the country where study was conducted, the year of publication, the
language of publication, demographic characteristics of participants (mean age, sex
proportions), study design, setting (rural or urban, health-facility or community-based),
sample size, number and proportion with diabetes, duration of diabetes, diagnostic
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criteria for diabetes and heart failure respectively, cardiovascular as well as diabetesspecific risk factors of patients, measures of association (chi square, odds ratios, risk
ratios, p values and confidence intervals) will be recorded.
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Assessment of methodological quality and risk of bias
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Two reviewers (LNA and AD) will independently score the quality of included studies
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using the STROBE checklist (Table 4)
to evaluate reporting methodology in each
paper while risk of bias in individual studies will be assessed using the Risk of Bias Tool
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for Prevalence Studies developed by Hoy et al (Table 5)
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, and the Cochrane
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guidelines available in Review Manager version 5.3 (http://tech.cochrane.org/revman).
Discrepancies will be resolved by consensus or by consulting the third investigator
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(APK). Inter-rater agreement on screening, data abstraction, and methodological quality
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will be assessed using Cohen’s kappa (κ) coefficient 24. We intend to present risk of bias
and quality scores in a table.
Data synthesis, analysis and assessment of heterogeneity
Data will be synthesized to answer both research questions. Prevalence data will be
summarized by country and geographic regions. A meta-analysis will be performed for
variables defined similarly across studies. Standard errors for study-specific estimates
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would be determined from the point estimates and the appropriate denominators,
assuming a binomial distribution. Further to this, the study specific estimates will be
pooled through a random-effects meta-analysis model, to obtain the overall summary
estimate of the prevalence across studies, after stabilizing the variance of individual
studies with the use of the Freeman-Tukey double arc-sine transformation 25.
Heterogeneity will be evaluated by the χ2 test on Cochrane’s Q statistic which is
quantified by I2 values
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, assuming that I2 values of 25%, 50% and 75% represent low,
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medium and high heterogeneity respectively. When statistical data pooling does not
yield meaningful results, such as in the presence of considerable clinical heterogeneity,
we will conduct a narrative synthesis.
The data will be analyzed using the statistical software R (version 3.0.3 [2014-03-04],
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The R Foundation for statistical computing, Vienna, Austria).
Sensitivity analysis
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We will perform subgroup analysis where substantial heterogeneity will be detected to
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setting (rural vs urban, health-facility vs community-based), geographical region
(central, west, east and southern Africa) and study quality. Any subgroup differences
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identified will be described, and our findings will be interpreted in the light of these
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differences.
Confidence in cumulative evidence
We intend to assess the quality of evidence of the studies included in the review to get
the confidence in cumulative estimates in the systematic review. This will be assessed
using the Grading of recommendations Assessment, Development and Evaluation
(GRADE) approach. This assessment of the quality of evidence would include; risk of
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bias, consistency and publication bias. Studies in which further research is unlikely to
change effect estimates, or likely to have a considerable impact on effect estimates, or
capable of changing the effect estimates, and those in which there is uncertainty in
effect estimates will be described as ‘high’, ‘moderate’, ‘low’ and ‘very low’ qualities
respectively.
Reporting of this review
The proposed systematic review will be reported following the Preferred Reporting
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Items for Systematic reviews and Meta-analyses (PRISMA) guidelines
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publish a PRISMA checklist alongside the final report.
Potential amendments
We do not intend to make any amendments to the protocol to avoid the possibility of
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outcome reporting bias. However, any unintended amendments will not be influenced
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by the results of studies in the review, and shall be validated by the authors of this
protocol.
Conclusion
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Cardiovascular disease continues to be a daunting problem in SSA and is projected to
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worsen in the coming decades if no action is taken. Diabetes mellitus is a major
contributor to the CVD burden and studies among Caucasians suggest it is an
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independent predictor of mortality in heart failure (the final end point for most
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cardiovascular diseases). The epidemiology and burden of diabetes in this group of
patients with heart failure has been less well documented in Africa. We intend to
describe the current prevalence of diabetes mellitus among heart failure patients and
the determinants of heart failure among diabetic patients in SSA. Determining this
current burden will be important for clinicians providing care to this vulnerable group of
patients. If diabetes is found to be common among heart failure patients in SSA like
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elsewhere, clinicians in this setting would have to be alert when managing these
patients and more especially aggressively control identified modifiable risk factors. This
would reduce the morbidity and mortality associated with CVD, as well as economic
burden in an already financially-constrained setting plagued with communicable
diseases as well. Possible limitations of this study would include; predominance of poor
quality studies, significant heterogeneity of studies precluding further analysis. In
addition, a predominance of cross-sectional studies would make it difficult to obtain or
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determine risk factors for diabetes. Finally, including only studies published in English or
French, we may lose relevant data from studies published in other languages. This
review will however identify gaps in the current literature on this topic and provide
direction for future research in people with diabetes and cardiomyopathy.
Ethics and dissemination
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The current study is based on published data hence wouldn’t require ethical approval.
The final report of this review in the form of a scientific paper will be published in a peer-
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reviewed journal. Findings will also be presented at conferences and submitted to
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relevant health and policy authorities. We also plan to update the review in the future to
monitor any progressive changes on the subject.
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Authors’ contributions
Conceived and designed protocol: AD and LNA. Manuscript drafting: LNA. Critical
revision for methodological and intellectual content: APK, AD. LNA is the guarantor of
this review. All authors read and approved the final version of the manuscript.
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Competing Interest: None
Funding: This research received no specific grant from any funding agency in the
public, commercial or not-for-profit sectors
Acknowledgement
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REFERENCES
1.
Zimmet P, Alberti KG, Shaw J. Global and societal implications of the diabetes
epidemic. Nature. 2001 Dec 13;414(6865):782–7.
2.
United States Department of Health and Human Services. Global Health Topics:
Non-communicable
diseases
[Internet].
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Available
from:
http://www.globalhealth.gov/global-health-topics/non-communicable-diseases/
3.
Kengne AP, Mayosi BM. Readiness of the primary care system for noncommunicable diseases in sub-Saharan Africa. Lancet Glob Health. 2014
May;2(5):e247–8.
4.
Mensah GA, Roth GA, Sampson UKA, Moran AE, Feigin VL, Forouzanfar MH, et
al. Mortality from cardiovascular diseases in sub-Saharan Africa, 1990-2013: a
systematic analysis of data from the Global Burden of Disease Study 2013.
Cardiovasc J Afr. 2015 Apr;26(2 Suppl 1):S6–10.
5.
Roth GA, Forouzanfar MH, Moran AE, Barber R, Nguyen G, Feigin VL, et al.
Demographic and epidemiologic drivers of global cardiovascular mortality. N Engl J
Med. 2015 Apr 2;372(14):1333–41.
6.
Global Burden of Disease Study 2013 Collaborators. Global, regional, and national
incidence, prevalence, and years lived with disability for 301 acute and chronic
diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the
Global Burden of Disease Study 2013. Lancet Lond Engl. 2015 Jun 7;
7.
Aguiree F, Brown A, Cho NH, Dahlquist G, Dodd S, Dunning T, et al. IDF Diabetes
Atlas, 6th edition. International Diabetes Federation; 2013.
8.
Kengne AP, Echouffo-Tcheugui J-B, Sobngwi E, Mbanya J-C. New insights on
diabetes mellitus and obesity in Africa-part 1: prevalence, pathogenesis and
comorbidities. Heart Br Card Soc. 2013 Jul;99(14):979–83.
9.
Stamler J, Vaccaro O, Neaton JD, Wentworth D. Diabetes, other risk factors, and
12-yr cardiovascular mortality for men screened in the Multiple Risk Factor
Intervention Trial. Diabetes Care. 1993 Feb;16(2):434–44.
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10. Woodward M, Zhang X, Barzi F, Pan W, Ueshima H, Rodgers A, et al. The effects
of diabetes on the risks of major cardiovascular diseases and death in the AsiaPacific region. Diabetes Care. 2003 Feb;26(2):360–6.
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11. Soläng L, Malmberg K, Rydén L. Diabetes mellitus and congestive heart failure.
Further knowledge needed. Eur Heart J. 1999 Jun;20(11):789–95.
12. Bell DSH. Diabetic cardiomyopathy. Diabetes Care. 2003 Oct;26(10):2949–51.
13. Kengne AP, Dzudie A, Sobngwi E. Heart failure in sub-Saharan Africa: a literature
review with emphasis on individuals with diabetes. Vasc Health Risk Manag.
2008;4(1):123–30.
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14. Aguilar D, Solomon SD, Køber L, Rouleau J-L, Skali H, McMurray JJV, et al. Newly
diagnosed and previously known diabetes mellitus and 1-year outcomes of acute
myocardial infarction: the VALsartan In Acute myocardial iNfarcTion (VALIANT)
trial. Circulation. 2004 Sep 21;110(12):1572–8.
15. Ancion A, Lancellotti P, Piérard LA. [Congestive heart failure and diabetes
mellitus]. Rev Médicale Liège. 2005 Jun;60(5-6):536–40.
16. World Health Organisation (WHO). Definition and diagnosis of diabetes mellitus
and intermediate hyperglycaemia: report of a WHO/IDF consultation. 2006.
Available
from:
https://www.idf.org/webdata/docs/WHO_IDF_definition_diagnosis_of_diabetes.pdf
17. McMurray JJV, Adamopoulos S, Anker SD, Auricchio A, Böhm M, Dickstein K, et
al. ESC guidelines for the diagnosis and treatment of acute and chronic heart
failure 2012: The Task Force for the Diagnosis and Treatment of Acute and
Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in
collaboration with the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail.
2012 Aug;14(8):803–69.
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18. Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE, Drazner MH, et al. 2013
ACCF/AHA guideline for the management of heart failure: a report of the American
College of Cardiology Foundation/American Heart Association Task Force on
Practice Guidelines. J Am Coll Cardiol. 2013 Oct 15;62(16):e147–239.
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19. McKee PA, Castelli WP, McNamara PM, Kannel WB. The natural history of
congestive heart failure: the Framingham study. N Engl J Med. 1971 Dec
23;285(26):1441–6.
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20. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred
reporting items for systematic review and meta-analysis protocols (PRISMA-P)
2015 statement. Syst Rev. 2015;4:1.
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21. Pienaar E, Grobler L, Busgeeth K, Eisinga A, Siegfried N. Developing a geographic
search filter to identify randomised controlled trials in Africa: finding the optimal
balance between sensitivity and precision. Health Inf Libr J. 2011 Sep;28(3):210–5.
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22. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et
al. The Strengthening the Reporting of Observational Studies in Epidemiology
(STROBE) statement: guidelines for reporting observational studies. Bull World
Health Organ. 2007 Nov;85(11):867–72.
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23. Hoy D, Brooks P, Woolf A, Blyth F, March L, Bain C, et al. Assessing risk of bias in
prevalence studies: modification of an existing tool and evidence of interrater
agreement. J Clin Epidemiol. 2012 Sep;65(9):934–9.
24. Landis JR, Koch GG. The measurement of observer agreement for categorical
data. Biometrics. 1977 Mar;33(1):159–74.
25. Barendregt JJ, Doi SA, Lee YY, Norman RE, Vos T. Meta-analysis of prevalence. J
Epidemiol Community Health. 2013 Nov 1;67(11):974–8.
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26. Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat
Med. 2002 Jun 15;21(11):1539–58.
27. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting
items for systematic reviews and meta-analyses: the PRISMA statement. Ann
Intern Med. 2009 Aug 18;151(4):264–9, W64.
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Table 1: Definitions of Diabetes Mellitus and Heart Failure
Disease
Definition
Diabetes mellitus (IDF/WHO) 2006
Fasting plasma glucose ≥ 7.0mmol/l (126mg/dl) or
2-hour plasma glucose ≥ 11.1mmol/l (200mg/dl)
Heart Failure definition
HF is defined, clinically, as a syndrome in which patients have typical symptoms
1) European Society of Cardiology
(e.g. breathlessness, ankle swelling, and fatigue) and signs (e.g. elevated jugular
(ESC) guidelines 2012
venous pressure, pulmonary crackles, and displaced apex beat) resulting from an
abnormality of cardiac structure or function (cardiomegaly, third heart sound,
abnormality on echocardiogram, raised natriuretic peptide concentration).
2) American Heart Association/
A complex clinical syndrome that results from any structural or functional
American College of Cardiology
impairment of ventricular filling or ejection of blood. The cardinal manifestations are
Foundation (AHA/ACCF) 2013
dyspnoea and fatigue, which may limit exercise tolerance, and fluid retention, which
may lead to pulmonary and/or splanchnic congestion and/or peripheral oedema.
A) Major Criteria: paroxysmal nocturnal dyspnoea; neck vein distension; crackles;
3) Framingham criteria for clinical
diagnosis
radiographic cardiomegaly; acute pulmonary oedema; S3 gallop; central venous
pressure > 16cmH20; circulation time 25s; hepatojugular reflux; pulmonary oedema,
visceral congestion or cardiomegaly at autopsy; weight loss – 4.5kg in 5 days in
response to treatment of congestive heart failure.
B) Minor criteria: bilateral ankle oedema; nocturnal cough; dyspnoea on ordinary
exertion; hepatomegaly; pleural effusion; decrease vital capacity by one-third from
maximal value recorded; tachycardia (>120beats/min)
N.B: The diagnosis of congestive heart failure requires that two major only or one
major and two minor criteria be present concurrently.
Minor criteria are acceptable only if they are not attributed to another medical
condition
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Table 3: Search strategy for MEDLINE, and adaptable to regional data bases
Search
Search terms
Hits
1
Heart failure [tw] OR cardiac failure [tw] OR cardiac insufficiency [tw] OR heart
disease [tw] OR cardiac
2
diabetes mellitus [tw] OR type 1 diabetes [tw] OR type 2 diabetes [tw] OR type
1 diabetes mellitus [tw] OR type 2 diabetic mellitus [tw] OR diabetes [tw] OR
diabetics [tw] diabetic cardiomyopathy [tw]
3
#1 AND #2
4
African filter
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((((Angola[tw] OR Benin[tw] OR Botswana[tw] OR "Burkina Faso"[tw] OR
Burundi[tw] OR Cameroon[tw]
OR "Cape Verde"[tw] OR "Central African
Republic"[tw] OR Chad[tw] OR Comoros[tw] OR Congo[tw] OR "Democratic
Republic of Congo"[tw] OR Djibouti[tw] OR "Equatorial Guinea"[tw] OR
Eritrea[tw] OR Ethiopia[tw] OR Gabon[tw] OR Gambia[tw] OR Ghana[tw] OR
Guinea[tw] OR "Guinea Bissau"[tw] OR "Ivory Coast"[tw] OR "Cote d'Ivoire"[tw]
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OR Kenya[tw] OR Lesotho[tw] OR Liberia[tw] OR Madagascar[tw] OR Malawi[tw]
OR Mali[tw] OR Mauritania[tw] OR Mauritius[tw] OR Mozambique[tw] OR
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Namibia[tw] OR Niger[tw] OR Nigeria[tw] OR Principe[tw] OR Reunion[tw] OR
Rwanda[tw] OR "Sao Tome"[tw] OR Senegal[tw] OR Seychelles[tw] OR "Sierra
Leone"[tw]
OR
Somalia[tw]
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OR
"South
Africa"[tw]
OR
Sudan[tw]
OR
Swaziland[tw] OR Tanzania[tw] OR Togo[tw] OR Uganda[tw] OR "Western
Sahara"[tw] OR Zambia[tw] OR Zimbabwe[tw] OR "Central Africa"[tw] OR
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"Central African"[tw] OR "West Africa"[tw] OR "West African"[tw] OR "Western
Africa"[tw] OR "Western African"[tw] OR "East Africa"[tw] OR "East African"[tw]
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OR "Eastern Africa"[tw] OR "Eastern African"[tw] OR "South African"[tw] OR
"Southern Africa"[tw] OR "Southern African"[tw] OR "sub Saharan Africa"[tw] OR
on
"sub Saharan African"[tw] OR "subSaharan Africa"[tw] OR "subSaharan
African"[tw] NOT “guinea pig” [tw] NOT “guinea pigs” [tw] NOT “aspergillus niger”
[tw] ))))
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# 3 AND # 4 Limits: 01/01/1995 to 31/08/2015 in English and French on humans
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Table 4: STROBE Statement: checklist of items that should be included in reports of
Observational studies
Item N°
Recommendation
1
(a) Indicate the study’s design with a commonly used term in
the title or the abstract
(b) Provide in the abstract an informative and balanced
summary of what was done and what was found
Background/rationale
2
Objective
3
Explain the scientific background and rationale for the
investigation being reported
State specific objectives, including any prespecified hypotheses
Title and abstract
Introduction
Methods
Study design
Setting
Participants
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Present key elements of study design early in the paper
5
Describe the setting, locations, and relevant dates, including
periods of recruitment, exposure, follow-up, and data collection
6
(a) Cohort study—Give the eligibility criteria, and the sources
and methods of selection of participants. Describe methods of
follow-up
Case-control study—Give the eligibility criteria, and the sources
and methods of case ascertainment and control selection. Give
the rationale for the choice of cases and controls
Cross-sectional study—Give the eligibility criteria, and the
sources and methods of selection of participants
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(b) Cohort study—For matched studies, give matching criteria
and number of exposed and unexposed
Case-control study—For matched studies, give matching
criteria and the number of controls per case
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Variables
7
Data sources/
measurement
8*
Bias
9
Clearly define all outcomes, exposures, predictors, potential
confounders, and effect modifiers. Give diagnostic criteria, if
applicable
For each variable of interest, give sources of data and details of
methods of assessment (measurement). Describe comparability
of assessment methods if there is more than one group
Describe any efforts to address potential sources of bias
Study size
10
Explain how the study size was arrived at
Quantitative variables
11
Statistical methods
12
Explain how quantitative variables were handled in the
analyses. If applicable, describe which groupings were chosen
and why
(a) Describe all statistical methods, including those used to
control for confounding
(b) Describe any methods used to examine subgroups and
interactions
(c) Explain how missing data were addressed
(d) Cohort study—If applicable, explain how loss to follow-up
was addressed
Case-control study—If applicable, explain how matching of
cases and controls was addressed
Cross-sectional study—If applicable, describe analytical
methods taking account of sampling strategy
(e) Describe any sensitivity analyses
Results
Participants
13*
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(a) Report numbers of individuals at each stage of study—eg
numbers potentially eligible, examined for eligibility, confirmed
eligible, included in the study, completing follow-up, and
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Descriptive data
14*
Outcome data
15*
Main results
16
Other analyses
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analysed
(b) Give reasons for non-participation at each stage
(c) Consider use of a flow diagram
(a) Give characteristics of study participants (eg demographic,
clinical, social) and information on exposures and potential
confounders
(b) Indicate number of participants with missing data for each
variable of interest
(c) Cohort study—Summarise follow-up time (eg, average and
total amount)
Cohort study—Report numbers of outcome events or summary
measures over time
Case-control study—Report numbers in each exposure
category, or summary measures of exposure
Cross-sectional study—Report numbers of outcome events or
summary measures
(a) Give unadjusted estimates and, if applicable, confounder
adjusted estimates and their precision (eg, 95% confidence
interval). Make clear which confounders were adjusted for and
why they were included
(b) Report category boundaries when continuous variables were
categorized
(c) If relevant, consider translating estimates of relative risk into
absolute risk for a meaningful time period
Report other analyses done—eg analyses of subgroups and
interactions, and sensitivity analyses
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Limitations
19
Interpretation
20
Generalizability
21
Other information
Funding
22
Summarise key results with reference to study objectives
Discuss limitations of the study, taking into account sources of
potential bias or imprecision. Discuss both direction and
magnitude of any potential bias
Give a cautious overall interpretation of results considering
objectives, limitations, multiplicity of analyses, results from
similar studies, and other relevant evidence
Discuss the generalizability (external validity) of the study
results
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Discussion
Key results
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Give the source of funding and the role of the funders for the
present study and, if applicable, for the original study on which
the present article is based
*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and
unexposed groups in cohort and cross-sectional studies.
Note: An Explanation and Elaboration article discusses each checklist item and gives methodological
background and published examples of transparent reporting. The STROBE
checklist is best used in conjunction with this article (freely available on the Web sites of
PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and
Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe
statement.org.
Quality assessment score
A quality assessment score out of 22 will be determined for each study by assigning a point
per STROBE item addressed. Good/fair quality papers will be categorized as having a score of ≥14/22 and poor
quality papers will be classified as having a score of <14/22.
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Table 5: Risk of bias assessment tool (Adapted from the Risk of Bias Tool for Prevalence
Studies developed by Hoy et al. (2012))
Risk of bias Item
Response:
Yes (Low Risk) or No (High risk)
External Validity
1. Was the study target population a close representation of the
national population in relation to relevant variables?
2. Was the sampling frame a true or close representation of the target
population?
3. Was some form of random selection used to select the sample,
OR, was a census undertaken?
4. Was the likelihood of non-participation bias minimal?
Internal Validity
5. Were data collected directly from the subjects (as opposed to
medical records)?
6. Were acceptable case definition of diabetes and heart failure
used?
7. Were reliable and accepted diagnostic methods for diabetes and
heart failure utilized?
8. Was the same mode of data collection used for all subjects?
9. Was the length of the shortest prevalence period for the parameter
of interest appropriate?
10. Were the numerator(s) and denominator(s) for the calculation of
the prevalence of diabetes appropriate?
11. Summary item on the overall risk of study bias
Low Risk of Bias: 8 or more “yes” answers.
Further research is very unlikely to change our confidence in the
estimate.
Moderate Risk of Bias: 6 to 7 “yes” answers.
Further research is likely to have an important impact on our
confidence in the estimate and may change the estimate.
High Risk of Bias: 5 or fewer “yes” answers.
Further research is very likely to have an important impact on our
confidence in the estimate and is likely to change the estimate.
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PRISMA-P 2015 checklist
Section/topic
Item
No.
Title
Identification
Update
1a
1b
Registration
2
Authors
Contact
3a
Checklist item
Status
Identify the report as a protocol of a systematic review
If the protocol is for an update of a previous systematic review, identify
as such
If registered, provide the name of the registry (e.g. PROSPERO) and
registration number
Done (page 1)
NAP
Provide name, institutional affiliation, and e-mail address of all protocol
authors, provide physical mailing address of corresponding author
Describe contributions of protocol authors and identify the guarantor of
the review
If the protocol represents an amendment of a previously completed or
published protocol, identify as such and list changes; otherwise, state
plan for documenting important protocol amendments
Done
(page 1)
Done
(page 11)
Done
(page 10)
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Contributions
3b
Amendments
4
Support
Sources
5a
Indicate sources of financial or other support
Done
(page 2)
Done (page 11)
NAP
NAP
Sponsor
Role of
sponsor/funder
INTRODUCTION
Rationale
5b
5c
Provide name of the review funder and/or sponsor
Describe role(s) of funder(s), sponsor(s), and/or institution(s), if any, in
developing the protocol
6
Done (page 2)
Objectives
7
Describe the rationale for the review in the context of what is already
known
Provide an explicit statement of the question(s) the review will address
with reference to participants, interventions, comparators, and
outcomes (PICO)
METHODS
Eligibility criteria
8
Specify the study characteristics (e.g. PICO, study design, setting, time
frame) and report characteristics (e.g. years considered, language,
publication status) to be used as criteria of eligibility for the review
Describe all intended information sources (e.g. electronic databases
contact with study authors, trial registers, or other grey literature
sources) with planned dates of coverage
Present draft of search strategy to be used for at least one electronic
database, including planned limits, such that it could be repeated
Done (pages 4 &
5)
Selection process
11b
Data collection
process
11c
Data items
12
Outcomes and
prioritization
Risk of bias in
individual studies
13
Data Synthesis
15a
14
15b
Describe the mechanism(s) that will be used to manage data throughout
the review
State the process that will be used for selecting studies (e.g. two
independent reviewers) through each phase of the review (i.e.
screening, eligibility, and inclusion in meta-analysis)
Describe planned method of extracting data from reports (e.g. piloting
forms, done independently, in duplicate), any process for obtaining and
confirming data from investigators
List and define all variables for which data will be sought (e.g. PICO
items, funding sources), any pre-planned data assumptions and
simplifications
List and define all outcomes for which data will be sought, including
prioritization of main and additional outcomes, with rationale
Describe anticipated methods for assessing risk of bias of individual
studies, including whether this will be done at the outcome or study
level, or both; state how this information will be used in data synthesis
Describe criteria under which study data will be quantitatively
synthesized
If data are appropriate for quantitative synthesis, describe planned
summary measures, methods of handling data, and methods of
combining data from studies, including any planned exploration of
2
consistency (e.g. I , kendall’s tau)
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Data management
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15c
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Meta-bias(es)
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Confidence in
cumulative
evidence
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Describe any proposed additional analysis (e.g. sensitivity or sub group
analysis, meta-regression)
If quantitative synthesis is not appropriate, describe the type of
summary planned
Specify if any planned assessment of meta-bias(es) (e.g. publication
bias across studies, selective reporting within studies)
Describe how the strength of the body of evidence will be assessed
(e.g. GRADE)
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Prevalent diabetes mellitus in heart failure patients and
disease determinants in diabetic sub-Saharan Africans with
heart failure: a protocol for a systematic review and metaanalysis
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Journal:
Manuscript ID
Article Type:
Date Submitted by the Author:
bmjopen-2015-010097.R1
Protocol
19-Jan-2016
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Complete List of Authors:
BMJ Open
<b>Primary Subject
Heading</b>:
Cardiovascular medicine, Diabetes and endocrinology, Epidemiology
Diabetes mellitus, Heart failure < CARDIOLOGY, Prevalence, sub-Saharan
Africa
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Keywords:
Cardiovascular medicine
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Secondary Subject Heading:
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Aminde, Leopold; Clinical Research Education, Networking & Consultancy
(CRENC), Douala,
Dzudie, Anastase; Clinical Research Education, Networking & Consultancy
(CRENC), Douala, Cameroon; Department of Internal Medicine, Faculty of
Health Sciences, University of Buea, Buea, Cameroon and Department of
Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town,
South Africa
Kengne, Andre; Clinical Research Education, Networking & Consultancy
(CRENC), Douala, Cameroon; Non-communicable Diseases Research Unit,
South African Medical Research Council and Department of Medicine,
Faculty of Health Sciences, University of Cape Town, Cape Town, South
Africa
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Page 1 of 22
Prevalent diabetes mellitus in heart failure patients and disease determinants in
diabetic sub-Saharan Africans with heart failure: a protocol for a systematic
review and meta-analysis
Leopold Ndemnge Aminde1 MD, Anastase Dzudie1,2,3 MD; PhD; FESC, Andre Pascal
Kengne1,3,4 MD; PhD
1
Clinical Research Education, Networking and Consultancy (CRENC), Douala, Cameroon;
Department of Internal Medicine, General Hospital Douala and Faculty of health Sciences,
University of Buea, Buea, Cameroon; 3Department of Medicine, Faculty of Health Sciences,
University of Cape Town, Cape Town, South Africa; 4Non-communicable Disease Research
Unit, South African Medical Research Council, Cape Town, South Africa.
2
Corresponding Author: Dr Leopold N. AMINDE, Clinical Research Education, Networking &
Consultancy (CRENC), Douala, B.P. 3480 – Douala, Cameroon. Email: amindeln@gmail.com,
Tel: 00 237 674625384.
Abstract
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Introduction: Heart failure (HF) is the final common pathway for most CVDs. Diabetes mellitus
(DM) is a major contributor to CVD burden and an independent predictor of mortality in patients
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with heart failure. However the epidemiology of DM in African patients with heart failure is less
well described. The current proposal is for a systematic review to assess the prevalence of DM
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in HF and the determinants of disease in diabetic patients with HF in SSA.
Methods and analysis: A systematic search of published literature will be conducted for
observational studies on the prevalence of DM in HF and risk factors of HF in these patients in
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SSA. Databases including MEDLINE, Google Scholar, SCOPUS, Africa Wide Information will be
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searched from January 1995 to August 2015. Screening of identified articles and data extraction
will be conducted independently by two investigators. Risk of bias and methodological quality of
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the included studies will be assessed using a Risk of Bias tool and STROBE checklist.
Appropriate meta-analytic techniques will be used to pool prevalence estimates from studies
with similar features, overall and by major subgroups. Heterogeneity of the estimates across
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studies will be assessed and quantified and publication bias investigated. This protocol is
reported according to Preferred Reporting Items for Systematic reviews and Meta-Analysis
protocols (PRISMA-P) 2015 guidelines.
Ethics and dissemination: The proposed study will utilize published data; as such there is no
requirement for ethical approval. The resulting manuscript will be published in a peer-reviewed
journal. This review will identify the knowledge gaps as well as inform policy makers in the
region on the contemporary burden of DM in patients with heart failure.
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Protocol registration number: This protocol was registered with the International Prospective
Register of Systematic Reviews (PROSPERO) on 22 09 2015 (registration number:
CRD42015026410).
Key words: Diabetes mellitus, heart failure, prevalence, sub-Saharan Africa
Introduction
Rationale
Sub-Saharan Africa (SSA) continues to face rapid epidemiologic transition from
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communicable diseases to chronic non-communicable diseases (NCD) owing to the
growing burden of risk factors such as high blood pressure, obesity, diabetes mellitus,
physical inactivity and unhealthy eating habits 1. NCDs are the number one cause of
death around the world
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and second leading cause of mortality in SSA accounting for
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30% of the 9.5 million deaths in 2011.3 According to the 2013 Global Burden of Disease
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Study (GBD), cardiovascular disease (CVD) accounted for 38.3% of NCD deaths in
SSA
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and recent increases in global deaths due to CVD have been attributed to
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population growth and ageing 5. Bloomfield and coworkers, in a recent comprehensive
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review on aetiologies, epidemiology and clinical characteristics of heart failure in SSA
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highlighted that this syndrome was largely due to non-ischaemic causes majoring
hypertensive heart disease, rheumatic heart disease and the cardiomyopathies6. This
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was similarly observed in THESUS-HF, the first heart failure registry on the continent7. It
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was however suggested that though atherosclerotic heart disease (to which diabetes is
a major contributor) was apparently rare, these conclusions were based on just a few
studies and hence its contribution can’t be totally ruled out6.
Diabetes mellitus is a
major contributor to CVD burden 8. Recent estimates from the International Diabetes
Federation (IDF) suggest that global population of individuals with diabetes will increase
from 382 million in 2013 to 592 million in 2035, with the highest relative increase at
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109% occurring in SSA where it is estimated that the number of people with diabetes
will double from 19.8 million to 41.5 million 9. Kengne and colleagues recently estimated
that diabetes was accounting for 8.6% of total mortality in SSA in 2013
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. Several
studies have shown that the risk of developing CVD is more than twice in patients with
diabetes over those without diabetes and about 80% of the mortality in patients with
diabetes occurs through CVD 11,12.
Besides atherosclerotic CVDs, cardiac failure is a recognized CVD complication in
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diabetes where it is over two times more frequent than in people without diabetes 12. Via
several mechanisms including diabetic cardiomyopathy and coronary heart disease,
diabetes mellitus (DM) has been shown to play a significant role in the pathogenesis
and outcome of heart failure 13. Besides conventional cardiovascular risk factors leading
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to the development of heart failure, individuals with diabetes are more vulnerable via the
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contributing influence of diabetes-related risk factors including chronic hyperglycaemia,
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insulin resistance and collagen deposition in the myocardium eventually leading to the
so called ‘diabetic cardiomyopathy’, causing abnormal left ventricular and diastolic
function
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. In an in-depth literature review on heart failure in people with diabetes, it
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was suggested that the determinants of heart failure documented in other parts of the
world are similar to those in African subjects, however the contribution of diabetic
cardiomyopathy was still somewhat discordant
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. Moreover, several reports have
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shown DM to be an independent predictor of mortality in HF
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. In spite of these
observations, the epidemiology of diabetes mellitus in patients with heart failure has
been less well described. Hence, we propose this protocol for a systematic review and
meta-analysis to estimate the current prevalence of diabetes among individuals with
heart failure in sub-Saharan Africa as well as the determinants of disease in those
diabetic patients with HF. Results will provide evidence on the current burden of
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diabetes in this vulnerable population and inform health authorities on major risk factors
for which control interventions should be tailored in the region to curb this burden.
Objectives
To conduct a systematic review and meta-analysis to ascertain the prevalence of
diabetes mellitus among patients with heart failure as well as the determinants of
disease in diabetic sub-Saharan Africans with heart failure.
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Review questions
The proposed review will strive to address the following research questions:
1) What is the prevalence of diabetes mellitus among adult sub-Saharan Africans
with heart failure as documented in studies reported between 1995 and 2015?
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2) What are the determinants of heart failure among diabetic sub-Saharan Africans
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with heart failure in those studies?
Methods
Eligibility criteria
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Inclusion criteria
a) Study designs: Cross-sectional, case-control and cohort studies conducted on
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heart failure in SSA, with data available on prevalent diabetes and risk factors for
heart failure among patients with diabetes.
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b) Study participants: adult (age >18 years) human participants residing in SSA,
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regardless of the ethnic background.
c) The final diagnosis will be based on physician-made diagnosis or as defined by
WHO/IDF18 for DM and European society of cardiology (ESC)19 / American heart
association (AHA)20 / Framingham criteria21 for HF diagnosis at the time of study
(Table 1), or self-reported.
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d) Time-period: We intend to consider all published and unpublished data done
between January 1, 1995 and August 31st, 2015 while considering changes in
definition of diabetes and heart failure over time.
e) Study settings: health facilities or community-based settings; rural or urban SSA
f) Language: All studies reported in English or French language and conducted on
human subjects will be considered.
Exclusion criteria
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a) Studies conducted among populations of African origin but residing outside
Africa.
b) Studies lacking prevalence rates and risk factors, with absence of data to
compute them.
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c) Case series with small sample sizes (sample less than 30 participants).
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d) Letters to editor, reviews, commentaries, editorials, and any publication without
primary data.
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e) Studies in sub-groups of participants selected based on the presence of
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diabetes.
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f) Duplicate publications from the same study. For studies published in more than
one journal/conference, the most recent and comprehensive publication will be
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used.
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g) Studies not performed in human participants or published in languages other
than English and French.
Source of information
The methods of this systematic review are reported in accordance to the PRISMA-P
2015 Guidelines 22. See table 2 for checklist.
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Search strategy for studies identification
Electronic searches
We will search PubMed MEDLINE, Google Scholar, SCOPUS, ISI Web of Science
(Science Citation Index), Africa Wide Information, African Index Medicus (AIM) and
AFROLIB databases from January 1, 1995 to August 31st, 2015 for published studies on
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diabetes mellitus in heart failure patients in sub-Saharan Africa. This search shall be
conducted using a pre-defined comprehensive and sensitive search strategy combining
relevant terms with names of countries in SSA to obtain the maximum possible number
of studies. This search will be guided by the African search filter which has been
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reported to have good sensitivity (and improved precision) of 74% (1.3% to 9.4%) and
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73% (5% to 28%) for Medline and EMBASE respectively
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. This search filter includes
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names of each African country and shortened terms to capture studies from regions.
Countries with official names in a language other than English will also be entered in the
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official form, and for countries that have changed names over time, both names shall be
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included in the search. Table 3 depicts the main search strategy to be employed.
Reference lists
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We will search reference lists of relevant citations for articles of interest.
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Grey literature
We will contact authors, experts in the field, research organizations, conference
websites and proceedings for any relevant material. This shall be done via emails. If
after repeated attempts to contact authors via email for relevant information and no
response is gotten, the said study shall be excluded.
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Study Records
Data management
All identified search results would be entered into RevMan 5 software for deduplication
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of records. These shall be subsequently uploaded into Eppi-Reviewer which is an
internet based software program to facilitate collaboration between investigators during
the selection of studies to be included in the review. Prior to screening of studies,
investigators shall create standardized and pre-tested questions following the inclusion
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criteria. These questions together with abstracts and full texts of articles would be
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uploaded into Eppi-Reviewer for eventual piloting of the test questions.
Screening
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Two investigators (LNA and AD) will independently select studies that meet inclusion
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criteria. Citations and abstracts will be screened for relevance, and duplicate citations
will be excluded. Titles and abstracts shall then be screened following inclusion criteria
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stipulated earlier, and the full texts of potentially eligible articles will then be obtained.
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These full texts will be screened using a standardized and pre-tested form to include
eligible studies. Disagreements will be resolved by consensus, with consultation of a
third author (APK) when resolution cannot be achieved. Corresponding authors will be
contacted in the event that the publication (1) is unclear and may be subject to multiple
interpretations, or (2) has collected data but did not report data that are relevant to our
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study analysis. For studies which shall be excluded, the reasons shall be documented.
A flow chat will be used to demonstrate the entire review process.
Data extraction
Two investigators (LNA and AD) will independently extract data from included studies,
using a standardized and pre-tested data extraction form. Any inconsistencies or
disagreement shall be resolved by consensus or consultation with the third investigator
(APK).
Data items
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Data will include the geographic region and country where study was conducted, the
year study was done and year of publication, the language of publication, demographic
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characteristics of participants (mean age, sex proportions), study design, setting (rural
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or urban, health-facility or community-based), sample size, number and proportion with
diabetes, known duration of diabetes, diagnostic criteria for diabetes and heart failure
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respectively, cardiovascular as well as diabetes-specific risk factors of patients,
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measures of association (chi square, odds ratios, risk ratios, p values and confidence
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intervals) will be recorded.
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Assessment of methodological quality and risk of bias
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Two reviewers (LNA and AD) will independently score the quality of included studies.
The STROBE checklist
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will be used to evaluate reporting methodology in each paper
while risk of bias in individual studies will be assessed using the Risk of Bias Tool for
Prevalence Studies developed by Hoy et al (Table 4)
available
in
Review
Manager
version
5.3
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, and the Cochrane guidelines
(http://tech.cochrane.org/revman).
Discrepancies will be resolved by consensus or by consulting the third investigator
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(APK). Inter-rater agreement on screening, data abstraction, and methodological quality
will be assessed using Cohen’s kappa (κ) coefficient 26. We intend to present risk of bias
and quality scores in a table.
Data synthesis, analysis and assessment of heterogeneity
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Data will be synthesized to answer both research questions. Prevalence data will be
summarized by country and geographic regions. In a situation where a population is
reported at both regional level and national estimates, we shall consider the most
comprehensive and updated national estimates. Any other material will be excluded or
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considered as duplicate. A meta-analysis will be performed for the prevalence across
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studies with similar characteristic. Further to this purpose, the study specific estimates
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will be pooled through a random-effects meta-analysis model, to obtain the overall
summary estimate of the prevalence across studies, after stabilizing the variance of
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individual studies with the use of the Freeman-Tukey double arc-sine transformation27.
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Such a transformation is required to reduce the effect of extremely high or extremely
low prevalence rates on the pooled estimate. Heterogeneity will be evaluated by the χ2
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test on Cochrane’s Q statistic which is quantified by I2 values 28, assuming that I2 values
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of 25%, 50% and 75% represent low, medium and high heterogeneity respectively.
Funnel plots supplemented by the Egger test
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of bias will be used to investigate the
publication bias. When statistical data pooling does not yield meaningful results, such
as in the presence of considerable clinical heterogeneity, we will conduct a narrative
synthesis. Meta-analysis will be conducted overall, that is across all possible eligible
studies. However we will also conduct subgroup analysis to compare the estimate
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across major predictive characteristics and assess the consistency of the effects across
those subgroups. Major grouping characteristics will include gender (gender specific
analysis where possible; and below vs. at or above the median proportion of men
across study) age (below vs. at or above the median), geographic region, time the study
was conducted/published (below vs. at or above the median); diagnosed duration of
diabetes (below vs. at or above the median), diagnostic methods; study design, etc. For
determinants of heart failure, in anticipation of the large variability in their investigation
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and reporting across studies, only a narrative synthesis of the evidence will be
conducted. We will report the total number of determinants investigated across all
studies, and for each of the determinants, the number of times it was reported to be
associated with the outcome. We will further report on the range of measures
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association used for each determinants across studies, with indication of whether those
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measures were adjusted for confounders or not.
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The data will be analyzed using the statistical software R (version 3.0.3 [2014-03-04],
The R Foundation for statistical computing, Vienna, Austria).
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Sensitivity analysis
We will perform subgroup analysis where substantial heterogeneity will be detected to
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identify possible sources with the following grouping variables; age group, gender, study
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setting (rural vs urban, health-facility vs community-based), geographical region
(central, west, east and southern Africa) and study quality. Any subgroup differences
identified will be described, and our findings will be interpreted in the light of these
differences.
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Confidence in cumulative evidence
We intend to assess the strength of evidence provided by studies included in the review
using the Grading of recommendations Assessment, Development and Evaluation
(GRADE) approach. This assessment of the quality of evidence would include; risk of
bias, consistency and publication bias. Studies in which further research is respectively;
unlikely to change effect estimates, or likely to have a considerable impact on effect
estimates, or capable of changing the effect estimates, or those in which there is
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uncertainty in effect estimates will be described as ‘high’, ‘moderate’, ‘low’ or ‘very low’
qualities.
Reporting of this review
The proposed systematic review will be reported following the Preferred Reporting
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Items for Systematic reviews and Meta-analyses (PRISMA) guidelines
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publish a PRISMA checklist alongside the final report.
Potential amendments
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outcome reporting bias. However, any amendments that do prove necessary will be
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documented and reported transparently.
Conclusion
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worsen in the coming decades if no action is taken. Diabetes mellitus is a major
contributor to the CVD burden and studies among Caucasians suggest it is an
independent predictor of mortality in heart failure (the final end point for most
cardiovascular diseases). The epidemiology and burden of diabetes in this group of
patients with heart failure has been less well documented in Africa. We intend to
describe the current prevalence of diabetes mellitus among heart failure patients and
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Page 12 of 22
the determinants of heart failure among diabetic patients in SSA. Determining this
current burden will be important for clinicians providing care to this vulnerable group of
patients. If diabetes is found to be common among heart failure patients in SSA like
elsewhere, clinicians in this setting would have to be alert when managing these
patients and more especially, aggressively control identified modifiable risk factors. This
would reduce the morbidity and mortality associated with CVD, as well as economic
burden in an already financially-constrained setting plagued with communicable
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diseases as well. Possible limitations of this study would include; predominance of poor
quality studies, significant heterogeneity of studies precluding further analysis. In
addition, a predominance of cross-sectional studies would make it difficult to obtain or
determine risk factors for diabetes. Finally, including only studies published in English or
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French, we may lose relevant data from studies published in other languages. This
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review will however identify gaps in the current literature on this topic and provide
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direction for future research in people with diabetes and cardiomyopathy.
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Ethics and dissemination
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The current study is based on published data hence wouldn’t require ethical approval.
The final report of this review in the form of a scientific paper will be published in a peer-
on
reviewed journal. Findings will also be presented at conferences and submitted to
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relevant health and policy authorities. We also plan to update the review in the future to
monitor any progressive changes on the subject.
Authors’ contributions
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Conceived and designed protocol: AD and LNA. Manuscript drafting: LNA. Critical
revision for methodological and intellectual content: APK, AD. LNA is the guarantor of
this review. All authors read and approved the final version of the manuscript.
Competing Interest: None
Funding: This research received no specific grant from any funding agency in the
public, commercial or not-for-profit sectors
Acknowledgement
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We are grateful to our families for supporting us through our research endeavours.
REFERENCES
1.
Zimmet P, Alberti KG, Shaw J. Global and societal implications of the diabetes
epidemic. Nature. 2001 Dec 13;414(6865):782–7.
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Kengne AP, Mayosi BM. Readiness of the primary care system for noncommunicable diseases in sub-Saharan Africa. Lancet Glob Health. 2014
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Bloomfield GS, Barasa FA, Doll JA, Velazquez EJ. Heart failure in Sub-Saharan
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Aguiree F, Brown A, Cho NH, Dahlquist G, Dodd S, Dunning T, et al. IDF Diabetes
Atlas, 6th edition. International Diabetes Federation; 2013.
10. Kengne AP, Echouffo-Tcheugui J-B, Sobngwi E, Mbanya J-C. New insights on
diabetes mellitus and obesity in Africa-part 1: prevalence, pathogenesis and
comorbidities. Heart Br Card Soc. 2013 Jul;99(14):979–83.
11. Stamler J, Vaccaro O, Neaton JD, Wentworth D. Diabetes, other risk factors, and
12-yr cardiovascular mortality for men screened in the Multiple Risk Factor
Intervention Trial. Diabetes Care. 1993 Feb;16(2):434–44.
12. Woodward M, Zhang X, Barzi F, Pan W, Ueshima H, Rodgers A, et al. The effects
of diabetes on the risks of major cardiovascular diseases and death in the AsiaPacific region. Diabetes Care. 2003 Feb;26(2):360–6.
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13. Soläng L, Malmberg K, Rydén L. Diabetes mellitus and congestive heart failure.
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14. Bell DSH. Diabetic cardiomyopathy. Diabetes Care. 2003 Oct;26(10):2949–51.
15. Kengne AP, Dzudie A, Sobngwi E. Heart failure in sub-Saharan Africa: a literature
review with emphasis on individuals with diabetes. Vasc Health Risk Manag.
2008;4(1):123–30.
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16. Aguilar D, Solomon SD, Køber L, Rouleau J-L, Skali H, McMurray JJV, et al. Newly
diagnosed and previously known diabetes mellitus and 1-year outcomes of acute
myocardial infarction: the VALsartan In Acute myocardial iNfarcTion (VALIANT)
trial. Circulation. 2004 Sep 21;110(12):1572–8.
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17. Ancion A, Lancellotti P, Piérard LA. [Congestive heart failure and diabetes
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18. World Health Organisation (WHO). Definition and diagnosis of diabetes mellitus
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https://www.idf.org/webdata/docs/WHO_IDF_definition_diagnosis_of_diabetes.pdf
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19. McMurray JJV, Adamopoulos S, Anker SD, Auricchio A, Böhm M, Dickstein K, et
al. ESC guidelines for the diagnosis and treatment of acute and chronic heart
failure 2012: The Task Force for the Diagnosis and Treatment of Acute and
Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in
collaboration with the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail.
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20. Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE, Drazner MH, et al. 2013
ACCF/AHA guideline for the management of heart failure: a report of the American
College of Cardiology Foundation/American Heart Association Task Force on
Practice Guidelines. J Am Coll Cardiol. 2013 Oct 15;62(16):e147–239.
21. McKee PA, Castelli WP, McNamara PM, Kannel WB. The natural history of
congestive heart failure: the Framingham study. N Engl J Med. 1971 Dec
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22. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred
reporting items for systematic review and meta-analysis protocols (PRISMA-P)
2015 statement. Syst Rev. 2015;4:1.
23. Pienaar E, Grobler L, Busgeeth K, Eisinga A, Siegfried N. Developing a geographic
search filter to identify randomised controlled trials in Africa: finding the optimal
balance between sensitivity and precision. Health Inf Libr J. 2011 Sep;28(3):210–5.
24. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et
al. The Strengthening the Reporting of Observational Studies in Epidemiology
(STROBE) statement: guidelines for reporting observational studies. Bull World
Health Organ. 2007 Nov;85(11):867–72.
25. Hoy D, Brooks P, Woolf A, Blyth F, March L, Bain C, et al. Assessing risk of bias in
prevalence studies: modification of an existing tool and evidence of interrater
agreement. J Clin Epidemiol. 2012 Sep;65(9):934–9.
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26. Landis JR, Koch GG. The measurement of observer agreement for categorical
data. Biometrics. 1977 Mar;33(1):159–74.
27. Barendregt JJ, Doi SA, Lee YY, Norman RE, Vos T. Meta-analysis of prevalence. J
Epidemiol Community Health. 2013 Nov 1;67(11):974–8.
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28. Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat
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29. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected
by a simple, graphical test. BMJ. 1997 Sep 13;315(7109):629-34.
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30. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting
items for systematic reviews and meta-analyses: the PRISMA statement. Ann
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Table 1: Definitions of Diabetes Mellitus and Heart Failure
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Disease
Diabetes mellitus (IDF/WHO) 2006
Heart Failure definition
1) European Society of Cardiology
(ESC) guidelines 2012
2)
American Heart Association/
American College of Cardiology
Foundation (AHA/ACCF) 2013
3)
Framingham criteria for clinical
diagnosis
Definition
Fasting plasma glucose ≥ 7.0mmol/l (126mg/dl) or
2-hour plasma glucose ≥ 11.1mmol/l (200mg/dl)
HF is defined, clinically, as a syndrome in which patients have typical symptoms
(e.g. breathlessness, ankle swelling, and fatigue) and signs (e.g. elevated jugular
venous pressure, pulmonary crackles, and displaced apex beat) resulting from an
abnormality of cardiac structure or function (cardiomegaly, third heart sound,
abnormality on echocardiogram, raised natriuretic peptide concentration).
A complex clinical syndrome that results from any structural or functional
impairment of ventricular filling or ejection of blood. The cardinal manifestations are
dyspnoea and fatigue, which may limit exercise tolerance, and fluid retention, which
may lead to pulmonary and/or splanchnic congestion and/or peripheral oedema.
A) Major Criteria: paroxysmal nocturnal dyspnoea; neck vein distension; crackles;
radiographic cardiomegaly; acute pulmonary oedema; S3 gallop; central venous
pressure > 16cmH20; circulation time 25s; hepatojugular reflux; pulmonary oedema,
visceral congestion or cardiomegaly at autopsy; weight loss – 4.5kg in 5 days in
response to treatment of congestive heart failure.
B) Minor criteria: bilateral ankle oedema; nocturnal cough; dyspnoea on ordinary
exertion; hepatomegaly; pleural effusion; decrease vital capacity by one-third from
maximal value recorded; tachycardia (>120beats/min)
N.B: The diagnosis of congestive heart failure requires that two major only or one
major and two minor criteria be present concurrently.
Minor criteria are acceptable only if they are not attributed to another medical
condition
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Table 2: PRISMA-P 2015 checklist for systematic review and meta-analysis protocol on prevalent diabetes
mellitus in heart failure patients in sub-Saharan Africa.
Section/topic
Item
No.
Title
Identification
Update
1a
1b
Registration
2
Authors
Contact
3a
Contributions
Amendments
Support
Sources
Checklist item
Status
Identify the report as a protocol of a systematic review
If the protocol is for an update of a previous systematic review, identify
as such
If registered, provide the name of the registry (e.g. PROSPERO) and
registration number
Done (page 1)
NAP
Provide name, institutional affiliation, and e-mail address of all protocol
authors, provide physical mailing address of corresponding author
Describe contributions of protocol authors and identify the guarantor of
the review
If the protocol represents an amendment of a previously completed or
published protocol, identify as such and list changes; otherwise, state
plan for documenting important protocol amendments
Done
(page 1)
Done
(page 11)
Done
(page 10)
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3b
4
5a
Indicate sources of financial or other support
Done
(page 2)
Done (page 11)
NAP
NAP
Sponsor
Role of
sponsor/funder
INTRODUCTION
Rationale
5b
5c
Provide name of the review funder and/or sponsor
Describe role(s) of funder(s), sponsor(s), and/or institution(s), if any, in
developing the protocol
6
Done (page 2)
Objectives
7
Describe the rationale for the review in the context of what is already
known
Provide an explicit statement of the question(s) the review will address
with reference to participants, interventions, comparators, and
outcomes (PICO)
METHODS
Eligibility criteria
8
Specify the study characteristics (e.g. PICO, study design, setting, time
frame) and report characteristics (e.g. years considered, language,
publication status) to be used as criteria of eligibility for the review
Describe all intended information sources (e.g. electronic databases
contact with study authors, trial registers, or other grey literature
sources) with planned dates of coverage
Present draft of search strategy to be used for at least one electronic
database, including planned limits, such that it could be repeated
Done (pages 4 &
5)
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Study Records
Data management
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Information
sources
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Describe the mechanism(s) that will be used to manage data throughout
the review
Selection process
11b
State the process that will be used for selecting studies (e.g. two
independent reviewers) through each phase of the review (i.e.
screening, eligibility, and inclusion in meta-analysis)
Data collection
11c
Describe planned method of extracting data from reports (e.g. piloting
process
forms, done independently, in duplicate), any process for obtaining and
confirming data from investigators
Data items
12
List and define all variables for which data will be sought (e.g. PICO
items, funding sources), any pre-planned data assumptions and
simplifications
Outcomes and
13
List and define all outcomes for which data will be sought, including
prioritization
prioritization of main and additional outcomes, with rationale
Risk of bias in
14
Describe anticipated methods for assessing risk of bias of individual
individual studies
studies, including whether this will be done at the outcome or study
level, or both; state how this information will be used in data synthesis
Data Synthesis
15a
Describe criteria under which study data will be quantitatively
synthesized
15b
If data are appropriate for quantitative synthesis, describe planned
summary measures, methods of handling data, and methods of
combining data from studies, including any planned exploration of
2
consistency (e.g. I , kendall’s tau)
18
15c
Describe any proposed additional analysis (e.g. sensitivity or sub group
meta-regression)
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15d
If quantitative synthesis is not appropriate, describe the type of
summary planned
Meta-bias(es)
16
Specify if any planned assessment of meta-bias(es) (e.g. publication
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Done (page 4)
Done
(page 6)
Done
(page 17)
Done
(page 7)
Done (page 7)
Done (page 7)
Done (page 8)
--
Done (page 8)
Done (page 9)
Done (page 9)
Done (page 9)
Done (page 9)
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Confidence in
cumulative
evidence
17
bias across studies, selective reporting within studies)
Describe how the strength of the body of evidence will be assessed
(e.g. GRADE)
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Done
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Table 3: Search strategy for MEDLINE, and adaptable to regional data bases
Search
Search terms
Hits
1
Heart failure [tw] OR cardiac failure [tw] OR cardiac insufficiency [tw] OR heart
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disease [tw] OR cardiac
2
diabetes mellitus [tw] OR type 1 diabetes [tw] OR type 2 diabetes [tw] OR type
1 diabetes mellitus [tw] OR type 2 diabetic mellitus [tw] OR diabetes [tw] OR
diabetics [tw] diabetic cardiomyopathy [tw]
3
#1 AND #2
4
African filter
((((Angola[tw] OR Benin[tw] OR Botswana[tw] OR "Burkina Faso"[tw] OR
Burundi[tw] OR Cameroon[tw]
OR "Cape Verde"[tw] OR "Central African
Republic"[tw] OR Chad[tw] OR Comoros[tw] OR Congo[tw] OR "Democratic
Republic of Congo"[tw] OR Djibouti[tw] OR "Equatorial Guinea"[tw] OR
Eritrea[tw] OR Ethiopia[tw] OR Gabon[tw] OR Gambia[tw] OR Ghana[tw] OR
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Guinea[tw] OR "Guinea Bissau"[tw] OR "Ivory Coast"[tw] OR "Cote d'Ivoire"[tw]
OR Kenya[tw] OR Lesotho[tw] OR Liberia[tw] OR Madagascar[tw] OR Malawi[tw]
OR Mali[tw] OR Mauritania[tw] OR Mauritius[tw] OR Mozambique[tw] OR
Namibia[tw] OR Niger[tw] OR Nigeria[tw] OR Principe[tw] OR Reunion[tw] OR
Rwanda[tw] OR "Sao Tome"[tw] OR Senegal[tw] OR Seychelles[tw] OR "Sierra
Leone"[tw]
OR
ee
Somalia[tw]
OR
"South
Africa"[tw]
OR
Sudan[tw]
OR
Swaziland[tw] OR Tanzania[tw] OR Togo[tw] OR Uganda[tw] OR "Western
Sahara"[tw] OR Zambia[tw] OR Zimbabwe[tw] OR "Central Africa"[tw] OR
rr
"Central African"[tw] OR "West Africa"[tw] OR "West African"[tw] OR "Western
Africa"[tw] OR "Western African"[tw] OR "East Africa"[tw] OR "East African"[tw]
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OR "Eastern Africa"[tw] OR "Eastern African"[tw] OR "South African"[tw] OR
"Southern Africa"[tw] OR "Southern African"[tw] OR "sub Saharan Africa"[tw] OR
"sub Saharan African"[tw] OR "subSaharan Africa"[tw] OR "subSaharan
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African"[tw] NOT “guinea pig” [tw] NOT “guinea pigs” [tw] NOT “aspergillus niger”
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[tw] ))))
5
# 3 AND # 4 Limits: 01/01/1995 to 31/08/2015 in English and French on humans
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Table 4: Risk of bias assessment tool (Adapted from the Risk of Bias Tool for Prevalence
Studies developed by Hoy et al. (2012))
Risk of bias Item
Response:
Yes (Low Risk) or No (High risk)
External Validity
1. Was the study target population a close representation of the
national population in relation to relevant variables?
2. Was the sampling frame a true or close representation of the target
population?
3. Was some form of random selection used to select the sample,
OR, was a census undertaken?
4. Was the likelihood of non-participation bias minimal?
Internal Validity
5. Were data collected directly from the subjects (as opposed to
medical records)?
6. Were acceptable case definition of diabetes and heart failure
used?
7. Were reliable and accepted diagnostic methods for diabetes and
heart failure utilized?
8. Was the same mode of data collection used for all subjects?
9. Was the length of the shortest prevalence period for the parameter
of interest appropriate?
10. Were the numerator(s) and denominator(s) for the calculation of
the prevalence of diabetes appropriate?
11. Summary item on the overall risk of study bias
Low Risk of Bias: 8 or more “yes” answers.
Further research is very unlikely to change our confidence in the
estimate.
Moderate Risk of Bias: 6 to 7 “yes” answers.
Further research is likely to have an important impact on our
confidence in the estimate and may change the estimate.
High Risk of Bias: 5 or fewer “yes” answers.
Further research is very likely to have an important impact on our
confidence in the estimate and is likely to change the estimate.
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Page 22 of 22
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Prevalent diabetes mellitus in patients with
heart failure and disease determinants in
sub-Saharan Africans having diabetes with
heart failure: a protocol for a systematic review
and meta-analysis
Leopold Ndemnge Aminde, Anastase Dzudie and Andre Pascal Kengne
BMJ Open 2016 6:
doi: 10.1136/bmjopen-2015-010097
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