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      Sickle cell disease, sickle trait and the risk for venous thromboembolism: a systematic review and meta-analysis

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          Globally, sickle cell disease (SCD) is one of the most common haemoglobinopathy. Considered a public health problem, it leads to vessel occlusion, blood stasis and chronic activation of the coagulation system responsible for vaso-occlussive crises and venous thromboembolism (VTE) which may be fatal. Although contemporary observational studies suggest a relationship between SCD or sickle trait (SCT) and VTE, there is lack of a summary or meta-analysis data on this possible correlation. Hence, we propose to summarize the available evidence on the association between SCD, SCT and VTE including deep vein thrombosis (DVT) and pulmonary embolism (PE).


          We searched PubMed and Scopus to identify all cross-sectional, cohort and case-control studies reporting on the association between SCD or SCT and VTE, DVT or PE in adults or children from inception to April 25, 2017. For measuring association between SCD or SCT and VTE, DVT, or PE, a meta-analysis using the random-effects method was performed to pool weighted odds ratios (OR) of risk estimates.


          From 313 records initially identified from bibliographic databases, 10 studies were eligible and therefore included the meta-analysis. SCD patients had significantly higher risk for VTE (pooled OR 4.4, 95%CI 2.6–7.5, p < 0.001), DVT (OR 1.1, 95% CI 1.1–1.2, p < 0.001) and PE (pooled OR 3.7, 95% CI 3.6–3.8, p < 0.001) as compared to non SCD-adults. A higher risk of VTE (OR 33.2, 95% CI 9.7–113.4, p < 0.001) and DVT (OR 30.7, 95% CI 1.6–578.2, p = 0.02) was found in pregnant or postpartum women with SCD as compared to their counterparts without SCD. Compared to adults with SCT, the risk of VTE was higher in adults with SCD (pooled OR 3.1, 95% CI 1.8–5.3, p < 0.001), and specifically in SCD pregnant or postpartum women (OR 20.3, 95% CI 4.1–102, p = 0.0003). The risk of PE was also higher in adults with SCD (OR 3.1, 95% CCI 1.7–5.9, p = 0.0004) as compared to those with SCT. The risk of VTE was higher in individuals with SCT compared to controls (pooled OR 1.7, 95% CI 1.3–2.2, p < 0.0001), but not in pregnant or postpartum women (OR 0.9, 95% CI 0.3–2.9, p = 0.863). Compared to controls, SCT was associated with a higher risk of PE (pooled OR 2.1, 95% CI 1.2–3.8, p = 0.012) but not of DVT (pooled OR 1.2, 95% CI 0.9–1.7, p = 0.157).


          Individuals with SCD, especially pregnant or postpartum women, might have a higher risk of VTE compared to the general population. SCT might also increases the risk of VTE. However, currently available data are not sufficient to allow a definite conclusion. Further larger studies are needed to provide a definitive conclusion on the association between SCD, SCT and VTE.

          Electronic supplementary material

          The online version of this article (10.1186/s12959-018-0179-z) contains supplementary material, which is available to authorized users.

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          Most cited references 48

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          Quantifying heterogeneity in a meta-analysis.

          The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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            Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.

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              Understanding interobserver agreement: the kappa statistic.

              Items such as physical exam findings, radiographic interpretations, or other diagnostic tests often rely on some degree of subjective interpretation by observers. Studies that measure the agreement between two or more observers should include a statistic that takes into account the fact that observers will sometimes agree or disagree simply by chance. The kappa statistic (or kappa coefficient) is the most commonly used statistic for this purpose. A kappa of 1 indicates perfect agreement, whereas a kappa of 0 indicates agreement equivalent to chance. A limitation of kappa is that it is affected by the prevalence of the finding under observation. Methods to overcome this limitation have been described.

                Author and article information

                [1 ]ISNI 0000 0004 1937 1151, GRID grid.7836.a, Department of Medicine, , Groote Schuur Hospital and University of Cape Town, ; Cape Town, 7925 South Africa
                [2 ]ISNI 0000 0001 2173 8504, GRID grid.412661.6, Department of Internal Medicine and sub-Specialties, Faculty of Medicine and Biomedical Sciences, ; Yaoundé, Cameroon
                [3 ]ISNI 0000 0001 2173 8504, GRID grid.412661.6, Department of Surgery and sub-Specialties, Faculty of Medicine and Biomedical Sciences, ; Yaoundé, Cameroon
                [4 ]ISNI 0000 0004 1937 1151, GRID grid.7836.a, Division of Human Genetics, Faculty of Health Sciences, , University of Cape Town, ; Cape Town, South Africa
                [5 ]Department of Epidemiology and Public Health, Centre Pasteur of Cameroon, Yaoundé, Cameroon
                [6 ]ISNI 0000 0001 2171 2558, GRID grid.5842.b, Faculty of Medicine, , University of Paris Sud XI, ; Le Kremlin Bicêtre, France
                Thromb J
                Thromb J
                Thrombosis Journal
                BioMed Central (London )
                4 October 2018
                4 October 2018
                : 16
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

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