Differences in the Cardiovascular Risk Assessment in Cardiology Outpatients in Mali: Comparison between Framingham Body Mass Index-Based Tool and Low-Information World Health Organization Chart

Abstract

Objective. This study aimed to compare 2 laborless tools, namely, the body mass index-based Framingham (bmi-Frm) and low-information WHO- (li-WHO-) based risk scores, and assess their agreement in outpatients in a cardiology department. Methodology. Data stem from a cross-sectional previous study performed from May to September 2016 in the Cardiology Department of University Hospital Gabriel Touré (UH-GT) in Bamako. All patients aged 40 and more were included in the study allowing the assessment of bmi-Frm and li-WHO prediction charts. The cardiovascular risk (CVR) was evaluated using a calculator prepared by D‘Agostino et al. for the bmi-Frm and the li-WHO chart for the Afro-D region of the WHO. The risk score for both ranged from <10 to ≥40. The data were entered in an ACCESS 2010 database, then processed by MS Excel 2010, and finally analysed using IBM SPSS Statistics 20. Continuous variables were presented as means and standard deviations, and categorical variables were presented as frequencies with percentages. $P<0.05$ was considered the statistical significance level. After sample description, the risk score was assessed using bmi-Frm and li-WHO prediction tools. Finally, a kappa test was performed to check for the interreliability of both methods. For weighted kappa, coefficients were given all five classes of risk groups in 0, 25 steps from 1 for total concordance to 0 for total discordance. Results. This study involved 793 outpatients, 63.7% being female, 35.1% of them younger than 50 years, 57.9% with no formal education, and 67.7% with no medical insurance. Means for age, body mass index (BMI), and systolic blood pressure (SBP) were, respectively, 53.81 ± 16.729 years, 25.29 ± 06.151 kg/m2, and 139.49 ± 27.110 mm Hg. Using the li-WHO prediction chart gives a much higher proportion of low-risk patients compared to bmi-Frm (83.6 vs. 37.7). Sociodemographic characteristics such as education or income level were not different in risk score neither for the bmi-Frm nor for the li-WHO risk score. The percentage of agreement between both tools was 40.4%, and agreement (kappa of 0.1 and weighted kappa of 0.2) was found to be slight. Conclusion. Using the bmi-Frm and li-WHO tool gives a similar risk estimation in younger female patients. Older patients must be evaluated using high-information tools with cholesterol, e.g., versions of the Framingham risk equation or WHO using cholesterol. These must be confirmed in further studies and compared to data from prospective studies