Estimation of cardiovascular risk in a rural population of Lucknow district using WHO/ISH risk prediction charts

• Main Authors: Trideep J Deori, Monika Agarwal, Jamal Masood, Sugandhi Sharma, Arshi Ansari
• Format: Article
• Language: English
• Published: Wolters Kluwer Medknow Publications 2020-01-01
• Series: Journal of Family Medicine and Primary Care
• Subjects: cardiovascular disease; noncommunicable disease; risk prediction; rural population; who/ish risk prediction charts
• Online Access: http://www.jfmpc.com/article.asp?issn=2249-4863;year=2020;volume=9;issue=9;spage=4853;epage=4860;aulast=Deori

Context: Cardiovascular diseases (CVDs) are the number one cause of death globally, with low- and middle-income countries being affected disproportionately. By 2020, it is projected that there will be 25 million deaths from CVD worldwide, 19 million of which would be from middle- and low-income countries. Aims: The aim of this study was to estimate the 10-year risk of cardiovascular events among adults aged ≥40 years in a rural population of Lucknow district using the World Health Organization (WHO)/International Society of Hypertension (ISH) risk prediction charts for SEAR-D region. Settings and Design: This was a community based cross-sectional study, conducted from September 2017 to August 2018, in the rural areas of Lucknow district. Methods and Material: This study was conducted on 397 subjects aged ≥40 years. The two sets of the WHO/ISH risk prediction charts, with and without cholesterol, for WHO SEAR-D region were used in the study. Statistical analysis used: SPSS, version 23 was used for data analysis. Results: Using the risk assessment tools, with and without cholesterol, 78.5 and 76.8%, respectively, of the study population were in the 10-year cardiovascular risk category of <10% risk, while 11.2 and 10.4%, respectively, were in the category of ≥20% risk. Risk categories were found to be concordant in 86.3% of the population. Conclusions: The WHO/ISH risk prediction charts can be used at low-cost resource setting as a tool to predict CVD risk among asymptomatic individuals, thus, helping in early detection and prevention of CVDs in resource-scarce settings.