Concordance between two versions of world health organization/international society of hypertension risk prediction chart and framingham risk score among postmenopausal women in a rural area of Bangladesh - Lingkan Barua; Mithila Faruque; Palash Chandra Banik; Liaquat Ali (Wolters Kluwer Medknow Publications, 2019)
Background: Prevention of cardiovascular disease (CVD) among postmenopausal women with limited resource is a great challenge for a country like Bangladesh. Objectives: This study aimed to evaluate the level of agreement among different risk prediction tools to find out the cost-effective and suitable one that can be applied in a low-resource setting. Methods: This was a cross-sectional study conducted from February through December 2016 among 265 postmenopausal women of 40-70 years age. Data were collected from the outpatient department of a rural health-care center situated in the village Karamtola of Gazipur district, Bangladesh. The CVD risk was estimated using the World Health Organization/International Society of Hypertension (WHO/ISH) "with" and "without" cholesterol risk charts and the Framingham Risk Score (FRS). Concordance among the tools was evaluated using Cohen's kappa (κ), prevalence-adjusted bias-adjusted kappa (PABAK), and first-order agreement coefficient (AC1). Results: The "without" cholesterol version showed 79% concordance against the "with" cholesterol and 75.4% concordance against the FRS. In between the WHO/ISH risk charts, slight-to-substantial levels of agreement (κ = 0.14, PABAK = 0.58, and AC1 = 0.72; P = 0.023) were observed. With FRS, the "without" cholesterol version showed higher agreement (κ = 0.38, fair; PABAK = 0.50, moderate; and AC1 = 0.60, moderate; P = 0.000) compared to "with" cholesterol version (κ = 0.13, slight; PABAK = 0.30, fair; and AC1 = 0.44, moderate; P = 0.013). Predictability of CVD risk positive (≥10%) cases was similar for both the versions of WHO/ISH risk charts. Conclusion: In a low-resource setting, the "without" cholesterol version of WHO/ISH risk chart is a good option to detect and target the population with high CVD risk.