Manish Bansal1, Ravi R Kasliwal2, Naresh Trehan3. 1. Senior Consultant, Cardiology, Medanta - The Medicity, Gurgaon, India. Electronic address: manishaiims@hotmail.com. 2. Chairman, Clinical and Preventive Cardiology, Medanta - The Medicity, Gurgaon, India. 3. Chairman, Cardiothoracic Surgery, Medanta - The Medicity, Gurgaon, India.
Abstract
BACKGROUND: Relative accuracy of the various currently available cardiovascular (CV) risk assessment algorithms in Indian patients is not known. METHODS: This study included 194 consecutive patients (mean age 49.6 ± 10.3 years, 84.5% males) attending a CV disease prevention clinic at a tertiary center in north India. Four risk assessment models [Framingham Risk score (RiskFRS), American College of Cardiology/American Heart Association pooled cohort equations (RiskACC/AHA), the 3rd iteration of Joint British Societies' risk calculator (RiskJBS) and the World Health Organization/International Society of Hypertension risk prediction charts (RiskWHO)] were applied. The estimated risk scores were correlated with carotid intima-media thickness (CIMT) and coronary calcium score (CCS) using nonparametric statistics (Chi-square test, Kruskal-Wallis test and Spearman rank correlation). RESULTS: Overall, RiskACC/AHA and RiskWHO significantly underestimated CV risk as compared to RiskJBS and RiskFRS, with RiskJBS being the least likely to underestimate the risk (patients with coronary artery disease who were found to have ≥20% CV risk- 21.4% with RiskACC/AHA, 17.9% with RiskWHO, 41.4% with RiskFRS, and 58.6% with RiskJBS). Further, only RiskJBS and RiskFRS, but not RiskACC/AHA and RiskWHO, demonstrated consistent relationship with CIMT and CCS (Spearman rho 0.45 and 0.46 for RiskJBS and 0.39 and 0.36 for RiskFRS for CIMT and CCS respectively, all p values < 0.001). CONCLUSIONS: The present study shows that in Indian subjects RiskJBS appears to provide the most accurate estimation of CV risk. It least underestimates the risk and has the best correlation with CIMT and CCS. However, large-scale prospective studies are needed to confirm these findings.
BACKGROUND: Relative accuracy of the various currently available cardiovascular (CV) risk assessment algorithms in Indian patients is not known. METHODS: This study included 194 consecutive patients (mean age 49.6 ± 10.3 years, 84.5% males) attending a CV disease prevention clinic at a tertiary center in north India. Four risk assessment models [Framingham Risk score (RiskFRS), American College of Cardiology/American Heart Association pooled cohort equations (RiskACC/AHA), the 3rd iteration of Joint British Societies' risk calculator (RiskJBS) and the World Health Organization/International Society of Hypertension risk prediction charts (RiskWHO)] were applied. The estimated risk scores were correlated with carotid intima-media thickness (CIMT) and coronary calcium score (CCS) using nonparametric statistics (Chi-square test, Kruskal-Wallis test and Spearman rank correlation). RESULTS: Overall, RiskACC/AHA and RiskWHO significantly underestimated CV risk as compared to RiskJBS and RiskFRS, with RiskJBS being the least likely to underestimate the risk (patients with coronary artery disease who were found to have ≥20% CV risk- 21.4% with RiskACC/AHA, 17.9% with RiskWHO, 41.4% with RiskFRS, and 58.6% with RiskJBS). Further, only RiskJBS and RiskFRS, but not RiskACC/AHA and RiskWHO, demonstrated consistent relationship with CIMT and CCS (Spearman rho 0.45 and 0.46 for RiskJBS and 0.39 and 0.36 for RiskFRS for CIMT and CCS respectively, all p values < 0.001). CONCLUSIONS: The present study shows that in Indian subjects RiskJBS appears to provide the most accurate estimation of CV risk. It least underestimates the risk and has the best correlation with CIMT and CCS. However, large-scale prospective studies are needed to confirm these findings.
Authors: G Belcaro; A N Nicolaides; G Ramaswami; M R Cesarone; M De Sanctis; L Incandela; P Ferrari; G Geroulakos; A Barsotti; M Griffin; S Dhanjil; M Sabetai; M Bucci; G Martines Journal: Atherosclerosis Date: 2001-06 Impact factor: 5.162
Authors: Clara Kayei Chow; Brendan McQuillan; P Krishnam Raju; Srinivas Iyengar; Rama Raju; Jason A Harmer; Bruce C Neal; David S Celermajer Journal: Atherosclerosis Date: 2008-02-20 Impact factor: 5.162
Authors: S S Anand; S Yusuf; V Vuksan; S Devanesen; K K Teo; P A Montague; L Kelemen; C Yi; E Lonn; H Gerstein; R A Hegele; M McQueen Journal: Lancet Date: 2000-07-22 Impact factor: 79.321
Authors: Robert Detrano; Alan D Guerci; J Jeffrey Carr; Diane E Bild; Gregory Burke; Aaron R Folsom; Kiang Liu; Steven Shea; Moyses Szklo; David A Bluemke; Daniel H O'Leary; Russell Tracy; Karol Watson; Nathan D Wong; Richard A Kronmal Journal: N Engl J Med Date: 2008-03-27 Impact factor: 91.245