Literature DB >> 25070380

Comparison of the Framingham Risk Score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population.

Sharmini Selvarajah1, Gurpreet Kaur2, Jamaiyah Haniff3, Kee Chee Cheong4, Tee Guat Hiong2, Yolanda van der Graaf5, Michiel L Bots5.   

Abstract

BACKGROUND: Cardiovascular risk-prediction models are used in clinical practice to identify and treat high-risk populations, and to communicate risk effectively. We assessed the validity and utility of four cardiovascular risk-prediction models in an Asian population of a middle-income country.
METHODS: Data from a national population-based survey of 14,863 participants aged 40 to 65 years, with a follow-up duration of 73,277 person-years was used. The Framingham Risk Score (FRS), SCORE (Systematic COronary Risk Evaluation)-high and -low cardiovascular-risk regions and the World Health Organization/International Society of Hypertension (WHO/ISH) models were assessed. The outcome of interest was 5-year cardiovascular mortality. Discrimination was assessed for all models and calibration for the SCORE models.
RESULTS: Cardiovascular risk factors were highly prevalent; smoking 20%, obesity 32%, hypertension 55%, diabetes mellitus 18% and hypercholesterolemia 34%. The FRS and SCORE models showed good agreement in risk stratification. The FRS, SCORE-high and -low models showed good discrimination for cardiovascular mortality, areas under the ROC curve (AUC) were 0.768, 0.774 and 0.775 respectively. The WHO/ISH model showed poor discrimination, AUC=0.613. Calibration of the SCORE-high model was graphically and statistically acceptable for men (χ(2) goodness-of-fit, p=0.097). The SCORE-low model was statistically acceptable for men (χ(2) goodness-of-fit, p=0.067). Both SCORE-models underestimated risk in women (p<0.001).
CONCLUSIONS: The FRS and SCORE-high models, but not the WHO/ISH model can be used to identify high cardiovascular risk in the Malaysian population. The SCORE-high model predicts risk accurately in men but underestimated it in women.
Copyright © 2014. Published by Elsevier Ireland Ltd.

Entities:  

Keywords:  Cardiovascular disease prevention; Mortality; Risk prediction; Risk score; Validation

Mesh:

Year:  2014        PMID: 25070380     DOI: 10.1016/j.ijcard.2014.07.066

Source DB:  PubMed          Journal:  Int J Cardiol        ISSN: 0167-5273            Impact factor:   4.164


  54 in total

1.  Coronary artery disease risk assessment from unstructured electronic health records using text mining.

Authors:  Jitendra Jonnagaddala; Siaw-Teng Liaw; Pradeep Ray; Manish Kumar; Nai-Wen Chang; Hong-Jie Dai
Journal:  J Biomed Inform       Date:  2015-08-28       Impact factor: 6.317

2.  Cardiovascular risk assessment models: Have we found the perfect solution yet?

Authors:  Aiden Abidov; Omar Chehab
Journal:  J Nucl Cardiol       Date:  2019-02-21       Impact factor: 5.952

3.  Assessment of cardiovascular risk in low resource settings "So much to do - So little done".

Authors:  V Hariram; Sreenivas Kumar Arramraju
Journal:  Indian Heart J       Date:  2016-01-18

Review 4.  Contemporary Review of Risk Scores in Prediction of Coronary and Cardiovascular Deaths.

Authors:  Jose B Cruz Rodriguez; Khan O Mohammad; Haider Alkhateeb
Journal:  Curr Cardiol Rep       Date:  2022-01-27       Impact factor: 2.931

5.  Estimation of the cardiovascular risk using World Health Organization/International Society of Hypertension (WHO/ISH) risk prediction charts in a rural population of South India.

Authors:  Arun Gangadhar Ghorpade; Saurabh RamBihariLal Shrivastava; Sitanshu Sekhar Kar; Sonali Sarkar; Sumanth Mallikarjuna Majgi; Gautam Roy
Journal:  Int J Health Policy Manag       Date:  2015-04-21

6.  The risk of hypertension and cardiovascular disease in women with uterine fibroids.

Authors:  Yentl C Haan; Frederieke S Diemer; Lisa Van Der Woude; Gert A Van Montfrans; Glenn P Oehlers; Lizzy M Brewster
Journal:  J Clin Hypertens (Greenwich)       Date:  2018-03-22       Impact factor: 3.738

7.  Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis.

Authors:  Manish Motwani; Damini Dey; Daniel S Berman; Guido Germano; Stephan Achenbach; Mouaz H Al-Mallah; Daniele Andreini; Matthew J Budoff; Filippo Cademartiri; Tracy Q Callister; Hyuk-Jae Chang; Kavitha Chinnaiyan; Benjamin J W Chow; Ricardo C Cury; Augustin Delago; Millie Gomez; Heidi Gransar; Martin Hadamitzky; Joerg Hausleiter; Niree Hindoyan; Gudrun Feuchtner; Philipp A Kaufmann; Yong-Jin Kim; Jonathon Leipsic; Fay Y Lin; Erica Maffei; Hugo Marques; Gianluca Pontone; Gilbert Raff; Ronen Rubinshtein; Leslee J Shaw; Julia Stehli; Todd C Villines; Allison Dunning; James K Min; Piotr J Slomka
Journal:  Eur Heart J       Date:  2017-02-14       Impact factor: 29.983

8.  Which serum uric acid levels are associated with increased cardiovascular risk in the general adult population?

Authors:  Alena Krajčoviechová; Peter Wohlfahrt; Jan Bruthans; Pavel Šulc; Věra Lánská; Lenka Eremiášová; Jan Pudil; Aleš Linhart; Jan Filipovský; Otto Mayer; Jiří Widimský; Milan Blaha; Claudio Borghi; Renata Cífková
Journal:  J Clin Hypertens (Greenwich)       Date:  2020-04-09       Impact factor: 3.738

9.  Geographic and Sociodemographic Disparities in Cardiovascular Risk in Burkina Faso: Findings from a Nationwide Cross-Sectional Survey.

Authors:  Kadari Cisse; Sekou Samadoulougou; Mady Ouedraogo; Bruno Bonnechère; Jean-Marie Degryse; Seni Kouanda; Fati Kirakoya-Samadoulougou
Journal:  Risk Manag Healthc Policy       Date:  2021-07-07

10.  Ethnic and Gender Differences in 10-Year Coronary Heart Disease Risk: a Cross-Sectional Study in Hawai'i.

Authors:  Claire Townsend Ing; Hyeong Jun Ahn; Rachel Kawakami; Andrew Grandinetti; Todd B Seto; Joseph Keawe'aimoku Kaholokula
Journal:  J Racial Ethn Health Disparities       Date:  2020-08-31
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.