Literature DB >> 18717978

UKPDS-modelling of cardiovascular risk assessment and lifetime simulation of outcomes.

A I Adler1.   

Abstract

Although known principally as a clinical trial, the UK Prospective Diabetes Study (UKPDS) provided longitudinal data which helped define the natural history of cardiovascular complications in Type 2 diabetes. Using clinical, epidemiological, statistical and economics methods, UKPDS investigators developed mathematical models that helped define predictors (risk factors) for cardiovascular disease including angina, myocardial infarction, stroke, peripheral vascular disease and death in Type 2 diabetes. The UKPDS made clearer the contributions to risk of age, hyperglycaemia, elevated blood pressure, adverse blood lipids and smoking. Equations were developed, combined and incorporated into the UKPDS Risk Engine and the UKPDS Outcomes models. For example, the UKPDS risk engine-version 2-estimates that a white 62-year-old man with 11 years of Type 2 diabetes, a glycated haemoglobin of 8.3%, a systolic blood pressure of 145 mmHg and total and high-density lipoprotein cholesterol values of 5.8 and 1.1 mmol/l who did not smoke has a 33% chance of having overt coronary heart disease within 10 years. These models contribute to the estimation of risk and/or health outcomes adjusted for quality of life for use by, amongst others, clinicians, trialists, health planners, guideline developers and health economists.

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Year:  2008        PMID: 18717978     DOI: 10.1111/j.1464-5491.2008.02498.x

Source DB:  PubMed          Journal:  Diabet Med        ISSN: 0742-3071            Impact factor:   4.359


  7 in total

1.  Cardiovascular disease in type 2 diabetes: Attributable risk due to modifiable risk factors.

Authors:  John Zeber; Michael L Parchman
Journal:  Can Fam Physician       Date:  2010-08       Impact factor: 3.275

2.  Matrix metalloproteinases in type 2 diabetes and non-diabetic controls: effects of short-term and chronic hyperglycaemia.

Authors:  Krzysztof C Lewandowski; Ewa Banach; Małgorzata Bieńkiewicz; Andrzej Lewiński
Journal:  Arch Med Sci       Date:  2011-05-17       Impact factor: 3.318

3.  Individual risk assessment and information technology to optimise screening frequency for diabetic retinopathy by Aspelund et al. (2011) Diabetologia 54:2525-2532.

Authors:  Sarah McGhee; Simon P Harding; David Wong
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  2012-03-10       Impact factor: 3.117

4.  The Diabetes Management Education Program in South Texas: An Economic and Clinical Impact Analysis.

Authors:  Bita A Kash; Szu-Hsuan Lin; Juha Baek; Robert L Ohsfeldt
Journal:  Front Public Health       Date:  2017-12-18

5.  Behavioral Strategies to Lower Postprandial Glucose in Those with Type 2 Diabetes May Also Lower Risk of Coronary Heart Disease.

Authors:  Daniel J Cox; Kun Fang; Anthony L McCall; Mark R Conaway; Tom A Banton; Matthew A Moncrief; Anne M Diamond; Ann G Taylor
Journal:  Diabetes Ther       Date:  2018-12-18       Impact factor: 2.945

6.  Coronary computed tomography angiography as a screening tool for moderate-high risk asymptomatic type 2 diabetes mellitus patients.

Authors:  Qiaolu Liu; Jianfeng Qiu; Shuxin Sun; Xiaoqiang Wang; Zhanguo Sun; Huihui Zhao
Journal:  Front Cardiovasc Med       Date:  2022-08-09

7.  Diet and glycosylated haemoglobin in the 1946 British birth cohort.

Authors:  C J Prynne; A Mander; M E J Wadsworth; A M Stephen
Journal:  Eur J Clin Nutr       Date:  2009-06-24       Impact factor: 4.016

  7 in total

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