Are the Framingham and PROCAM coronary heart disease risk functions applicable to different European populations? The PRIME Study

Empana, Jean‐Philippe; Ducimetière, Pierre; Arveiler, Dominique; Ferrières, Jean; Evans, Alun; Ruidavets, Jean‐Bernard; Haas, Bernadette; Yarnell, John; Bingham, Annie; Amouyel, Philippe; Dallongeville, Jean

doi:10.1016/j.ehj.2003.09.002

Cited by 226 publications

(148 citation statements)

References 24 publications

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“…For example, similar relative risks were obtained for the classic cardiovascular risk factors for men living in Northern Ireland, France, the USA and Germany, despite the fact that the underlying risk of coronary heart disease varies substantially between these countries. 166,167 In contrast, in a study of hypertension as a risk factor for cardiovascular disease mortality, the data were more compatible with a constant rate difference than with a constant rate ratio. 168 Widely used statistical models, including logistic 169 and proportional hazards (Cox) regression 170 are based on ratio measures.…”

Section: Explanationcontrasting

confidence: 55%

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration

et al. 2007

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Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalizability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies.The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers.This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated web site (http://www.strobe-statement.org) should be helpful resources to improve reporting of observational research. (Epidemiology 2007;18: 805-835) R ational health care practices require knowledge about the etiology and pathogenesis, diagnosis, prognosis and treatment of diseases. Randomized trials provide valuable evidence about treatments and other interventions. However, much of clinical or public health knowledge comes from observational research. 1 About nine of ten research papers published in clinical speciality journals describe observational research. 2,3 The STROBE StatementReporting of observational research is often not detailed and clear enough to assess the strengths and weaknesses of the investigation. 4,5 To improve the reporting of observational research, we developed a checklist of items that should be addressed: the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement (Table 1). Items relate to title, abstract, introduction, methods, results and discussion sections of articles. The STROBE Statement has recently been published in several journals. 6 Our aim is to ensure clear presentation of what was planned, done, and found in an observational study. We stress that the recommendations are not prescriptions for setting up or conducting studies, nor do they dictate methodology or mandate a uniform presentation.STROBE provides general reporting recommendations for descriptive observational studies and studies that investigate 805associations between expos...

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Section: Explanationcontrasting

confidence: 55%

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration

et al. 2007

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“…Of note, the FRS applicability among Puerto Ricans yielded a modest AUC, suggesting that the diagnostic performance of the FRS might still be inferior among Hispanics versus Caucasians, even after recalibration. These results are consistent with other studies that have tried to implement the FRS in populations other than the one in which it was derived (13,(25)(26)(27). In China, Liu and colleagues (14) compared the performance of the FRS to that of a local risk-prediction equation derived from a local cohort.…”

Section: Discussionsupporting

confidence: 79%

Validity of cardiovascular risk prediction models in Latin America and among Hispanics in the United States of America: a systematic review

Cortés-Bergoderi¹,

Thomas²,

Albuquerque³

et al. 2012

Rev Panam Salud Publica

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. MeSH search terms and domains were related to CVD, prediction rules, Latin America (including the Caribbean), and Hispanics in the United States. Database searches were supplemented by correspondence with experts in the field. 1.68 (95% CI, incident myocardial infarction, 1.36 (95% CI, and CHD death, 1.21 (95% CI,. In Ecuador, a prediction model for CVD and total deaths in hypertensive patients had an area under the curve (AUC) of 0.79 (95% CI,, while the World Health Organization method had an AUC of 0.74 (95% CI,. A study predicting mortality risk in people with Chagas' disease had an AUC of 0.81 (95% CI, According to the World Health Organization (WHO), about 25% of all annual deaths in Latin America are due to cardiovascular diseases (CVD) (2, 3). Cardiovascular risk factors, including obesity, hypertension, hypercholesterolemia, diabetes mellitus, and tobacco use are highly prevalent in Latin America and contribute to three-fourths of its CVD cases (4). The prevalence of most, if not all, of these factors has been steadily increasing in recent years in Latin America, and the trend is expected to continue (3). Results. A total of 1 655 abstracts were identified, of which five cohorts with a total of 13 142 subjects met inclusion criteria. A Mexican cohort showed that the predicted/observed event-rate ratio for coronary heart disease (CHD) according to the Framingham risk score (FRS) wasData regarding CVD burden and trends among Hispanics living in the United States is also alarming and represents the most likely cause of death among this population (5), about 31% of all deaths (6). The prevalence of heart disease is 8.1% in Hispanics and 12.1% in Caucasians (7) and the prevalence of obesity (8), diabetes mellitus, hypertension, and dyslipidemia, is generally higher among Hispanics than Caucasians (7, 9).Estimation of CVD risk using prediction models is considered of high importance to public health since it can aid clinical decision-making, guide therapy, and make interventions more cost effective. It may also help direct public policy when measuring risk trends at the population level (10). In the last few decades, several cardiovascular risk prediction models have been created and introduced worldwide.The Framingham risk score (FRS) was the first of such tools and is arguably the most commonly used model for calculating 10-year risk for CVD events in the world. The FRS has been validated and recalibrated in several different populations, including Australia, China, Japan, New Zealand, Spain, and the United States (11)(12)(13)(14). These studies have shown that the FRS may overestimate CVD risk in some populations and under estimate it in others. Some other prediction models include the Prospective Cardiovascular Munster Heart Study (PROCAM) (15), the Reynolds score (16), and the Systematic Coronary Risk Evaluation system (SCORE) (17).The aim of this study was to assess the use and validity of CVD risk prediction models in Latin America and the Caribbean and among Hispanic populations in the United Sta...

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“…All models were adjusted for age (in age categories) and for study centre to allow for the fact that the absolute risk varies from one population to another [11,17,18,19,20,21,22,23,24]. All factors were included as categories in the risk score to allow for possible non-linear effects of the factors and so that a risk score can be evaluated for a given individual by simply summing scores corresponding with the categories for each factor.…”

Section: Methodsmentioning

confidence: 99%

“…In 2003 the European SCORE project published their risk score based on pooled data from more than 200,000 men and women [1,17]. The Framingham and other risk scores have been tested in different populations [11,15,18,19,20,21,22,23,24]. The general conclusion from the comparative studies is that while the absolute risk may differ from population to population, the risk ranking provided by these scores is consistent over populations.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the risk of cardiovascular mortality using a score that includes glucose as a risk factor. The DECODE Study

Balkau

Hu²,

Qiao³

et al. 2004

Diabetologia

139

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Aims/hypothesis. Risk scores have been developed to predict cardiovascular or coronary risk, and while most have included diabetes as a risk factor, none have included lower glucose concentrations, either at fasting or following a 2-h oral glucose tolerance test. This article develops 5-and 10-year risk scores for cardiovascular mortality that include glucose concentrations as well as known diabetes status. Methods. Data is from the DECODE cohort: 16,506 men and 8,907 women from 14 European studies. The risk factors studied were as follows: age, fasting and 2-h glucose (including cases of known diabetes), fasting glucose alone (including cases of known diabetes), cholesterol, smoking status, systolic blood pressure and BMI. For an absolute risk score the 1995 countryand sex-specific cardiovascular death rates were used. Results. In men, for both 5-and 10-year cardiovascular mortality, after adjusting for age and study centre, all studied risk factors, except BMI, were significantly associated with cardiovascular mortality (p<0.05). These results were unchanged in multivariate models with all factors included. In women, after adjusting for age and centre, glucose categories, systolic blood pressure and BMI were predictive of 5-year cardiovascular mortality. With all factors in the model, only age and glucose categories were predictive. In terms of 10-year cardiovascular mortality, smoking status and blood pressures were also predictive in the women. For men and women, the same scores were used for the risk factors, except for age and glucose categories where the hazard ratios differed significantly. Conclusions/interpretation. Including glucose concentrations as well as diabetic status provides quantitative information on cardiovascular risk prediction.

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Are the Framingham and PROCAM coronary heart disease risk functions applicable to different European populations? The PRIME Study

Abstract: The Framingham and PROCAM risk functions should not be used to estimate the absolute CHD risk of middle-aged men in Belfast and France without any CHD history because of a clear overestimation. Specific population risk functions are needed.

Cited by 226 publications

References 24 publications

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration

Validity of cardiovascular risk prediction models in Latin America and among Hispanics in the United States of America: a systematic review

Prediction of the risk of cardiovascular mortality using a score that includes glucose as a risk factor. The DECODE Study

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