A simple risk score using routine data for predicting cardiovascular disease in primary care

Chamnan, Parinya; Simmons, Rebecca K.; Sharp, Stephen J.; Wareham, Nicholas J.; Griffin, Simon J.; Hori, Hiroyuki; Khaw, Kay‐Tee

doi:10.3399/bjgp10x515098

Cited by 12 publications

(12 citation statements)

References 24 publications

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“…However, of this high-risk population, 11% will indeed develop diabetes within the relatively short timeframe of 5 years whereas lifetime diabetes risk will be higher. Further, diabetes risk scores have also been shown to predict cardiovascular disease [41,42]. Thus, the 11% of the high-risk population that will develop diabetes within 5 years is the minimum that will benefit from timely intervention.…”

Section: Discussionmentioning

confidence: 99%

The Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes

Alssema

Vistisen

Heymans³

et al. 2010

Diabetologia

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Aims/hypothesis The Finnish diabetes risk questionnaire is a widely used, simple tool for identification of those at risk for drug-treated type 2 diabetes. We updated the risk questionnaire by using clinically diagnosed and screendetected type 2 diabetes instead of drug-treated diabetes as an endpoint and by considering additional predictors. Diabetologia (2011) 54:1004-1012 DOI 10.1007/s00125-010-1990 logistic regression model was assessed by the area under the receiver-operating characteristic (ROC) curve and by the net reclassification improvement. Internal validation was by bootstrapping techniques. Results Of the 18,301 participants, 844 developed type 2 diabetes in a period of 5 years (4.6%). The Finnish risk score had an area under the ROC curve of 0.742 (95% CI 0.726-0.758). Re-estimation of the regression coefficients improved the area under the ROC curve to 0.766 (95% CI 0.750-0.783). Additional items such as male sex, smoking and family history of diabetes (parent, sibling or both) improved the area under the ROC curve and net reclassification. Bootstrapping showed good internal validity. Conclusions/interpretation The predictive value of the original Finnish risk questionnaire could be improved by adding information on sex, smoking and family history of diabetes. The DETECT-2 update of the Finnish diabetes risk questionnaire is an adequate and robust predictor for future screen-detected and clinically diagnosed type 2 diabetes in Europid populations. Methods

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Section: Discussionmentioning

confidence: 99%

The Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes

Alssema

Vistisen

Heymans³

et al. 2010

Diabetologia

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“…Of note, it has been demonstrated in many studies that laboratory-and non-laboratory-based CVD risk assessments are similarly effective at predicting future cardiovascular disease [30][31][32]. A modelling study based on data from the EPIC-Norfolk cohort and published evidence on effectiveness of key preventive interventions clearly suggests that inviting individuals at high risk identified using routine data for a vascular risk assessment could prevent a similar number of new cardiovascular events with potential cost-saving, compared to inviting all individuals for laboratory-based CVD risk assessment [33].…”

Section: Laboratory-versus Non-laboratory-based Cvd Risk Assessmentmentioning

confidence: 99%

Cardiovascular Risk Assessment in Developing World

Chamnan¹,

Aekplakorn²

2017

Recent Trends in Cardiovascular Risks

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Many international and national authorities recommend that cardiovascular risk assessment using multivariate risk scores be used to identify individuals at high risk of cardiovascular disease (CVD). This approach is likely to assure that resources in developing countries are allocated to those who need it most. However, not many developing countries have implemented this approach and different countries have varying progresses in adopting the concept. While many developing countries solely described estimated cardiovascular risk by applying existing CVD risk scores to their population's cross-sectional data, a number of countries have validated and recalibrated existing risk scores and only a few have developed new risk scores specific to their populations. To enhance the adoption of such a policy in developing countries, new CVD risk prediction charts for low-and medium-resource settings were developed and endorsed by the WHO and International Society of Hypertension. However, a number of issues need to be addressed, including development of population-specific risk scores, recalibration of available risk scores and uncertainty over cost-effectiveness of CVD risk assessment in developing countries. Although this high risk approach might represent an effective and practical strategy for developing countries, a complementary population-based approach is also needed to maximize benefits for CVD prevention.

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“…19 Performance and predictive capacity of the Framingham equations have been analyzed with different results. 14,20,21 Framingham functions yield prediction for patients when they are not treated with lipid-lowering or antihypertensive therapy; however, a substantial fraction of patients are included in these studies even though they are being treated. 14,[19][20][21] One would expect inclusion of treated patients to lower the risk of cardiovascular disease (that is, of course, why treatment is offered), and for this reason alone, it should be expected that the prediction models are overestimating risk as they pertain to an untreated population.…”

Section: Introductionmentioning

confidence: 99%

“…14,20,21 Framingham functions yield prediction for patients when they are not treated with lipid-lowering or antihypertensive therapy; however, a substantial fraction of patients are included in these studies even though they are being treated. 14,[19][20][21] One would expect inclusion of treated patients to lower the risk of cardiovascular disease (that is, of course, why treatment is offered), and for this reason alone, it should be expected that the prediction models are overestimating risk as they pertain to an untreated population. Accordingly, for risk communication and individual decisions, cardiovascular risk should be based on study populations that do not receive additional treatment for cardiovascular disease.…”

Section: Introductionmentioning

confidence: 99%

“…[11][12][13][14] For this reason, it is recommended that the function should be calibrated and validated on large populations representative of each country's reality. [11][12][13][14][15][16][17] In this context, various systems have been used to calculate cardiovascular risk in our country, an example being the Framingham coronary risk tables (REGI-COR). 18 This function has been validated using patient data from 67 Spanish primary care centers that volunteered to participate in the study.…”

Section: Introductionmentioning

confidence: 99%

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Original and REGICOR Framingham Functions in a Nondiabetic Population of a Spanish Health Care Center: A Validation Study

Buitrago¹,

Calvo-Hueros²,

Cañón-Barroso³

et al. 2011

The Annals of Family Medicine

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PURPOSE Risk functions can help general practitioners identify patients at high cardiovascular risk, but overprediction inevitably leads to a disproportionate number of patients being targeted for treatment. To assess predicted cardiovascular risk, we analyzed the 10-year performance of the original and REGICOR Framingham coronary risk functions in nondiabetic patients. METHODSOurs was a longitudinal, observational study of a retrospective cohort of patients observed for 10 years in primary care practices in Badajoz, Spain. Our cohort comprised 447 nondiabetic patients aged 35 to 74 years who had no evidence of cardiovascular disease and were not on lipid-lowering or antihypertensive therapy. We assessed the patients' 10-year coronary risk measurement from the time of their recruitment. We also estimated the percentage of patients who were candidates for antihypertensive and lipid-lowering therapy. RESULTSThe actual incidence rate of coronary events was 6.7%. The original Framingham equation overpredicted risk by 73%, whereas the REGICOR Framingham function underpredicted risk by 64%. The Brier scores were 0.06364 and 0.06093 (P = .365) for the original Framingham and REGICOR Framingham functions, respectively, and the remaining discrimination and calibration parameters were also highly similar for both functions. The original Framingham function classifi ed 14.8% of the population as high risk and the REGICOR Framingham function classifi ed 6.9%. The proportions of patients who, according to the original Framingham and REGICOR functions, would be candidates for lipid-lowering therapy were 14.3% and 6.7%, and for antihypertensive therapy they were 12.5% and 7.8%, respectively. CONCLUSION

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A simple risk score using routine data for predicting cardiovascular disease in primary care

Cited by 12 publications

References 24 publications

The Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes

The Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes

Cardiovascular Risk Assessment in Developing World

Original and REGICOR Framingham Functions in a Nondiabetic Population of a Spanish Health Care Center: A Validation Study

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