Effects of a global risk educational tool on primary coronary prevention: the Atherosclerosis Assessment Via Total Risk (AVIATOR) study

Jacobson, Terry A.; Gutkin, Stephen W.; Harper, Charles R.

doi:10.1185/030079906x104605

Cited by 25 publications

(34 citation statements)

References 18 publications

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“…While this alone was not surprising, the magnitude of statin treatment intensity in both the control and experimental groups for lower risk vignettes in the study (CV risk of 2% and 7%) were particularly striking in our national sample, contrasting with much lower statin prescribing rates recently reported by PCPs at a single academic center [15]. In addition, the relative increases in treatment intensity in the experimental group for the high risk vignettes (CV risk of 21% and 22%) were much higher than in other studies of risk presentation to PCPs ([15,21,22]. Our results are consistent with, but cannot prove, that providers over-value high and low LDL levels, resulting in over-estimating CV risk in high LDL subjects and under-estimating risk in low LDL patients.…”

Section: Discussioncontrasting

confidence: 87%

Providing clinicians with a patient’s 10-year cardiovascular risk improves their statin prescribing: a true experiment using clinical vignettes

Sekaran

Sussman

et al. 2013

BMC Cardiovasc Disord

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Background: Statins are effective for primary prevention of cardiovascular (CV) disease, the leading cause of death in the world. Multinational guidelines emphasize CV risk as an important factor for optimal statin prescribing. However, it's not clear how primary care providers (PCPs) use this information. The objective of this study was to determine how primary care providers use information about global CV risk for primary prevention of CV disease. Methods: A double-blinded, randomized experiment using clinical vignettes mailed to office-based PCPs in the United States who were identified through the American Medical Association Physician Masterfile in June 2012. PCPs in the control group received clinical vignettes with all information on the risk factors needed to calculate CV risk. The experimental group received the same vignettes in addition to the subject's 10-year calculated CV risk (Framingham risk score). The primary study outcome was the decision to prescribe a statin.

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Section: Discussioncontrasting

confidence: 87%

Providing clinicians with a patient’s 10-year cardiovascular risk improves their statin prescribing: a true experiment using clinical vignettes

Sekaran

Sussman

et al. 2013

BMC Cardiovasc Disord

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“…The AVIATOR study was a randomised trial conducted in the general medical clinics of Grady Memorial Hospital in the USA 39. It was designed to test the hypothesis that global risk assessment could help doctors to determine individuals at high-risk of CHD and subsequently, based on these predicted risks, better target preventive treatments.…”

Section: Empirical Examples Of Impact Studiesmentioning

confidence: 99%

Risk prediction models: II. External validation, model updating, and impact assessment

Moons

Kengne

Grobbee

et al. 2012

Heart

925

952

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Clinical prediction models are increasingly used to complement clinical reasoning and decision-making in modern medicine, in general, and in the cardiovascular domain, in particular. To these ends, developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in the targeted individuals. Subsequently, the adoption of such models by professionals must guide their decision-making, and improve patient outcomes and the cost-effectiveness of care. In the first paper of this series of two companion papers, issues relating to prediction model development, their internal validation, and estimating the added value of a new (bio)marker to existing predictors were discussed. In this second paper, an overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals (external validation studies), how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies). Each step is illustrated with empirical examples from the cardiovascular field.

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“…To our knowledge, no study has addressed this question exclusively among individuals with diabetes. In the US-based Atherosclerosis Assessment Via Total Risk trial (AVIATOR) [43], which included 368 participants from the general population, free of prior CVD and not on statin therapy, who were randomly assigned to intervention and control arms. In the intervention group, the 10-year absolute CVD risk was computed using the Framingham equation.…”

Section: Effect Of the Use Of Cvd Risk Tools On Provider And Patiementioning

confidence: 99%

“…Among the low-risk group (<10%), there was a significant higher level of statin prescription in the intervention group. Physicians in the intervention group tended to recommend smoking cessation and those in the control group to recommend dietary changes [43]. More recently, a UK-based study examined the effect of CVD risk communication on patients' health-related behaviors.…”

Section: Effect Of the Use Of Cvd Risk Tools On Provider And Patiementioning

confidence: 99%

Estimation of Absolute Cardiovascular Risk in Individuals with Diabetes Mellitus: Rationale and Approaches

2011

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Purpose. To examine the usefulness of cardiovascular risk estimation models in people with diabetes. Methods. Review of published studies that compare the discriminative power of major cardiovascular risk factors single or in combination in individuals with and without diabetes, for major cardiovascular outcomes. Results. In individuals with and without diabetes, major risk factors affect cardiovascular risk similarly, with no evidence of any significant interaction. Accounting for diabetes-specific parameters, cardiopreventative therapies can significantly improve risk estimation in diabetes. General and diabetes-specific cardiovascular risk models have a useful discriminative power, but tend to overestimate risk in individuals with diabetes. Their impact on care delivery, adherence to therapies, and patients' outcome remain poorly understood. Conclusions. The high-risk status conferred by diabetes does not preclude the estimation of absolute cardiovascular risk estimation using global risk tools in individuals with diabetes, as this is useful for the initiation and intensification of preventive measures.

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Effects of a global risk educational tool on primary coronary prevention: the Atherosclerosis Assessment Via Total Risk (AVIATOR) study

Cited by 25 publications

References 18 publications

Providing clinicians with a patient’s 10-year cardiovascular risk improves their statin prescribing: a true experiment using clinical vignettes

Providing clinicians with a patient’s 10-year cardiovascular risk improves their statin prescribing: a true experiment using clinical vignettes

Risk prediction models: II. External validation, model updating, and impact assessment

Estimation of Absolute Cardiovascular Risk in Individuals with Diabetes Mellitus: Rationale and Approaches

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