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Of the 112,156 patients classified as high risk (that is, >or=20% risk over 10 years) by the modified Framingham score, 46,094 (41.1%) would be reclassified at low risk with QRISK2. Safety Risk Score Calculator Software Credit Score Calculator v.1.0 The following is a calculator that estimates your credit score to help you determine what kind of interest rates you can expect when borrowing money. Section Editor: Bernard J Gersh, MB, ChB, DPhil, FRCP, MACC. Cardiovascular life expectancy model or Framingham risk score. The JBS3 risk calculator extends the. This is known as the modified Framingham Risk Score.3. In the “points” column enter the appropriate value according to the patient’s age, HDL-C, total cholesterol, systolic blood pressure, and if they smoke or have diabetes. Calculate the total points. As the modified Framingham Risk Score.3 Step 1 1 In the “points” column enter the appropriate value according to the patient’s age, HDL-C, total cholesterol, systolic blood pressure, and if they smoke or have diabetes. Calculate the total points. Step 2 Using the total points from Step 1, determine the 10-year CVD risk* (%). For estimation of 10-year Cardiovascular Disease (CVD) Risk to aid in decision to initiate lipid-lowering therapy. Framingham Risk Score (FRS) Calculator Gender.

Abstract

Objective

To review the 2009 Canadian Cardiovascular Society guidelines and provide practical recommendations for physicians.

Sources of information

Initial review of the references provided with the guidelines led to a search of the PubMed, ACP Journal Club, and Cochrane databases using the key words primary prevention and statin for English-language clinical trials, randomized controlled trials, meta-analyses, and reviews conducted with human participants. References from appropriate retrieved articles were also reviewed.

Main message

The guidelines outline low-density lipoprotein cholesterol (LDL-C) thresholds and targets to inform optimal use of statins in the primary prevention of cardiovascular disease (CVD). Family history of CVD and levels of high-sensitivity C-reactive protein (hsCRP) are risk modifiers in calculating the risk score with the new recommendations. An electronic calculator has been developed to facilitate increased uptake of these guidelines. Large numbers of asymptomatic people, particularly the elderly, will become eligible for statin therapy according to these new guidelines. Poor uptake by physicians and patients might result from the need for repeated testing of hsCRP and LDL-C levels in people who do not perceive themselves to be ill. Controversy persists concerning the role of hsCRP in the reclassification of CVD risk, and the concept of treating LDL-C to target has never been tested as an independent variable in a randomized trial. As two-thirds of the LDL-C lowering achieved by a statin occurs at the initial dose, it might be possible to achieve considerable CVD risk reduction for those at risk by treating initially with a mid-dose statin without LDL-C follow-up.

Conclusion

A simplified approach might appeal to patients or physicians who find current guidelines too complex, cumbersome, or costly. Success in getting high-risk patients to take statins is key to achieving improved CVD mortality reduction.

Résumé

Objectif

Revoir les directives 2009 de la Société canadienne de cardiologie et fournir des recommandations pratiques aux médecins.

Sources de l’information

Une revue initiale des références fournies par les directives nous a amenés à consulter PubMED, l’ACP Journal Club et la base de données Cochrane à l’aide des rubriques primary prevention et statin pour repérer les essais cliniques, essais cliniques randomisés, méta-analyses et revues de langue anglaises portant sur des humains. On a également révisé les références des articles pertinents identifiés.

Principal message

Les directives précisent les seuils et les cibles pour le cholestérol lié aux lipoprotéines de basse densité (LDL-C) afin de faire connaître l’utilisation optimale des statines dans la prévention primaire des maladies cardiovasculaires (MCV). Une histoire familiale de MCV et des niveaux élevés de la protéine-C réactive hautement sensible (hsCRP) sont des éléments qui interviennent dans le calcul du score de risque selon les nouvelles recommandations. Un calculateur électronique a été développé pour faciliter une meilleure adhésion à ces directives. D’après ces directives, bon nombre de sujets asymptomatiques, notamment les personnes âgées, vont devenir candidats pour un traitement aux statines. Une adhésion insuffisante de la part du médecin ou du patient pourrait être due à la nécessité de répéter les dosages de la hsCRP et du LDL-C chez des sujets qui ne se considèrent pas malades. Le rôle de la hsCRP dans la détermination du risque de MCV demeure controversé et le concept de traiter le LDL-C en fonction de cibles n’a jamais été testé en tant que variable indépendante dans un essai randomisé. Étant donné que, dans une proportion de deux sur trois, la réduction du LDL-C causée par une statine survient à la dose initiale, on pourrait peut-être obtenir une réduction considérable du risque de MCV chez les personnes à risque en commençant par une dose de statine intermédiaire, sans suivi du LDL-C.

Conclusion

Une approche simplifiée pourrait s’avérer intéressante pour les patients ou les médecins qui trouvent les directives actuelles trop complexes, trop exigeantes ou trop coûteuses. Il est crucial de convaincre les patients à risque élevé de prendre des statines si on veut obtenir une meilleure réduction de la mortalité par MCV.

Case description

Ms M.E. is a 61-year-old recently retired real estate agent who presents with general health concerns, as she feels she is unfit and somewhat overweight. Her body mass index is 28 kg/m². Blood pressure is 145/95 mm Hg, and she is not taking any medication. Findings of physical examination are otherwise unremarkable. She has never smoked and gives no personal or family history of diabetes. Two uncles were known to have heart disease, but both parents died in their eighties of other causes.

Results of laboratory work include a fasting blood sugar level of 5.6 mmol/L, total cholesterol of 6.50 mmol/L, a high-density lipoprotein cholesterol (HDL-C) level of 1.25 mmol/L, a low-density lipoprotein cholesterol (LDL-C) level of 3.26 mmol/L, a triglyceride level of 2.65 mmol/L, and a ratio of total cholesterol to HDL-C of 5.2 mmol/L.

You explore her motivation to begin a meaningful commitment to exercise, and she agrees to a referral to a dietitian. Your old Framingham calculator indicates a 13% risk for all cardiovascular events and a threshold LDL-C level of 4.13 mmol/L for initiation of lipid-lowering therapy. You discuss the modest benefit of acetylsalicylic acid (ASA) for primary prevention and resolve to become familiar with the new Canadian dyslipidemia guidelines before her next visit.

Sources of information

References provided with the 2009 Canadian Cardiovascular Society (CCS) guidelines were initially reviewed. PubMed was searched using the key words primary prevention and statin, restricted to English-language clinical trials, randomized controlled trials, meta-analyses, and reviews conducted with human subjects. The ACP Journal Club and Cochrane databases were searched using the same key words. References from appropriate retrieved articles were also reviewed.

Risk score derivation

The Framingham risk score (FRS) has evolved in North America as a validated means of predicting cardiovascular disease (CVD) risk in asymptomatic patients. More recently, tables have been developed to help predict all aspects of CVD risk. Input variables are easily obtained from office history, physical examination findings, and basic laboratory evaluations. A 10-year risk score can be derived as a percentage, which can then be used to inform the decision about initiating lipid-lowering therapy for primary prevention. Risk is considered low if the FRS is less than 10%, moderate if it is 10% to 19%, and high if it is 20% or higher.

Decisions based on the Framingham tables are made every day in office practice. In 2009 the CCS published a new set of guidelines, which coupled the new Framingham algorithms with enhanced modifiers for subsets of patients. These modifiers included family history of coronary artery disease before age 60 in a first-degree relative, and evaluation of high-sensitivity C-reactive protein (hsCRP) levels in older patients at moderate 10-year risk of CVD. The FRS has been validated in Canada.

Value of primary prevention

Secondary prevention of CVD with statins is effective. Absolute risk is high and relative numbers of events are also high. Primary prevention using statins is a more population-based strategy; a lower absolute risk of CVD exists among these asymptomatic individuals, but numerous cardiovascular events still occur. Patients with the highest risk scores benefit most from statin therapy.^, There is, however, a 20% reduction in relative mortality risk for every 1-mmol/L reduction in LDL-C levels, no matter how high the initial lipid level might be. This implies that treating patients who have high risk scores and normal lipid levels can reduce mortality, and this has been demonstrated.^, Screening of appropriate patients (Box 1) is therefore important in order to identify those who might benefit from preventive measures.

Box 1.

Screen the following patients for cardiovascular disease:

• Men aged 40 y and older

• Women aged 50 y and older or postmenopausal women

• Children with a family history of hypercholesterolemia or chylomicronemia

Screen all patients with the following conditions regardless of age:

• Diabetes

• Hypertension

• Current cigarette smoking

• Obesity

• Family history of premature CAD (< 60 y in first-degree relative)

• Inflammatory disease (SLE, rheumatoid arthritis, psoriasis)

• Chronic renal disease (eGFR < 60 mL/min/1.73 m²)

• Clinical atherosclerosis

• HIV infection treated using highly active retroviral therapy

• Clinical manifestations of hyperlipidemia (xanthomas, xanthelasmas, premature arcus cornealis)

• Erectile dysfunction

CAD—coronary artery disease, eGFR—estimated glomerular filtration rate, HIV—human immunodeficiency virus, SLE—systemic lupus erythematosus.

The concept that relative risk reduction with statins is similar for patients all the way down to those at 5% 10-year CVD risk (with much larger numbers needed to treat) comes from the JUPITER study (Justification for the Use of Statins in Primary Prevention: an Intervention Trial Evaluating Rosuvastatin), which has generated many concerns related to its methodology.^– Most guidelines apply a higher treatment threshold to try to achieve an acceptable risk-benefit ratio and to avoid treating patients who might have very small absolute event reductions from statin therapy.

Treating larger numbers of patients at lower absolute risk also requires that statin therapy have few side effects.^, Although statins seem to be relatively safe,^, there are emerging concerns, such as those over increased myalgia with exercise^, and increased vascular events on sudden discontinuation of the medication.

Most reviews support the use of statins in the primary prevention of CVD.^,,,, Benefit has recently been questioned in women and in the elderly, however, and a recent meta-analysis was unable to show overall mortality reduction in primary prevention trials in which patients with existing CVD had been carefully excluded. It seems reasonable, therefore, to direct statin therapy in primary prevention toward patients with higher FRSs rather than those who simply have high lipid levels.

Evolving importance of risk factors

Differences in risk scoring between the 2006 and 2009 CCS guidelines reflect, in part, the inclusion of all vascular end points in the risk equation. In addition to cardiac death and infarction, end points also include stroke, peripheral vascular disease, and heart failure. Risk scores expressed as percentages over 10 years are therefore going to be higher. Table 1 outlines the changes in risk scoring assigned to various risk factors.

Table 1

Evolution of risk-factor scoring from the 2006 to 2009 CCS guidelines

CAD—coronary artery disease, CCS—Canadian Cardiovascular Society, CVD—cardiovascular disease, HDL-C–high-density lipoprotein cholesterol, hsCRP—high-sensitivity C-reactive protein, SBP—systolic blood pressure.

Patients with diabetes are not automatically considered to be at high risk of CVD according to statin guidelines. Many can be scored the same as patients without diabetes, but the presence of at least 1 cardiac risk factor, or age older than 45 years in men and 50 years in women, does move them to high-risk status.

Problem of LDL-C targets

Target LDL-C levels comprise the new treatment goals, and, although they are simplified, they are more ambitious (Table 2). They represent a “treat to LDL-C target” approach, which has been criticized because no statin trial to date has demonstrated that lowering LDL-C to target levels improves CVD outcomes.^,, Randomization in statin trials has been by type of statin treatment not by LDL-C targets. Further, use of LDL-C targets disregards nonlipid effects of statins on inflammation, thrombosis, and oxidation. All-or-nothing targets coupled with performance measures provide strong incentives for overtreatment, not only with high-dose statins, but also with drugs with unproven mortality benefits such as ezetimibe.

Table 2

Grades and levels of evidence for each target are shown in bold. Classes and levels of evidence are summarized below. Clinicians should exercise judgement when implementing lipid-lowering therapy. Lifestyle modifications will have an important long-term impact on health and the long-term effects of pharmacotherapy must be weighed against potential side effects. Meta-analysis of statin trials show that for each 1.0 mmol/L decrease in low-density lipoprotein cholesterol (LDL-C), there is a corresponding RR reduction of 20% to 25%. Intensive LDL-C lowering therapy is associated with decreased cardiovascular risk. Those whose 10-year risk for cardiovascular disease (CVD) is estimated to be between 5% and 9% have been shown in randomized clinical trials to achieve the same RR reduction from statin therapy as those at a higher 10-year risk (25% to 50% reduction in events), but the absolute benefit of therapy is estimated to be smaller (in the order of 1% to 5% reduction in CVD), the numbers needed to treat to prevent one cardiac event are higher and the cost/benefit ratio of therapy is less favourable than for those at higher risk for CVD. For individuals in this category, the physician is advised to discuss these issues with the patient and, taking into account the patient’s desire to initiate long-term preventive cholesterol-lowering therapy, to individualize the treatment decision.

*Atherosclerosis in any vascular bed, including carotid arteries. apoB Apolipoprotein B level; CAD Coronary artery disease; FRS Framingham risk score; HDL-C High-density lipoprotein cholesterol; hs-CRP High-sensitivity C-reactive protein; PVD Peripheral vascular disease; RRS Reynolds Risk Score; TC Total cholesterol

This table was originally published in Can J Cardiol 2009;25(10):567–9. Reproduced with permission.

Treatment thresholds for LDL-C have been identified for the 3 levels of 10-year risk. The threshold of 3.4 mmol/L for those at moderate risk comes from the ASCOT study (Anglo-Scandinavian Cardiac Outcomes Trial), which studied only patients with 3 or 4 CVD risk factors and cannot reflect the needs of the many people in this category who are at lower risk.

It has been shown that two-thirds of the lipid-lowering effect of any statin is realized at the starting dose. Thereafter, doubling the dose of a statin will only lower LDL-C levels by a further 4% to 7%.^– While it is acknowledged that patients with established CVD, or those at high risk of CVD, will benefit from high-intensity statin therapy, there is no good evidence for treating to a specific LDL-C target.^, To ascertain optimal dosing, Hayward and colleagues used a simulated model of population-level effects of statin therapy, using 40 mg of simvastatin for patients at 5% to 15% CVD risk and 40 mg of atorvastatin for patients at greater than 15% risk. Compared with a treat-to-target approach, this strategy resulted in a considerable saving of life-years at lower cost, while treating fewer patients with high-dose statins. In view of the lack of evidence for LDL-C targets, laboratory follow-up was only suggested to assure medication safety, reducing time and expense in follow-up.

This model reduces the number of patients treated with high-dose, high-potency statins while reducing cardiovascular mortality at least as effectively. The concept requires prospective controlled trials for validation.

Problem of hsCRP

Physician compliance with lipid guidelines has in the past been suboptimal in Canada. Adding another test along with a complex algorithm incorporating appropriate use is unlikely to improve this situation. Besides being an acute-phase reactant, hsCRP, much like blood pressure, shows considerable within-subject variability, with a standard deviation of 1.2 mg/L. Such variation is sufficient in itself to reassign a patient to a different level of treatment according to current guidelines. Even accepting the values obtained, adding hsCRP to the standard FRS produces changes that are small and inconsistent, and it seems unlikely that the increase in cost and complexity is warranted. There is also prospective evidence that hsCRP level is significantly related to risk factors already in use, including smoking status, blood pressure, and glucose and cholesterol levels.

It was shown in the JUPITER trial that treating older patients at moderate risk, with LDL-C levels below 3.4 mmol/L and hsCRP levels greater than 2 mg/L, with high-dose rosuvastatin reduced the number of CVD end points. The trial did not compare hsCRP testing with no testing, nor did it compare outcomes of those with high versus low levels of hsCRP. There is at present poor evidence of the contribution of hsCRP to the reduction of CVD events.

Problem of evaluation

Many of the trials used to derive cardiovascular end points also involve secondary prevention.^– Treatment recommendations for primary prevention in patients at lower risk might be inappropriate if they are derived from secondary prevention trials.

As guidelines start to use more subgroup analyses and cost-benefit considerations, it becomes difficult to remember age cutoffs and targets for such variables as sex, presence of diabetes, hsCRP levels, and family history. Framingham tables and text are adequate guides, but they are time-consuming and difficult to retrieve. A search of the Internet found no electronic tool appropriate for the new CCS guidelines. The Reynolds risk score (RRS) includes the more recently added factors of family history and hsCRP levels, but yields different values when compared with the new CCS guidelines based on the FRS. The RRS is validated in the United States but has not yet been validated in Canada.

The treat-to-target approach leads to repetitive testing to determine if LDL-C goals have been met, despite an absence of evidence that such goals are important. Adding hsCRP testing on at least 2 occasions for selected subgroups adds further to expense and complexity.

Problems of advocacy and adherence

As guidelines evolve and the population ages, large numbers of patients without known disease will be identified as being at risk and will have indications for statin therapy. Age is by far the largest contributor to the FRS.

The cost of statins will become an increasing burden to individuals and to society, having long-term financial consequences for both. It has been shown even at current levels of advocacy that fewer than 50% of patients take 80% or more of their prescribed statin dosages. Thus, we need to continue to clarify which people actually derive net benefit from statin therapy so that we can advocate more effectively and, perhaps, achieve improved compliance.

Practical alternatives

Practical application of statin therapy can follow 2 courses, one supported by guidelines, the other by expediency (Table 3):

Table 3

FRS—Framingham risk score, hsCRP—high-sensitivity C-reactive protein, LDL-C—Low density lipoprotein cholesterol.

Treat-to-target approach using LDL-C as a surrogate goal

The best evidence and clinical support comes from the 2009 CCS guidelines. Complex new guidelines should be accompanied by accessible application tools available electronically. This should comprise electronic decision support as well as simple calculation. I have developed a tool for use with the 2009 CCS guidelines that is available for use until an authorized version appears. It will calculate risk scores using the new algorithms. It will also flag patients with diabetes who become high risk, patients who might benefit from ASA therapy, and patients who might be reclassified by measuring hsCRP levels, although hsCRP entry is optional. Family history is included in the calculation. Treatment thresholds and targets are specified. This allows rapid use of statin and ASA guidelines⁴¹ without reference to tables. It runs in Firefox, Google Chrome, or Internet Explorer and requires that JavaScript be enabled. It is available at www.palmedpage.com. Files can be downloaded for use on local computers. With use of this calculator it quickly becomes clear that large numbers of people, particularly the elderly, become candidates for statin therapy.

Expedient approach when adherence or persistence is a problem

The most important issue is that a patient at considerable 10-year risk be given a statin, with the realization that most of the benefit will be achieved at the initial dose. If the physician or patient resists repeated hsCRP testing or follow-up LDL-C testing, or therapy is discontinued because of cost or complexity, the evidence does support submaximal dosing with less intensive LDL-C monitoring. The FRS could be calculated without hsCRP testing, and, if statin therapy were indicated, 40 mg of simvastatin (if the FRS were < 15%) or 40 mg of atorvastatin (if the FRS were > 15%) could be given. Starting with higher doses seems to be well tolerated, and repeat visits for dose adjustment, which are so often met with reduced compliance, are avoided. Because doses are not maximized, the 80-mg formulations can be split, leading to an almost 50% reduction in costs, as the prices of 80-mg and 40-mg tablets are very similar. This strategy could result in more patients beginning and remaining on statin therapy, which is the outcome most likely to improve mortality.

Conclusion

New CCS guidelines provide consistency and professional support in CVD prevention. A calculator has been developed to facilitate implementation. Evidence and opinion vary in their support of treating to target LDL-C levels and use of hsCRP measurement in risk evaluation. Because most outcome benefit is seen at the initial dose, there is supporting evidence that when guideline uptake is suboptimal, patients derive substantial benefit from an empirical mid-dose statin without LDL-C monitoring.

Case revisited

Ms M.E. returns in 3 weeks. She has seen the dietitian and is restricting salt and calories. She is walking 2 km each day and complains about her knees. Her weight is unchanged. Blood pressure is 140/90 mm Hg and several home blood pressure readings are below 135/85 mm Hg.

You have found the new CCS guidelines and ordered her hsCRP level be tested; results show levels of 5.25 mg/L and 5.70 mg/L taken 2 weeks apart.

Her 10-year CVD risk using the new tables was 13.7%. It is now 13.0% with a lower blood pressure. Being older than 60 and having a high hsCRP level places her at moderate risk. Despite her moderate LDL-C level of 3.26 mmol/L, guidelines recommend further LDL-C lowering to 2.0 mmol/L. She is also a candidate for ASA therapy, although evidence for this is not robust.

Framingham Risk Calculator

You discuss this with Ms M.E., and she indicates that she is willing to take ASA but that she is not ready to take a statin. She believes that she can continue the diet and exercise program and perhaps reach her lipid goal with this lifestyle modification. You agree on a 6-month trial of diet and exercise and further consideration of the need for statin therapy at that time. You point out that if medication is eventually needed, a moderate dose of a generic drug might suffice, provided that she adheres to her diet and exercise program.

Notes

KEY POINTS

The 2009 Canadian Cardiovascular Society (CCS) guidelines provide consistency and professional support in cardiovascular disease prevention, and the author has developed a free calculator (available at www.palmedpage.com) to facilitate their implementation. The treat-to-target approach leads to repetitive testing to determine if low-density lipoprotein cholesterol goals have been met, despite an absence of evidence that such goals are important. Adding high-sensitivity C-reactive protein testing on at least 2 occasions for selected subgroups, as the newer guidelines suggest, adds further to expense and complexity. Most outcome benefit is seen at the initial dose of statin therapy, and there is supporting evidence that when guideline uptake is suboptimal, patients derive substantial benefit from an empirical mid-dose statin without monitoring of low-density lipoprotein cholesterol levels.

Framingham Risk Score Calculator Pdf Editor Free

Footnotes

This article has been peer reviewed.

Competing interests

None declared

Cet article a fait l’objet d’une révision par des pairs.

This article is eligible for Mainpro-M1 credits. To earn credits, go to www.cfp.ca and click on the Mainpro link.

References

Abstract

PURPOSE

To examine the features of available Framingham-based risk calculation tools and review their accuracy and feasibility in clinical practice.

DATA SOURCES

medline, 1966–April 2003, and the google search engine on the Internet.

TOOL AND STUDY SELECTION

We included risk calculation tools that used the Framingham risk equations to generate a global coronary heart disease (CHD) risk. To determine tool accuracy, we reviewed all articles that compared the performance of various Framingham-based risk tools to that of the continuous Framingham risk equations. To determine the feasibility of tool use in clinical practice, we reviewed articles on the availability of the risk factor information required for risk calculation, subjective preference for 1 risk calculator over another, or subjective ease of use.

DATA EXTRACTION

Two reviewers independently reviewed the results of the literature search, all websites, and abstracted all articles for relevant information.

DATA SYNTHESIS

Multiple CHD risk calculation tools are available, including risk charts and computerized calculators for personal digital assistants, personal computers, and web-based use. Most are easy to use and available without cost. They require information on age, smoking status, blood pressure, total and HDL cholesterol, and the presence or absence of diabetes. Compared to the full Framingham equations, accuracy for identifying patients at increased risk was generally quite high. Data on the feasibility of tool use was limited.

CONCLUSIONS

Several easy-to-use tools are available for estimating patients' CHD risk. Use of such tools could facilitate better decision making about interventions for primary prevention of CHD, but further research about their actual effect on clinical practice and patient outcomes is required.

DISCLOSURE

Drs. Sheridan and Pignone have participated in the development of Heart-to-Heart, one of the risk tools evaluated within. They have also received speaking and consulting fees from Bayer, Inc. Bayer, Inc. has licensed the Heart-to-Heart tool.

Clinical practice guidelines recommend that providers and patients base treatment decisions regarding coronary heart disease (CHD) prevention on assessment of underlying global CHD risk.^– In addition, the American Heart Association has recommended that adults aged 40 and older with no previous history of cardiovascular disease have their global CHD risk calculated every 5 years. To implement these guidelines in clinical practice, providers need an accurate and feasible means of calculating global CHD risk.

Previous research has shown that providers do not accurately estimate the risk of CHD events on their own.^– Fortunately, multivariate risk prediction equations have been developed to better estimate CHD risk. These equations have been derived from large prospective cohort studies or randomized trials^– and estimate a patient's risk of having a CHD event over 5 to 10 years. They provide better estimates of CHD risk than either assessment of single risk factors or simple counting of multiple risk factors and appear to be more cost effective in guiding CHD treatment decisions. Some of the available risk equations, however, have limitations: they include relatively few risk factors; are derived from truncated middle-aged or male-only populations; use logistic regression models that require fixed follow-up periods (e.g., 10 years); treat events occurring at 1 year the same as events occurring at 5 or 10 years; and have been prospectively validated in limited populations.

Among the various risk prediction equations, those derived from the Framingham Heart Study are most commonly recommended for use in the United States. These equations calculate the absolute risk of CHD events for patients with no known previous history of CHD, stroke, or peripheral vascular disease (primary prevention). Compared to other risk equations, the Framingham risk equations have favorable characteristics: they were developed in a large prospective cohort of U.S. men and women aged 30 to 74 years, have been subsequently validated in multiple diverse populations, and discriminate well among those who will have a CHD event and those who won't.^,– In general, the Framingham equations also predict the degree of risk well in middle-aged white and African-American adults, although hypertension is somewhat underweighted as a risk factor in African Americans (particularly for women)^, and the risk associated with diabetes mellitus is undervalued.^,, The equations predict the degree of risk less well in men and women younger than age 30 or over age 65, Japanese-American men, Hispanic men, and Native-American women.^,, They also are less precise in patients with diabetes, severe hypertension, or left ventricular hypertrophy because fewer numbers of participants in the original Framingham cohort had these risk factors.

For use in clinical practice, the Framingham equations have been operationalized into several risk assessment “tools.” Common formats of available risk tools include risk charts (simple tables or wall charts) and electronic calculators, which are available as stand-alone applications for personal computers or personal digital assistants, and web-based tools. We sought to review available CHD risk calculation tools based on Framingham equations to help guide providers in selecting the best tools for their practices.

METHODS

To identify Framingham-based CHD risk calculation tools and review their accuracy and feasibility in clinical practice, we conducted a search of medline 1966–April 2003 using the MeSH terms coronary heart disease and risk assessment. To identify web-based tools that are readily available to the clinician, we also performed an Internet search in April 2002 using a popular search engine, google, and the search term “cardiac risk calculator.” Finally, we used our own literature files, and hand-checking of identified bibliographies and web links to identify other risk tools or articles evaluating risk assessment tools.

To identify available CHD risk calculation tools, we included articles and websites that used the Framingham risk equations to generate a global CHD risk, expressed either as the proportion of similar patients who would have a CHD event over a defined time period or as the movement of a patient across a predefined treatment threshold. We excluded articles and websites that used non-Framingham risk equations, did not specify the equation used for calculation, were designed for secondary prevention, did not clearly define the calculated risk outcome, or calculated risk using nontraditional risk factors such as blood type or measures of psychological stress.

To determine the accuracy of CHD risk tools, we included articles that compared the performance of various Framingham-based risk tools to that of the continuous Framingham equation in clinical practice. We included articles that tabulated the sensitivity and specificity of the risk tools or provided enough information that these could be calculated.

Because we wanted to focus on tools available for clinical practice, we excluded articles that compared the discriminatory and predictive abilities of continuous Framingham equations including different risk factors or prospectively examined the continuous Framingham equations in large epidemiological study populations. We also excluded articles that examined the accuracy of non-Framingham-based risk tools, used a gold standard other than the continuous Framingham model, or that reported only the difference in accuracy among various provider groups.

To determine the feasibility of risk tools in clinical practice, we included articles that provided information on the availability of the risk factor information required for risk calculation, subjective preference for one risk calculator over another, or subjective ease of use of the various risk calculators.

Two of us independently reviewed the results of the literature and web searches (MP, SS) to determine article and website inclusion. We then abstracted relevant information from included articles and websites into tables for analysis (CM, MP, SS). Disagreements were resolved by discussion among team members.

We categorized the risk tools into 2 main groups: 1) risk charts (usually printed); and 2) electronic calculators, including computer programs for personal digital assistants (handheld PDAs), spreadsheet programs designed to run on personal computers, and web-based risk calculators. We then reviewed each tool to determine the required input and to characterize its output.

For studies reporting on the accuracy and feasibility of various risk calculators, we abstracted information that we felt would impact the quality of the accuracy estimates reported and their applicability to clinical practice. Specifically, we abstracted information on the identity of the risk scorer, whether they were blinded to the gold standard risk assessment, what patient population was used for risk assessment, whether all necessary patient data were available for the risk calculation, and what reference cutpoint was used to distinguish high versus low CHD risk. We made no attempt to combine these factors into an overall quality score.

RESULTS

Literature Search

Our medline search identified 1,306 articles on risk assessment for coronary heart disease and our final Internet search, conducted on April 28, 2002, identified 3,690 websites. After review of abstracts and potentially relevant articles, we included 8 articles describing Framingham-based risk calculation tools and 7 articles providing information on the accuracy and feasibility of the tools. Two independent reviewers additionally reviewed the 100 websites rated most relevant to our search by the google search engine, including 10 sites described in this report. We did not include websites with required member log-in (N = 2), nonfunctional links (N = 3), no CHD risk calculator (N = 28), non-Framingham-based calculators (N = 7), calculators including nontraditional risk factors (N = 2), calculators with unspecified risk equations (N = 5), or calculators with undefined outcomes (N = 3). Forty of the 100 sites were repeat references.

Tool Characteristics

Table 1 provides a representative, but not exhaustive, sample of available tools. Tools have a variety of formats including risk charts (simple tables or wall charts) and electronic calculators, which are available as stand-alone or web-based applications for personal computers, or as stand-alone applications for personal digital assistants. All tools require information on age, gender, total cholesterol, systolic blood pressure, and smoking status for risk calculation; most also include diabetes, assessed as a yes/no answer, and high-density lipoprotein (HDL) cholesterol. Some tools using older versions of the Framingham equations also prompt input on the presence of left ventricular hypertrophy (LVH) on electrocardiogram, although lack of this information does not preclude risk calculation.

Table 1

C, static risk chart; S, spreadsheet calculator; W, web-based calculator; H, handheld computer program; SBP, systolic blood pressure; CVD, cardiovascular disease; CHD, coronary heart disease; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; Afib, atrial fibrillation; CHF, congestive heart failure; PVD, peripheral vascular disease.

All web addresses active at time of search: April 28, 2002.

The output of the risk tools we reviewed is diverse. CHD events are defined alternately as a composite of myocardial infarction (nonfatal or fatal) and sudden death or as new-onset stable angina, unstable angina (called “coronary insufficiency” in the Framingham study), myocardial infarction, and sudden death. Some tools (e.g., Sheffield tables, Joint British charts, and Joint European charts) estimate the risk of CHD events alone, while others (e.g., New Zealand tables) give risks for CHD events and for stroke. One tool (Birmingham Heartlands Calculator) also included peripheral vascular disease as an outcome.

The presentation of CHD risk (see Fig. 1) is generally in numeric or graphic terms, with few tools including written explanation of the results. Some tools (e.g., New Zealand tables) give a point estimate of risk, whereas others provide a range of risks or simply state whether a predefined treatment threshold to initiate therapy had been exceeded (e.g., Sheffield tables). Most tools provide either a comparison to the risk of an individual of the same age or gender who has no risk factors or to an individual with “average” risk factors. Many also provide a qualitative description, such as high or low risk. A minority provide treatment advice or links to evidence-based treatment guidelines.

Examples of the information presented in Framingham-based risk calculation tools.

Risk Charts

Several different risk charts are available in print form or from the Internet. The charts (or tables) generally fall into 2 types: 1 type assigns points to various levels of each risk factor and then assigns a specific risk for the total score obtained after summing the individual scores for each risk factor (e.g., Categorical Framingham tables). The second type arrays information in various combinations of columns and rows either to allow a specific risk to be read from the chart (e.g., New Zealand tables) or to reach a treatment decision given a predefined threshold for treatment (e.g., Sheffield tables). The main advantage of tables and charts is that they do not require a computer for use. They can be downloaded, printed, or photocopied and used in any setting. The main downsides are that they may be difficult or time consuming to use at first and that they are not as accurate or precise as some of the spreadsheet or web-based calculators described below.

Tools for Personal Digital Assistants (PDAs)

Currently, few risk tools are available for handheld computers or PDAs (e.g., Stat Cardiac Risk, the National Cholesterol Education Program Palm Calculator, FramPlus, and Heart-to-Heart). Based on the updated Framingham risk equations, these programs use categorical classification of risk factors to estimate the 10-year risk of CHD. Because they use ranges, they are slightly less precise than some of the spreadsheet calculators that use exact values. On the positive side, they are portable and very easy and fast to use and can be shared with other PDA users by simply “beaming” the program via the infrared port.

Spreadsheet Calculators for Personal Computers

Spreadsheet-based calculators make the Framingham equations available in a computer program such as Microsoft Excel (Microsoft Corporation, Redmond, WA). They require that the spreadsheet program be installed on each computer that is to be used for calculating risk. One commercial product, the BMJ CardioRisk Manager, adds the capability of producing more sophisticated reports (including a letter to send results to the patient) and can archive results. It also includes a “slider bar” to allow patients and providers to see the projected effect of treatment on CHD outcomes. The expected effect of treatment is demonstrated by recalculating risk using posttreatment risk factor levels rather than by applying the best evidence about expected risk reduction to baseline calculated risk. This may be misleading because changes in risk levels with treatment do not produce the same degree of risk reduction as would be predicted from observational studies. Another calculator, the Birmingham Heartlands Calculator, does estimate the effect of treatment, by applying evidence about expected risk reduction.

Web-based Calculators

Several web-based risk calculators are available. They require that the user have Internet access, but no local software is needed other than a web browser. They can only be used effectively in practice settings that have continuous access to the Internet; establishing a dial-up connection each time the program is used is impractical. Web-based calculators generally use the full Framingham equation. Results can be printed from the browser to be placed in the medical record. Additionally, a few tools (the risk calculator from the University of Edinburgh (www.cardiacrisk.org.uk) and the Heart-to- Heart tool (www.med-decisions.com) offer the option to print individualized evidence-based treatment advice for patients.

Studies that Assess the Accuracy of Various Framingham-based Risk Calculators and the Feasibility of Their Use in Clinical Practice

We found 6 studies that compared the relative accuracy of various risk prediction charts or tables with full Framingham risk equations (Table 2).^,– Because electronic calculators use the full Framingham equation or tally scores from charts or tables, we found no studies separately examining these tools. In the studies we identified, risk assessors calculated CHD risk from data obtained from patient charts, physical examinations, and laboratory assessments; the standard for comparison was the full Framingham equation. In 2 studies,^, risk assessors were practicing clinicians with no prior knowledge of the results of the full Framingham calculation. In the remaining 4 studies, the risk assessors were computer operators, medical students, or other observers.^,,, We could not tell whether these risk assessors had prior knowledge of the Framingham equations or the risk calculation tools or whether they received any special training in their use.

Table 2

Studies that Compare Various Framingham-based Risk Tools (Charts and Tables) with Full Framingham Equation Calculations

CVD, cardiovascular disease; CHD, coronary heart disease; LVH, left ventricular hypertrophy; H amp; P, history and physical examination; cat, categories; TC, total cholesterol; HDL, high-density lipoprotein cholesterol.

Table 3 gives reported sensitivity and specificity values for the most commonly used risk assessment tools from the 6 studies. Although all studies used full Framingham equations as the reference standard, different cutpoints were sometimes used to define high-risk status or thresholds for treatment. We include the results for the most common cutpoints here. In general, the tools displayed good to excellent sensitivity and specificity for detection of patients with increased CHD risk. Only the Canadian tool had poor accuracy in predicting a risk of greater than 3% per year; it performed much better at a reference standard cutpoint of 1.5% per year (sensitivity 95%−98%).^, We make no comparisons of sensitivity and specificity findings across studies due to the varying numbers of indeterminate assessments, different reference standard cutpoints, and diverse study populations.

Table 3

CHD, coronary heart disease; CVD, cardiovascular disease.

The proportion of insufficient data available to complete the Framingham calculations varied from 5% to 49% across studies, including 11%το 49% of cases in the 1 study that relied on randomly selected patient charts for risk calculations. When data were missing, none of the study authors used mean risk factor values to estimate risk. The most common reason for inability to assess patient risk was missing HDL cholesterol values. Thus, risk assessments that do not require HDL values (Joint European charts) were completed more often than those that rely on HDL values (Joint British tables, New Zealand tables).

McManus and colleagues examined the reliability of the risk calculations of general practitioners and practice nurses. They found κ values ranging from 0.47 to 0.58, suggesting moderate reliability. In the same study, however, risk assessments were inappropriately completed for 40% of patients with known coronary heart disease, even though such patients can be classified as high-risk based on disease history alone.

We found 1 additional Scottish study that compared the calculations from 3 risk assessment tools (New Zealand table, old Sheffield table, and Joint British chart) with each other, rather than with full Framingham equation estimates, and provided information about the feasibility of using these tools in clinical practice. In this study, a self-nominated general practitioner and nurse from each of 37 general practices completed risk assessments on a set of 12 case histories that reflected varying levels of CHD risk. Doctors and nurses preferred New Zealand tables and Joint British charts over the Sheffield tables and found them easier to use. Doctors generally scored case histories with similar risks using the 3 different risk tools, while accuracy among nurses was significantly poorer with the Sheffield table compared to the 2 other tools.

DISCUSSION

Policy-making bodies increasingly agree that the most efficient and effective clinical CHD prevention requires a global assessment of CHD risk.^, Fortunately, a variety of user-friendly tools based on the Framingham equation are available to help clinicians perform CHD risk assessment for patients with no known history of cardiovascular disease. Our review suggests that, in general, the categorical charts and tables derived from the Framingham equation are accurate and feasible for use in clinical settings and can be used in lieu of the continuous Framingham calculators when necessitated by the clinical environment. This supports findings by chart developers who report similar discriminatory ability between their charts and the full Framingham equations. Some features of the computer or PDA-based tools, however, may make them a better choice for providers with access to such devices.

In deciding among available tools, providers may wish to choose tools that provide risk information in a format that can be used with current guidelines for risk reduction (see Table 4). For instance, to allow risk-based decision making about lipid-lowering therapy, providers need a tool that allows stratification of risk into <10%, 10%−20%, and >20%.^, All of the spreadsheets, PDA, and web-based calculators have this capability because all use the continuous Framingham equations or the original Framingham categorical charts. Many of the risk charts also have this capability; the notable exception is the Modified Sheffield table, which uses only 15% and 30% cutoffs. To adhere to evidence-based guidelines on aspirin use, providers need a tool with finer gradations of risk because the risk/benefit ratio for aspirin use transitions from helpful to harmful at a 10-year risk of CHD events between 3% to 5% and 10%.^, This again reduces the number of useful risk charts, but still allows many acceptable options. At present, it is unclear how providers should address risk calculation in patients with diabetes. The National Cholesterol Education Program and the American Heart Association currently recommend that physicians treat patients with diabetes as though they have a risk for subsequent CHD events that is equivalent to that in patients with known CHD.^, In accordance with this, they have recommended that their Framingham risk calculators be used only in patients without diabetes. At present, however, we are unaware of direct evidence that suggests this strategy is more effective than relying on calculated risk assessment, and many calculators continue to request input of diabetes status for risk calculations.

Table 4

Current Guidelines for Cardiovascular Risk Reduction

In choosing which risk tool to use, providers should consider their practice environment and who will be performing the risk assessments. Providers who have access to a computer with an available spreadsheet program or dedicated high-speed Internet access line should consider spreadsheet and web-based programs for risk calculation. These tools allow calculation of fine gradations of risk, frequently provide comparisons to individuals with low risk (e.g., BMJ Cardiorisk Manager, Birmingham Heartlands Calculator, National Cholesterol Education Program Risk Calculator, RiskCalculator from the Center for Cardiovascular Sciences at the University of Edinburgh, Healing Hearts Risk Calculator, Medical-decisions.com calculator, and American Heart Association Calculator), and, in some cases, provide targeted advice on treatment and allow exploration of the effects of treatment on calculated risk (e.g. BMJ Cardiorisk Manager, Birmingham Heartlands Calculator, Heart-to-Heart Calculator). Additionally, at least one of these tools (Heart-to-Heart Calculator) is targeted to patients and can be used independently of the clinician visit. For providers who do not have access to these tools, current PDA tools and risk charts offer an acceptable option.

Some providers may find that a combination of products is most useful, particularly if the outcome of interest varies according to patient concerns. Most tools provide information on the combined risk of stable and unstable angina, myocardial infarction, and CHD death. Some tools, however, report only the risk of myocardial infarction and CHD death; these tools will produce smaller numeric estimates of risk than tools that also include angina. The current NCEP risk calculator, for example, uses a set of newly revised Framingham equations that only predict the risk of myocardial infarction and CHD death. To our knowledge, these equations have not been published in the peer-reviewed literature. Other tools allow calculation of all CVD events by adding stroke outcomes (e.g., New Zealand Risk Table, British Cardiac Risk Assessor, BMJ Cardiorisk Manager, Risk Calculator from the Center for Cardiovascular Sciences at the University of Edinburgh) or by allowing independent calculation of the risk of stroke and peripheral vascular disease (e.g., Birmingham Heartlands Calculator).

In addition to choosing which type of risk tool to use, providers must ensure that they have sufficient information to complete the risk assessment. Some information, such as age, smoking status, and presence or absence of diabetes, can be obtained by interview at the time the risk calculation is performed. Other information, such as blood pressure, cholesterol levels, and presence or absence of left ventricular hypertrophy on electrocardiogram must be obtained prior to risk calculation.

Our review identified several limitations among the available Framingham tools. First, existing tools do not predict risk beyond 12 years. This is a limitation imposed by the published data available from the Framingham Heart Study. Although Framingham investigators have published data on the lifetime risk of developing coronary heart disease, they have not incorporated lifetime risk into tools for clinical risk estimation. Presentation of lifetime risks may have different effects on perceived threat and motivation to undertake risk-reducing behavior for some patients, particularly younger ones, who are making longer-term prevention decisions,^– although to date this has not been empirically studied. Second, none of the tools specify how electrocardiographic LVH is to be defined, although available evidence suggests that LVH with repolarization abnormality (strain pattern) provides the best predictive ability, and LVH by voltage criteria alone is not associated with clearly increased risk. Third, none of the tools provide confidence intervals around risk estimates. Their absence may convey a false sense of precision. Finally, most tools do not provide accurate information about the benefits and adverse effects of risk-lowering interventions, which may limit their clinical utility.

Aside from the limitations of the tools, we acknowledge the limitations of the Framingham equations themselves. Although the Framingham equations predict the degree of risk well in white and African-American men and women between the ages of 30 and 65 in the United States, they predict the degree of risk less well in non-U.S. populations, certain U.S. ethnic groups (Japanese men, Hispanic men, and Native-American women), men and women younger than age 30 or older than age 65, and diabetic persons.^,, One approach to the Framingham equations' limits is to recalibrate the tool for use in designated target populations. At present, we are not aware of any Framingham-based risk calculation tools that have attempted to do this.

The current Framingham equations have additionally attempted to balance accuracy and feasibility and hence have limited the number of risk factors required for risk estimation. They do not include the following established and potential risk factors, which may be of interest: blood glucose level, hemoglobin A1C, triglycerides, lipoprotein A, small dense low-density lipoprotein particles, homocysteine, c-reactive protein, microalbuminuria, coagulation factors, weight or body mass index, physical activity, and family history of premature cardiovascular disease. The effect of adding additional risk factors to risk calculation tools has been little studied.

As of April 2003, our searches of the medical literature also show that the effect of risk calculators on clinical practice and outcomes has not been well studied. Two studies^, suggest that providing physicians with computerized risk calculators has had little impact on CHD risk. These studies, however, provided no link to evidence-based guidelines and had important methodological limitations including high attrition rates and use in populations who already have existing CHD. A third study, in which researchers alternately wrote patient risk scores on the front of patient charts or not, also suggests the limited effects of providing physicians with only risk estimates. Whether calculating and communicating global CHD risk to patients affects their willingness or ability to change their lifestyle and use preventive medications, such as aspirin, antihypertensive drugs, or cholesterol-lowering medication, has not been well studied. Although a recent pilot study testing the combined effects of a self-guided workbook and physician visit on global CHD risk reported that 68% of users planned to make a variety of interventions on their risk as a result of using the book, traditional CHD risk appraisal has had only modest impact on actual patient behavior in the areas of diet and exercise.^– One recent study has shown reductions in CHD risk, body mass index, and cholesterol levels at 5 years follow-up in intervention groups that received CHD risk appraisal with or without physician consultation, but conclusions were limited by high attrition rates and poor participation in follow-up consultations throughout most of the study. Further research is still needed.

Research should also determine whether the inclusion of newer risk factors for CHD (i.e., lipoprotein a, homocysteine, micro-albuminuria, or c-reactive protein), or noninvasive measures of atherosclerosis, such as electron-beam computerized tomography (EBCT) or carotid Doppler ultrasound, improves risk assessment and leads to better use of CHD risk-reducing treatments. Some have suggested that these novel risk factors may be best used to modify the pretest probability estimate from the Framingham risk score, particularly for those with intermediate risk.

REFERENCES

Framingham Risk Score Calculator Cdc

Framingham Risk Score Calculator Canada