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  2. Canadian Framingham Risk Score Calculator
  3. Framingham Risk Calculator Pdf
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  • LAYING THE FOUNDATION FOR PREVENTIVE HEALTH CARE. Framingham risk score”. The Framingham risk calculator, which in many ways changed the practice of medicine, has been refined over the years to include other risk factors, and a suite of additional risk.
  • We included observational and experimental studies that applied validated CVR tools, including Framingham risk score (FRS) with its subtypes of scores (cardiovascular disease (CVD), cardiovascular heart disease (CHD), Myocardial infarction (MI) and the Systematic Coronary Risk Evaluation (SCORE).
  • The Framingham risk score (FRS) was computed as previously described. Electron-beam computed tomography. Non-enhanced electron-beam computed tomography (EBCT) scans were performed on C-150 scanners (GE Imatron, South San Francisco, USA).
  • Framingham risk score explained. Smokers are at higher risk of angina, heart attack or stroke than non-smokers due to damage of the arterial lining, which leads to atheroma (narrowing arteries). The answers that are input in the calculator are interpreted according to the range of values they belong to, and weigh differently in the final score.
  • 10 year risk of fatal CVD in high risk regions of Europe by gender, age, systolic blood pressure, total cholesterol and smoking status SCORE - European High Risk Chart ©ESC 2018 15% and over 10% - 14% 5% - 9% 3% - 4% 2% 1% risk of fatal CVD in populations at high CVD risk SCORE 2 Non-smoker Smoker 14 16 26 11 15 6 8 9 11 13 9 15.

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.

Patients who require screening for cardiovascular disease

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

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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

RISK FACTORSCORING CHANGEIMPLICATION
SexWomen reach high risk at a lower point score (18% vs 23%); unchanged in menMight reflect inclusion of stroke risk, which is relatively higher in women
AgeAge is the main contribution to risk score—increased weighting for both sexes, but more for womenAll CVD end points are included; stroke inclusion will increase scores for women
Blood pressure (SBP)SBP has more influence on point score, and the effect is almost double for womenHypertension is an important contributor to stroke, which affects more women
SmokingPrevious tables increased scores for the young and for women; smoking now scores 4 points for men and 3 points for women, with no age differentialYounger smokers will be scored much lower than in previous guidelines
CholesterolPreviously higher point scores for younger age groups and for women; now scored the same across age groups, with women higher at the top lipid levelsLower scores for younger patients with high lipid levels
HDL-CScored similarly for both sexes; new tables subtract more points for high HDL-C levelsIncreased protection reflected in lower risk scores for those with high HDL-C levels in new tables
Family historyCAD in first-degree relative younger than 60 y of age imparts a multiple of 1.7 for women and 2.0 for men; unchanged, but seldom considered in older calculatorsMore realistic reflection of CAD risk in some patients without other important risk factors
hsCRPPossible reassignment of risk in men older than 50 y and women older than 60 y at moderate risk and with LDL-C < 3.5 mmol/L; those with hsCRP levels > 2.0 mg/L should be treated to high-risk targets according to the new recommendationsModerate-risk patients with low hsCRP levels are not treated; those with high hsCRP levels or LDL-C levels > 3.5 mmol/L are treated to high-risk targets; reflects some of the findings of the JUPITER study8
DiabetesNow a recommendation for high-risk status in men older than 45 y and women older than 50 y; younger patients are also scored as high risk if 1 other risk factor is presentPatients with diabetes are treated the same as the general population unless high-risk criteria are present

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

RISK LEVELINITIATE TREATMENT IF:PRIMARY TARGETS
LDL-CALTERNATE
High
CAD, PVD, atherosclerosis*
Most patients with diabetes
FRS ≥ 20%
RRS ≥ 20%		Consider treatment in all patients<2 mmol/L or ≥ 50% ↓ LDL-C
Class I, level A†		apoB < 0.80 g/L
Class I, level A†
Moderate
FRS 10%–19%		LDL-C > 3.5 mmol/L
TC/HDL > 5.0
hs-CRP > 2 mg/L
Men > 50 years
Women > 60 years
Family history and hs-CRP
modulates risk (RRS)		<2 mmol/L or
≥50% ↓ LDL-C
Class IIa, level A‡		apoB < 0.80 g/L
Class IIa, level A‡
Low
FRS < 10%		LDL-C ≥ 5.0 mmol/L≥ 50% ↓ LDL-C
Class IIa, level A‡

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.

†There is good evidence to recommend the clinical preventive action based on evidence from a meta-analysis of RCTs or from at least 1 RCT.
‡There is good evidence to recommend the clinical preventive action based on evidence from at least 1 well-designed controlled study without randomization.

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

TREATMENT APPROACHPATIENT COHORTLDL-C TARGETShsCRP TESTINGBENEFITSRISKS
Guideline-based
(treat to target)		High FRS
or
High LDL-C level		YesSelected groupsPeer support
Consistency
Optimization of benefits		Suboptimal physician uptake
Suboptimal patient compliance
Reliance on surrogate markers and targets
ExpedientHigh FRSNoNoSimplicity
Lower cost
Two-thirds of benefit realized		Maximal benefit not realized
No prospective validation studies exist

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.

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.

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

1. Genest J, McPherson R, Frohlich J, Anderson T, Campbell N, Carpentier A, et al. 2009 Canadian Cardiovascular Society/Canadian guidelines for the diagnosis and treatment of dyslipidemia and prevention of cardiovascular disease in the adult—2009 recommendations. Can J Cardiol. 2009;25(10):567–79.[PMC free article] [PubMed] [Google Scholar]
2. D’Agostino RB, Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation. 2008;117(6):743–53. Epub 2008 Jan 22. [PubMed] [Google Scholar]
3. Grover SA, Lowensteyn I, Joseph L, Kaouache M, Marchand S, Coupal L, et al. Cardiovascular Health Evaluation to Improve Compliance and Knowledge Among Uninformed Patients (CHECK-UP) Study Group. Patient knowledge of coronary risk profile improves the effectiveness of dyslipidemia therapy: the CHECK-UP study: a randomized controlled trial. Arch Intern Med. 2007;167(21):2296–303. [PubMed] [Google Scholar]
4. Ray KK, Seshasai SR, Erqou S, Sever P, Jukema W, Ford I, et al. Statins and all-cause mortality in high-risk primary prevention: a meta-analysis of 11 randomized controlled trials involving 65,229 participants. Arch Intern Med. 2010;170(12):1024–31. [PubMed] [Google Scholar]
5. Pletcher MJ, Lazar L, Bibbins-Domingo K, Moran A, Rodondl N, Coxson P, et al. Comparing impact and cost-effectiveness of primary prevention strategies for lipid lowering. Ann Intern Med. 2009;150(4):243–54. [PubMed] [Google Scholar]
6. Moride Y, Hegele RA, Langer A, McPherson R, Miller DB, Rinfret S. Clinical and public health assessment of benefits and risks of statins in primary prevention of coronary events: resolved and unresolved issues. Can J Cardiol. 2008;24(4):293–300.[PMC free article] [PubMed] [Google Scholar]
7. Baigent C, Keech A, Kearney PM, Blackwell L, Buck G, Pollicino C, et al. Cholesterol Treatment Trialists’ (CTT) Collaborators. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90056 participants in 14 randomised trials of statins. Lancet. 2005;366:1267–78. 9493. Epub 2005 Sep 27. Erratum in: Lancet 2005;366(9494):1358. Erratum in: Lancet 2008;371(9630):2084. [PubMed] [Google Scholar]
8. Ridker PM, Danielson E, Fonseca FA, Genest J, Gotto AM, Jr, Kastelein JJ, et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med. 2008;359(21):2195–207. Epub 2008 Nov 9. [PubMed] [Google Scholar]
9. Sever PS, Dahlöf B, Poulter NR, Wedel H, Beevers G, Caulfield M, et al. Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-than-average cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial—Lipid Lowering Arm (ASCOT-LLA): a multicentre randomised controlled trial. Lancet. 2003;361(9364):1149–58. [PubMed] [Google Scholar]
10. De Lorgeril M, Salen P, Abramson J, Dodin S, Hamazaki T, Kostucki W, et al. Cholesterol lowering, cardiovascular diseases, and the rosuvastatin-JUPITER controversy: a critical reappraisal. Arch Intern Med. 2010;170(12):1032–6. [PubMed] [Google Scholar]
11. Chan PS, Nallamothu BK, Hayward RA. Rosuvastatin in patients with elevated C-reactive protein [letter] N Engl J Med. 2009;360(10):1039. [PubMed] [Google Scholar]
12. Kappagoda CT, Amsterdam EA. Another look at the results of the JUPITER trial. Am J Cardiol. 2009;104(11):1603–5. Epub 2009 Oct 14. [PubMed] [Google Scholar]
13. Rose G. Sick individuals and sick populations. Int J Epidemiol. 2001;30(3):427–32. [PubMed] [Google Scholar]
14. Zulman DM, Vigan S, Omenn GS, Hayward RA. The relative merits of population-based and targeted prevention strategies. Milbank Q. 2008;86(4):557–80.[PMC free article] [PubMed] [Google Scholar]
15. Hippisley-Cox J, Coupland C. Unintended effects of statins in men and women in England and Wales: population based cohort study using the QResearch database. BMJ. 2010;340:c2197. doi: 10.1136/bmj.c2197.[PMC free article] [PubMed] [CrossRef] [Google Scholar]
16. Kearns AK, Bilbie CL, Clarkson PM, White CM, Sewright KA, O’Fallon KS, et al. The creatine kinase response to eccentric exercise with atorvastatin 10 mg or 80 mg. Atherosclerosis. 2008;200(1):121–5. Epub 2008 Feb 7. [PubMed] [Google Scholar]
17. Sinzinger H, O’Grady J. Professional athletes suffering from familial hyper-cholesterolaemia rarely tolerate statin treatment because of muscular problems. Br J Clin Pharmacol. 2004;57(4):525–8.[PMC free article] [PubMed] [Google Scholar]
18. Thomas M, Mann J. Increased thrombotic vascular events after change of statin. Lancet. 1998;352(9143):1830–1. [PubMed] [Google Scholar]
19. Josan K, Majumdar SR, McAlister FA. The efficacy and safety of intensive statin therapy: a meta-analysis of randomized trials. CMAJ. 2008;178(5):576–84.[PMC free article] [PubMed] [Google Scholar]
20. Brugts JJ, Yetgin T, Hoeks SE, Gotto AM, Shepherd J, Westendorp RG, et al. The benefits of statins in people without established cardiovascular disease but with cardiovascular risk factors: meta-analysis of randomised controlled trials. BMJ. 2009;338:b2376. doi: 10.1136/bmj.b2376.[PMC free article] [PubMed] [CrossRef] [Google Scholar]
21. Abramson J, Wright JM. Are lipid-lowering guidelines evidence-based? Lancet. 2007;369(9557):168–9. [PubMed] [Google Scholar]
22. Bulugahapitiya U, Siyambalapitiya S, Sithole J, Idris I. Is diabetes a coronary risk equivalent? Systematic review and meta-analysis. Diabet Med. 2009;26(2):142–8. [PubMed] [Google Scholar]
23. Hayward RA, Krumholz HM, Zulman DM, Timbie JW, Vijan S. Optimizing statin treatment for primary prevention of coronary artery disease. Ann Intern Med. 2010;152(2):69–77. [PubMed] [Google Scholar]
24. Hayward RA, Hofer TP, Vijan S. Narrative review: lack of evidence for recommended low-density lipoprotein treatment targets: a solvable problem. Ann Intern Med. 2006;145(7):520–30. [PubMed] [Google Scholar]
25. Hayward RA. All-or-nothing treatment targets make bad performance measures. Am J Manag Care. 2007;13(3):126–8. [PubMed] [Google Scholar]
26. Shepherd J. Resource management in prevention of coronary heart disease: optimising prescription of lipid-lowering drugs. Lancet. 2002;359(9325):2271–3. [PubMed] [Google Scholar]
27. Law MR, Wald NJ, Rudnicka AR. Quantifying effect of statins on low density lipoprotein cholesterol, ischaemic heart disease, and stroke: systematic review and meta-analysis. BMJ. 2003;326(7404):1423–7.[PMC free article] [PubMed] [Google Scholar]
28. Jones PH. Statins: a case for higher, individualized starting doses. Cleve Clin J Med. 2005;72(9):811–6. [PubMed] [Google Scholar]
29. Petrella RJ, Merikle EA. A retrospective analysis of the prevalence and treatment of hypertension and dyslipidemia in southwestern Ontario, Canada. Clin Ther. 2008;30(6):1145–54. [PubMed] [Google Scholar]
30. McCormack JP, Allan GM. Measuring hsCRP—an important part of a comprehensive risk profile or a clinically redundant practice? PLoS Med. 2010;7(2):e1000196.[PMC free article] [PubMed] [Google Scholar]
31. Shah T, Casas JP, Cooper JA, Tzoulaki I, Sofat R, McCormack V, et al. Critical appraisal of CRP measurement for the prediction of coronary heart disease events: new data and systematic review of 31 prospective cohorts. Int J Epidemiol. 2009;38(1):217–31. Epub 2008 Oct 17. Erratum in: Int J Epidemiol 2009;38(3):890. [PMC free article] [PubMed] [Google Scholar]
32. Miller M, Zahn M, Havas S. High attributable risk of elevated C-reactive protein level to conventional coronary heart disease risk factors: the Third National Health and Nutrition Examination Survey. Arch Intern Med. 2005;165(18):2063–8. [PubMed] [Google Scholar]
33. Ridker PM, McFayden JG, Fonseca FA, Genest J, Gotto AM, Kastelein JJ, et al. Number needed to treat among men and women with low low-density lipoprotein cholesterol and elevated high-sensitivity C-reactive protein: Justification for the Use of Statins in Prevention: an Interventional Trial Evaluating Rosuvastatin (JUPITER) Circ Cardiovasc Qual Outcomes. 2009;2(6):616–23. Epub 2009 Sep 22. [PubMed] [Google Scholar]
34. Buckley DI, Fu R, Freeman M, Rogers K, Helfand M. C-reactive protein as a risk factor for coronary disease: a systematic review and meta-analysis for the US Preventive Services Task Force. Ann Intern Med. 2009;151(7):483–95. [PubMed] [Google Scholar]
35. Cannon CP, Braunwald E, McCabe CH, Rader D, Rouleau JL, Belder R, et al. Intensive versus moderate lipid lowering with statins after acute coronary syndromes. N Engl J Med. 2004;350(15):1495–504. Epub 2004 Mar 8. Erratum in: N Engl J Med 2006;354(7):778. [PubMed] [Google Scholar]
36. LaRosa JC, Grundy SM, Waters DD, Shear C, Barter P, Fruchart JC, et al. Intensive lipid lowering with atorvastatin in patients with stable coronary disease. N Engl J Med. 2005;352(14):1425–35. Epub 2005 Mar 8. [PubMed] [Google Scholar]
37. Shepherd J, Cobbe SM, Ford I, Isles CG, Lorimer AR, MacFarlane PW, et al. Prevention of coronary heart disease with pravastatin in men with hyper-cholesterolemia. West of Scotland Coronary Prevention Study Group. N Engl J Med. 1995;333(20):1301–7. [PubMed] [Google Scholar]
38. ALLHAT Officers and Coordinators for the ALLHAT Collaborative Research Group Major outcomes in moderately hypercholesterolemic, hypertensive patients randomized to pravastatin vs usual care. The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT-LLT) JAMA. 2002;288(23):2998–3007. [PubMed] [Google Scholar]
39. Ridker PM, Paynter NP, Rifai N, Gaziano JM, Cook NR. C-reactive protein and family history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation. 2008;118(22):2243–51. Epub 2008 Nov 9. [PMC free article] [PubMed] [Google Scholar]
40. Perreault S, Dragomir A, Blais L, Bérard A, Lalonde L, White M, et al. Impact of better adherence to statin agents in the primary prevention of coronary artery disease. Eur J Clin Pharmacol. 2009;65(10):1013–24. Epub 2009 Jun 16. [PubMed] [Google Scholar]
41. US Preventive Services Task Force . Aspirin for the prevention of cardiovascular disease: clinical summary. Rockville, MD: Agency for Healthcare Research and Quality; 2009. Available from: www.ahrq.gov/clinic/uspstf09/aspirincvd/aspcvdsum.htm. Accessed 2010 Jan 30. [Google Scholar]
42. Dormuth CR, Schneeweiss S, Brookhart AM, Carney G, Bassett K, Adams S, et al. Frequency and predictors of tablet splitting in statin prescriptions: a population-based analysis. Open Medicine. 2008;2(3):74–82.[PMC free article] [PubMed] [Google Scholar]
Articles from Canadian Family Physician are provided here courtesy of College of Family Physicians of Canada
Circulation. Author manuscript; available in PMC 2013 Apr 10.
Published in final edited form as:
Published online 2012 Mar 7. doi: 10.1161/CIRCULATIONAHA.111.075929
NIHMSID: NIHMS366192
The publisher's final edited version of this article is available at Circulation
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Supplementary Materials
NIHMS366192-supplement-1.pdf (130K)

Abstract

Background

Framingham-based and Reynolds risk scores for cardiovascular disease (CVD) prediction have not been directly compared in an independent validation cohort.

Methods and Results

We selected a case-cohort sample of the multi-ethnic Women’s Health Initiative Observational Cohort, comprising 1722 cases of major CVD (752 MIs, 754 ischemic strokes, and 216 other CVD deaths) and a random subcohort of 1994 women without prior CVD. We estimated risk using the ATP-III score, the Reynolds risk score, and the Framingham CVD model, reweighting to reflect cohort frequencies. Predicted 10-year risk varied widely between models, with 10% or higher risk in 6%, 10%, and 41% of women using the ATP-III, Reynolds, and Framingham CVD models, respectively. Calibration was adequate for the Reynolds model, but the ATP-III and Framingham CVD models over-estimated risk for CHD and major CVD, respectively. After recalibration, the Reynolds model demonstrated improved discrimination over the ATP-III model through a higher c-statistic (0.765 vs. 0.757, p=0.03), positive net reclassification improvement (NRI) (4.9%, p=0.02) and positive integrated discrimination improvement (IDI) (4.1%, p<0.0001) overall, excluding diabetics (NRI=4.2%, p=0.01), and in white (NRI=4.3%, p=0.04) and black (NRI=11.4, p=0.13) women. The Reynolds (NRI=12.9, p<0.0001) and ATP-III (NRI=5.9%, p=0.0001) models demonstrated better discrimination than the Framingham CVD model.

Framingham

Conclusions

The Reynolds Risk Score was better calibrated than the Framingham-based models in this large external validation cohort. The Reynolds score also showed improved discrimination overall and in black and white women. Large differences in risk estimates exist between models, with clinical implications for statin therapy.

Keywords: cardiovascular disease risk factors, models, prediction, risk score, statins

Introduction

Traditional Framingham risk factors of age, hypertension, smoking, diabetes, and total and HDL cholesterol form the basis for the Adult Treatment Panel III (ATP-III) coronary heart disease (CHD) risk prediction model. Cardiovascular risk, however, also relates to family history, markers of inflammation such as high-sensitivity C-reactive protein (hsCRP), and hemoglobin A1c (HbA1c) among diabetics. These additional biomarkers are included in the Reynolds Risk Score, an alternative global risk algorithm developed in 2007 for women and men.

Both the Framingham ATP-III and the Reynolds scores have received class I recommendations from the American College of Cardiology and the American Heart Association, and both scores are endorsed as part of the national guidelines for cardiovascular disease prevention in Canada. However, to date there has been no direct comparison of these two risk scoring systems in an independent prospective cohort that was not used in the derivation of either score. In addition, a Framingham prediction model has recently been developed for total cardiovascular disease (CVD), but this has not yet been validated in an external population.

All of these risk models for CVD have been developed primarily among white men and women, with little validation in multi-ethnic populations., A Framingham risk model for hard CHD events was validated in subcohorts of black and Native American women, but these included very small numbers of events. Other studies did not have the same success in validating a Framingham model in various populations., How well these models fit in diverse populations remains to be determined.

To address these issues, we directly examined the clinical performance of the Framingham and Reynolds scores in a case-cohort analysis conducted within the Women’s Health Initiative Observational Study (WHI-OS), a multi-ethnic, prospective cohort of more than 90,000 initially healthy postmenopausal American women. Specifically, we directly compared model fit in this independent validation cohort of women for three prediction algorithms: the Framingham score currently used in the ATP-III guidelines, the Reynolds Risk Score, and the Framingham score for total CVD. As all three scores were derived in predominantly white populations, the WHI-OS provided the opportunity to address their performance in a multi-ethnic population, and separately in black and white subgroups.

Framingham Risk Score Calculator Pdf To Jpg File

Methods

Women were participants in the WHI-OS and its long term follow-up, the WHI Extension Study. The WHI-OS includes 93,676 ethnically diverse postmenopausal women aged 50 to 79 years recruited between 1994 and 1998 at 40 clinical centers targeting minority groups to obtain a cross-section of the US population. Of these, 71,872 had no prior history of myocardial infarction (MI), stroke, revascularization procedures, pulmonary embolism, deep vein thrombosis, peripheral vascular disease, or cancer, and 60,890 additionally had baseline blood specimens and baseline risk factor information.

The WHI Clinical Coordinating Center collected baseline information on sociodemographic characteristics, lifestyle factors, health behaviors, and medical history, including blood pressure measurements. Diabetes and family history, defined here as MI before age 55 in men and 65 in women, were self-reported. Participants brought current medications to clinic visits to assess medication use.

Self-reported outcome data through September 2008 were confirmed through medical record review by centrally trained physicians. MI and coronary death were combined for the CHD outcome. Medical records, electrocardiogram readings, and cardiac enzyme and troponin determinations were used for confirmation. Strokes were defined as rapid onset of a persistent neurologic deficit attributed to an obstruction or rupture of the brain arterial system, lasting more than 24 hours and without evidence of other cause. These were classified as ischemic or hemorrhagic through review of brain imaging study reports. Underlying cause of death was classified on the basis of death certificates, medical records, and other records such as autopsy reports. The primary endpoint for this analysis is a combined endpoint of major CVD, including CHD, ischemic stroke, and death due to cardiovascular causes. This project has been approved by the Institutional Review Board at the Brigham and Women’s Hospital, Boston, MA.

Sample selection

Because of the large size of the WHI, to reduce costs of biochemical assays a prospective case-cohort design14 was employed in this WHI substudy. To maximize efficiency for examining non-whites, selected cases included all eligible CVD cases from black (n=200), Hispanic (n=53), and Asian (n=55) women, and women with other/unknown ethnicity (n=55). For efficiency, the remaining 1637 of 2000 cases were randomly selected from 2370 cases among white women.

A subcohort of approximately 2000 women was selected using the same eligibility criteria and stratified to match cases by race/ethnicity and five-year age groups. Further exclusion for this analysis of those with other prior CVD conditions, including transient ischemic attack (TIA), CVD surgery, or congestive heart failure (CHF) led to a final sample size of 1,722 cases and a subcohort of size 1,994, of whom 121 were also cases. Among those in the subcohort who did not develop CVD, the median (25%, 75%) follow-up time was 9.9 (8.6, 11.8) years.

For women in the selected samples, blood specimens collected and stored at study entry were assayed centrally for total cholesterol, HDL cholesterol, hsCRP, and HbA1c (among diabetics) using standardized procedures. The core laboratory is certified by the National Heart, Lung, and Blood Institute/Centers for Disease Control and Prevention Lipid Standardization Program.

Statistical methods

The data were analyzed throughout as a case-cohort study14, using appropriate weighting of the observations. Because the numbers in the full sample were known, our stratified sampling enabled us to mimic or recapture the characteristics of the full WHI cohort using reweighting by the sampling frequency. Overall population characteristics were estimated using inverse probability weights in Proc Survey means in SAS 9.2.17 To first verify the associations of risk factors within the WHI sample, weighted Cox regression was used to estimate hazard ratios using Proc Phreg of SAS, and asymptotic variance estimates were computed following Langholz and Jiao.19 Continuous risk factors were treated in a continuous fashion as well as in clinical risk categories.

Predicted values for CHD and CVD were obtained using published equations from the Framingham risk scores for CHD and CVD and from the Reynolds models for CVD. Framingham risk factors include age, blood pressure, antihypertensive treatment, smoking, diabetes, and total and HDL cholesterol. The ATP-III model is intended for those without diabetes, which is considered a risk equivalent. The Reynolds Risk Score additionally includes hsCRP, family history of premature MI (before age 60), and hemoglobin A1c among diabetics only. The fit of the models in the WHI data was examined using appropriate weighting. The c-statistic for survival data, was computed and differences between models assessed using bootstrapping. Calibration plots were used to compare observed and predicted risk within deciles using inverse sampling weights. In addition, for completeness, we also considered the alternative Framingham simple model which uses body mass index (BMI) instead of lipids (available at http://www.framinghamheartstudy.org/risk/gencardio.html#).

Because different endpoint definitions were used in development of each of the three models, recalibration was necessary to compare models using reclassification methods. The Framingham ATP-III score predicts ‘hard’ CHD, defined as MI and coronary death, while the Reynolds Risk Score predicts a composite CVD outcome defined as incident MI, ischemic stroke, coronary revascularization, and cardiovascular death. The Framingham CVD score predicts ‘total’ CVD defined as all coronary events (MI, coronary death, coronary insufficiency, and angina), cerebrovascular events (including ischemic stroke, hemorrhagic stroke, and TIA), peripheral artery disease (intermittent claudication), and heart failure. As described above, the major CVD endpoint used in these WHI data comprised CHD, ischemic stroke, and CVD death, and did not match any of these precisely. To correct for differences in endpoint definition, after initial evaluation of fit, models were recalibrated to the WHI cohort using logistic regression calibration for 10-year risk., This process does not change the coefficients for risk factors, but changes the intercept only, to alter the mean predicted risk. The average predicted risk for each model then approximately equaled the overall incidence of major CVD at ten years in the WHI cohort of eligible women.

We used plots to examine the calibration of original and recalibrated models. These plot the average predicted risk within deciles against the observed risk in that decile, adding a reference line for perfect calibration. To directly compare recalibrated models, we examined the integrated discrimination improvement (IDI) and reclassification statistics, including reclassification calibration chi-squares, and net reclassification improvement (NRI). For the reclassification tables, clinical-based cut points of 5, 10, and 20% were used. Survival methods were used throughout,, and measures were reweighted to reflect the distribution in the overall cohort. Statistical tests of reweighted measures were derived using bootstrap samples. Models were also examined among women without diabetes, eliminating those on statins or other cholesterol-lowering medications (7%), and separately among white and black women.

Results

Of 1,722 incident cardiovascular cases, 752 were MIs, 754 ischemic strokes, and 216 CVD deaths. Baseline characteristics for cases and the subcohort are shown in Table 1 both in the selected subsample and reweighted to the population distribution. In the subcohort sample the average age was 69 years with 5% current smokers and 5% diabetics. Reweighted population estimates were age 62 years, 6% current smokers, and 4% diabetics. Cases included more smokers and diabetics, and generally higher risk factor levels. Reweighted distributions in the subcohort are shown by race/ethnicity in Supplemental Table 1. Blacks were slightly younger than whites, but had higher proportions of smokers and diabetics.

Table 1

Baseline characteristics in ischemic CVD cases and subcohort members, among those with no prior CVD (including TIA, CHF, CVD surgery), crude and weighted to the population distribution.*

Risk FactorSubcohortAll CasesMIStrokeCVD Death
N19941722752754216
Crude
Age (yrs)69 (63, 73)69 (64, 73)68 (63, 73)69 (64, 73)70 (64, 74)
Current Smoking (%)4.98.99.07.812.0
Diabetes (%)4.710.713.09.47.4
HbA1c among diabetics (%)7.0 (6.2, 7.9)7.5 (6.8, 8.9)7.4 (6.5, 8.6)7.7 (6.8, 9.6)7.5 (7.0, 8.4)
Systolic blood pressure (mmHg)128.0 (117, 140)134 (122, 148)133 (121, 147)135 (123, 149)133 (120, 142)
Hypertension medication (%)26.638.338.338.637.0
Total Cholesterol (mg/dl)225 (200, 256)226 (198, 253)229 (202, 258)221 (196, 248)227 (202, 252)
HDL Cholesterol (mg/dl)54.4 (44.6, 66.6)48.6 (39.8, 59.8)48.8 (39.8, 60.1)47.9 (38.8, 58.6)50.5 (42.3, 62.0)
C-reactive protein (mg/L)2.3 (1.0, 5.0)3.1 (1.4, 6.2)3.2 (1.4, 6.2)2.9 (1.3,6.3)3.1 (1.5, 6.0)
Family history of MI before age 65 (%)17.622.525.120.819.4
Weighted by Sample Frequencies
Age (yrs)62.0 (56.2, 67.8)68.2 (63.2, 72.6)67.8 (62.4, 72.3)68.3 (63.7, 72.6)69.2 (64.0, 73.6)
Current Smoking (%)5.78.58.67.411.7
Diabetes (%)3.710.112.28.87.0
HbA1c if diabetic (%)7.0 (6.2, 8.0)7.5 (6.7, 8.87.4 (6.5, 8.6)7.6 (6.8, 9.2)7.3 (7.0, 8.4)
Systolic blood pressure (mmHg)123.8 (113.9, 136.0)133.1 (121.5, 146.9)132.5 (120.6, 146.6)134.2 (122.6, 148.4)132.3 (120.4, 142.0)
Hypertension medication (%)22.537.837.838.036.9
Total Cholesterol (mg/dl)223.8 (199.5, 255.7)225.2 (198.8, 252.7)228.6 (202.0, 259.1)220.2 (195.8, 247.5)227.0 (202.1, 252.9)
HDL Cholesterol (mg/dl)54.5 (44.6, 66.5)48.7 (39.8, 59.8)48.9 (39.7, 60.1)48.0 (39.0, 58.5)50.4 (42.1, 62.2)
C-reactive protein (mg/L)2.4 (1.0, 5.2)3.1 (1.4, 6.1)3.1 (1.4, 6.1)2.9 (1.3, 6.2)3.0 (1.4, 5.8)
Family history of MI before age 65 (%)19.623.125.621.520.0

Risk factor associations

Multivariable Cox regression models confirmed the association of risk factors with CVD, both overall and among whites and blacks (Table 2). Each risk factor had significant associations in the overall sample except for total cholesterol, which was the same after excluding women on cholesterol-lowering medications. When examined separately using CHD as an endpoint, total cholesterol was a significant predictor (hazard ratio (HR) for total cholesterol ≥ 240 = 1.30, 95% confidence interval (CI) = 1.01–1.66). Estimated effects of each risk factor were generally consistent for whites and blacks, although results were more variable among blacks due to smaller numbers. The effect of measured blood pressure was weaker but anti-hypertensive medication stronger among blacks, and the effect of total cholesterol was stronger among black women. Age, diabetes, smoking, HDL-cholesterol, hsCRP, and family history all had significant independent effects on major CVD. Regressions using continuous versions of the risk factors showed similar results (Supplemental Table 2). Interactions of age and total cholesterol and of smoking and age included in the ATP-III model were not significant in these data, and are not included in these models.

Table 2

Results of multivariable Cox regression analysis, including all variables in the model, in case-cohort sample in ischemic CVD cases and subcohort members with no prior CVD – effects of risk factor categories.

Risk factorOverallWhitesBlacks
HR (95% CI)HR (95% CI)HR (95% CI)
Age (yrs)
 60–693.05 (2.53–3.69)3.39 (2.72–4.21)1.58 (0.92–2.71)
 70+6.66 (5.48–8.09)7.12 (5.70–8.90)7.27 (3.99–13.25)
Diabetes1.94 (1.40–2.67)1.52 (1.03–2.23)3.57 (1.75–7.31)
Current smoking2.39 (1.76–3.26)2.06 (1.43–2.95)3.65 (1.63–8.18)
SBP (mmHg)
 120–<1401.38 (1.15–1.65)1.47 (1.21–1.79)0.67 (0.35–1.30)
 >=1402.02 (1.65–2.49)2.10 (1.67–2.62)1.09 (0.54–2.20)
Hypertension medication1.50 (1.26–1.77)1.49 (1.23–1.79)1.80 (1.08–3.01)
Total cholesterol (mg/dl)
 200–<2400.93 (0.76–1.13)0.93 (0.75–1.15)1.50 (0.75–3.00)
 >=2401.06 (0.87–1.30)1.00 (0.81–1.25)2.18 (1.08–4.41)
HDL cholesterol (mg/dl)
 40–<600.66 (0.54–0.82)0.66 (0.53–0.83)0.34 (0.17–0.71)
 >=600.42 (0.34–0.53)0.42 (0.33–0.54)0.32 (0.14–0.72)
C-reactive protein (mg/L)
 1–<31.22 (0.99–1.50)1.21 (0.97–1.51)1.83 (0.79–4.22)
 >=31.45 (1.19–1.77)1.43 (1.15–1.78)1.75 (0.81–3.78)
Family history of premature MI1.25 (1.03–1.52)1.25 (1.02–1.53)1.47 (0.70–3.07)

Predicted risk and model fit

Predicted 10-year risk was estimated using published equations for each model. These varied widely between models, as shown in the distributions in Figure 1A. Average risk was 3.8%, 4.6%, and 10.9% for the ATP-III, Reynolds, and Framingham CVD models. Estimated risk was 10% or higher in 5.5%, 10.3%, and 41.1% of women, respectively (Figure 1B), and risk was 20% or higher in 0.5, 2.6, and 10.6% of women in the three models.

Distribution of risk estimated from the Framingham ATP-III score, the Reynolds risk score, and the Framingham CVD score among women in the WHI (A), and the estimated percent of women with risk of 10% or greater and 20% or greater using the published scores (B).

To assess calibration, predicted risk for the ATP-III score was compared to observed 10-year rates of CHD among non-diabetics only, to match its intended use (Figure 2A). As shown, the ATP-III model over-estimated risk of CHD, with predicted values higher than those observed. In contrast, the CHD score was actually better calibrated to the risk of major CVD among all women (Figure 2B). The Reynolds risk score appeared relatively well-calibrated for the endpoint of major CVD (Figure 2C). The Framingham CVD model, developed for a broader definition of CVD, greatly over-estimated risk of major CVD as anticipated (Figure 2D). Similar patterns of over-estimation of risk were seen for the ATP III model for CHD and the Framingham model for CVD among blacks (Figure 3) and whites (Supplemental Figure 1) separately, as the predicted risk was higher than the observed risk in each group.

Calibration plots for the original published risk prediction scores, including the ATP-III score and the coronary heart disease (CHD) outcome among only those without diabetes (A), and the ATP-III score and the cardiovascular disease (CVD) outcome (B), the Reynolds risk score and CVD (C), and the Framingham CVD score and CVD (D) among all women.

Calibration plots for the original published risk prediction scores among black women only, including the ATP-III score and the coronary heart disease (CHD) outcome among those without diabetes (A), and the ATP-III score and the cardiovascular disease (CVD) outcome (B), the Reynolds risk score and CVD (C), and the Framingham CVD score and CVD (D) among all black women.

In order to directly compare discrimination of the three models for the endpoint of CVD, the models were recalibrated such that the average predicted risk equaled the overall reweighted population estimate of 4%. The percent of women with estimated risk of 10% or higher was then 6.6%, 7.7%, and 6.2% for the ATP-III, Reynolds, and Framingham CVD model. After recalibration of the mean, all three models showed reasonable calibration to the major CVD endpoint (Supplemental Figure 2). C-statistics for major CVD (unaffected by recalibration) were 0.757 for the ATP-III model, 0.765 for the Reynolds model, and 0.750 for the Framingham CVD model. Differences in the c-statistics, though small, were all statistically significant (Table 3). When the simple non-lab based Framingham CVD model was considered, the calibration was very similar to that of the lab-based model. However, the discrimination was worse, with a c-statistic of 0.747, which was significantly lower than that for the Reynolds model (p=0.0004), but more similar to the lab-based Framingham CVD model (p=0.57) and the ATP-III model (P=0.055).

Table 3

Comparison of recalibrated models for CVD events in the WHI in ischemic CVD cases and subcohort members with no prior CVD, based on weighted survival estimates for case-cohort studies.

Model ComparisonChange in C-indexRC X2old*RC X2new*NRI (%)Contin NRI (%)IDI (%)
All Women
RRS vs. ATP-III0.008273.5185.74.932.14.1
 (p)0.0320.010<0.0001<0.0001
Framingham CVD vs. ATP-III−0.00885.2246.8−5.9−51.50.2
 (p)0.0200.0002<0.00010.42
RRS vs. Framingham CVD0.016283.5153.212.960.03.9
 (p)<0.0001<0.0001<0.0001<0.0001
Whites
RRS vs. ATP-III0.007132.0198.84.330.84.3
 (p)0.0680.043<0.0001<0.0001
Framingham CVD vs. ATP-III−0.00880.2227.1−6.9−52.80.3
 (p)0.015<0.0001<0.00010.34
RRS vs. Framingham CVD0.016275.6186.413.058.64.1
 (p)<0.0001<0.0001<0.0001<0.0001
Blacks
RRS vs. ATP-III0.00715.28.011.432.93.5
 (p)0.560.130.0040.010
Framingham CVD vs. ATP-III−0.0136.815.7−2.1−30.1−0.1
 (p)0.160.760.0010.80
RRS vs. Framingham CVD0.02041.214.023.776.73.6
 (p)0.0180.004<0.00010.0003
All Non-Diabetics
RRS vs. ATP-III0.0008114.2103.54.228.11.5
 (p)0.820.010<0.0001<0.0001
Framingham CVD vs. ATP-III−0.01670.3225.8−9.0−61.4−0.6
 (p)<0.0001<0.0001<0.0001<0.0001
RRS vs. Framingham CVD0.016246.039.013.459.72.2
 (p)<0.0001<0.0001<0.0001<0.0001
*Under population weighting. Only computed for cells with at least 20 observations (small number of cells in blacks).

Risk reclassification

Fit of the recalibrated models was directly compared using reclassification measures (Table 3). Reclassification tables comparing the Reynolds to both Framingham models using both the crude and recalibrated predicted values are shown in Supplemental Tables 3A–D. These show the numbers of women in the total WHI cohort who would be reclassified into the various risk groups. For example, using the original scores in Table 3A, 664 (23%) of the 2,943 women at ATP III risk of 10–<20% would be reclassified as >=20% risk and 544 (18%) as less than 10% risk. In addition, of the 10,620 women originally at 5–<10% risk, 576 (5%) would be reclassified above 20% and 3,028 (29%) above 10%. Compared to the recalibrated ATP-III model, the Reynolds model showed improvement in discrimination based on the NRI (4.9%, 95%CI=1.2–8.7%, p=0.010), with improvement of 4.0% (p=0.02) in cases, and 1.0% (p=0.30) in non-cases. Improvement was also seen using the IDI (4.1%, 95%CI=2.7–5.7%, p<0.0001; relative IDI=117.3%), the continuous NRI (32.1%, 95%CI=24.2–39.5%, p<0.0001), and reclassification calibration chi-squares (273.5 vs. 185.7), indicating that calibration improved with the Reynolds model. When comparing the Framingham CVD model to the ATP-III model, the former provided a worse fit even though it was developed for the endpoint of CVD rather than CHD. The NRI and IDI were negative, calibration worse, and c-statistic lower (Table 3). Comparison of the Reynolds to the Framingham CVD model indicated improved fit on all measures, with an NRI of 12.9% (95%CI=9.4–16.3%, p<0.0001). Comparison of the Reynolds model to the simple non-lab based Framingham model was very similar with an NRI of 11.9% (p<0.0001).

Model fit statistics for recalibrated models were similar among whites only (Table 3). Although not always significant due to smaller numbers, the direction of effects was the same and often stronger among black women, with the Reynolds model showing improved fit over the ATP-III score, and both providing better fit than the Framingham CVD score.

Since the ATP-III model was intended for those without diabetes, we also recalibrated the models separately including only women without diabetes at baseline (Table 3). Although the c-statistics and reclassification calibration chi-squares were similar for the ATP-III and Reynolds models, the NRI showed a significant improvement favoring the Reynolds model (4.2%, 95%CI=1.1–7.5%, p=0.01), as did the continuous NRI and the IDI (both p<0.0001). Finally, results were very similar after excluding women on cholesterol-lowering medications (Supplemental Table 4).

Discussion

This validation study prospectively examined the fit of three cardiovascular risk prediction models in a large-scale external cohort, the WHI-OS, a national, geographically representative, racially and ethnically diverse sample of US women. Models included the Framingham-based ATP-III model, the Reynolds Risk Score, and the Framingham CVD model. The published ATP-III model was poorly calibrated for CHD, and the Reynolds Risk Score was better calibrated for major CVD than the Framingham CVD model. Moreover, after recalibration, the Reynolds Risk Score continued to show statistically significant but modest improvement in fit when compared to either of the Framingham-based models.

We know of no prior work directly comparing the Framingham and Reynolds scores in an independent prospective cohort. However, the findings here that family history, inflammation, and HbA1c among diabetics improve global risk prediction are consistent with observations made in other settings considering each factor in isolation.– Further, the lack of effect modification in our data by ethnicity suggests that these findings are clinically relevant in ethnically diverse populations.

Care must be used when interpreting these data, as the endpoints used to generate the three scores differ from each other and from the primary endpoint of the WHI-OS, which could lead to lack of calibration. However, the ATP-III score over-estimated risk of its intended endpoint, ‘hard CHD’ among non-diabetics, a finding replicated in the Women’s Health Study (data available on request). The WHI-OS sample is older than that of Framingham, and traditional risk markers are generally less predictive in older age ranges,, although the ATP-III score includes interactions of age with smoking and total cholesterol. One Framingham model has previously been validated in six prospective, ethnically diverse cohorts and was shown to have reasonable calibration overall, but prior data specifically for the ATP-III model are sparse. Other, primarily European, investigators have also reported that various versions of Framingham models over-predict CHD incidence,, Since clinicians in practice cannot re-calibrate a score for an individual patient, population calibration is essential.

The Framingham CVD model was also poorly calibrated for the endpoint of major CVD. However, it was developed for the broader endpoint of total CVD including several other conditions, namely angina, coronary insufficiency, TIA, peripheral artery disease, and CHF. As statins reduce stroke as well as CHD, scoring systems including stroke have recently been favored, whereas the inclusion of other endpoints such as CHF are more controversial. In this research setting, re-calibration allowed us to minimize differences in outcomes and directly compare risk scores for a comparable endpoint. As shown in the re-calibrated analyses, the Framingham CVD score, developed using an endpoint closer to that used here, was not superior to the ATP-III score for CHD only, and both of these discriminated less well than the Reynolds score. It is possible that the lower levels of discrimination seen with the Framingham CVD model may reflect reduced effects of the traditional risk factors on these secondary endpoints.

More importantly, the number of women potentially eligible for statin therapy can vary greatly depending on the equation and endpoint used. As shown in Figure 1, the estimated distributions of predicted risks in the population vary widely across models. Using the WHI data, in a hypothetical population of 100,000 women the number who would be classified at 10% or higher risk would be 5,549 with the published ATP-III model, 10,304 with the Reynolds score, and 41,074 with the Framingham CVD model (Supplemental Tables 3AC). Even if all models were perfectly calibrated for their respective outcomes, the choice of endpoints needs to be addressed. Whether statins are equally effective for the various CVD endpoints, and whether the risk-benefit equation is the same for all endpoints, are important criteria in developing guidelines for therapy.

We believe these data may have clinical implications for CVD prevention in otherwise healthy middle-aged women. Risk prediction algorithms have been widely used to better target cardiovascular preventive therapies, in particular the use of statins. A recent meta-analysis including 13,154 women in primary prevention statin trials reported a 37% reduction in cardiovascular event rates. However, the great majority of women destined to suffer a cardiovascular event have ATP-III scores less than 10 percent. These women would not qualify for treatment under current guidelines yet could benefit from statin therapy. Using data from the WHI-OS, among women with 10-year ATP-III risks of 5 to 10 percent, the Reynolds score would reclassify 15% to a lower risk category (< 5%) and over 28% to a higher risk category (> 10%), including 5% with estimated risk exceeding 20% (Supplemental Table 3A).

Limitations of our analysis merit consideration. First, the WHI-OS is composed exclusively of women, so these results cannot be generalized to men. However, prior work in the Physicians Health Study has shown that the Reynolds Risk Score for men improves fit and reclassification in that setting. Second, there may be remaining differences in endpoint definition. Confirmation procedures for Framingham, WHS, and the WHI-OS may have differed somewhat even for common endpoints such as CHD. Third, we were not able to fully assess the calibration of the Framingham CVD model since we did not have data available on the broader definition used for that model. Fourth, estimates from this case-cohort sample may offer less precision than those based on the full WHI cohort.

In sum, in this large scale comparison of risk prediction models commonly used in North America, the Reynolds Risk Score significantly improved fit as compared to either the Framingham-based ATP-III CHD risk score or the newer Framingham CVD score. Within the WHI-OS, the greatest impact of the Reynolds Risk Score appeared to be among those women with 5 to 10 percent 10-year estimated risk according to ATP-III, a group including a large number of women destined to suffer MI or stroke, and in whom trial data indicate efficacy of statin therapy in reducing cardiovascular events.

Supplementary Material

1

Acknowledgments

Canadian Framingham Risk Score Calculator

Women’s Health Initiative Investigators: A full listing of Women’s Health Initiative investigators can be found at http://whiscience.org/publications/WHI_investigators_longlist. Additional Contributions: We thank the Women’s Health Initiative investigators, staff, and study participants for their outstanding dedication and commitment. We also thank Dr. Bryan Langholz for advice concerning inference for the case-cohort sample.

Funding Sources: This project was supported by National Heart, Lung, and Blood Institute’s Broad Agency Announcement contract number HHSN268200960011C. The Women’s Health Initiative program is funded by the National Heart, Lung, and Blood Institute, the National Institutes of Health, and the US Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-9, 32122, 42107-26, 42129-32, and 44221.

Footnotes

Conflict of Interest Disclosures: Dr. Ridker is listed as a co-inventor on patents held by the Brigham and Women’s Hospital, Boston, MA, that relate to the use of inflammatory biomarkers in cardiovascular disease that have been licensed to AstraZeneca and Siemens. Drs. Ridker and Manson are listed as co-inventors on a patent held by Brigham and Women’s Hospital, Boston, MA, that relates to the use of inflammatory biomarkers in diabetes that has been licensed to AstraZeneca.

Framingham Risk Calculator Pdf

References

1. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) JAMA. 2001;285:2486–2497. [PubMed] [Google Scholar]
2. Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women. JAMA. 2007;297:611–619. [PubMed] [Google Scholar]
3. Ridker PM, Paynter NP, Rifai N, Gaziano JM, Cook NR. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation. 2008;118:2243–2251.[PMC free article] [PubMed] [Google Scholar]
4. Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA, Foster E, Hlatky MA, Hodgson JM, Kushner FG, Lauer MS, Shaw LJ, Smith SC, Jr, Taylor AJ, Weintraub WS, Wenger NK, Jacobs AK for the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circ. 2010;122:e584–636. [PubMed] [Google Scholar]
5. Genest J, McPherson R, Frohlich J, Anderson T, Campbell N, Carpentier A, Couture P, Dufour R, Fodor G, Francis GA, Grover S, Gupta M, Hegele RA, Lau DC, Leiter L, Lewis GF, Lonn E, Mancini GB, Ng D, Pearson GJ, Sniderman A, Stone JA, Ur E. 2009 Canadian Cardiovascular Society/Canadian guidelines for the diagnosis and treatment of dyslipidemia and prevention of cardiovascular disease in the adult - 2009 recommendations. Can J Cardiol. 2009;25:567–569.[PMC free article] [PubMed] [Google Scholar]
6. D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circ. 2008;117:743–753. [PubMed] [Google Scholar]
7. D’Agostino RB, Sr, Grundy S, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;286:180–187. [PubMed] [Google Scholar]
8. Brindle P, Emberson J, Lampe F, Walker M, Whincup P, Fahey T, Ebrahim S. Predictive accuracy of the Framingham risk score in British men: prospective cohort study. BMJ. 2003;327:1–6.[PMC free article] [PubMed] [Google Scholar]
9. Diverse Populations Collaborative Group. Prediction of mortality from coronary heart disease among diverse populations: is there a common predictive function? Heart. 2002;88:222–228.[PMC free article] [PubMed] [Google Scholar]
10. Langer RD, White E, Lewis CE, Kotchen JM, Hendrix SL, Trevisan M. The Women’s Health Initiative Observational Study: Baseline characteristics of participants and reliability of baseline measures. Ann Epidemiol. 2003;13:S107–121. [PubMed] [Google Scholar]
11. The Women’s Health Initiative Study Group. Design of the Women’s Health Initiative clinical trial and observational study. Contr Clin Trials. 1998;19:61–109. [PubMed] [Google Scholar]
12. Margolis KL, Qi L, Brzyski R, Bonds DE, Howard BV, Kempainen S, Liu S, Robinson JG, Safford MM, Tinker LT, Phillips LS for the Women’s Health Initiative Investigators. Validity of diabetes self-reports in the Women’s Health Initiative: comparison with medication inventories and fasting glucose measurements. Clin Trials. 2008;5:240–247.[PMC free article] [PubMed] [Google Scholar]
13. Curb JD, McTiernan A, Heckbert SR, Kooperberg C, Stanford J, Nevitt M, Johnson KC, Proulx-Burns L, Pastore L, Criqui M, Daugherty S. Outcomes ascertainment and adjudication methods in the Women’s Health Initiative. Ann Epidemiol. 2003;13:S122–128. [PubMed] [Google Scholar]
14. Prentice RL. A case-cohort design for epidemiologic cohort studies and disease prevention trials. Biomka. 1986;73:1–11.[Google Scholar]
15. Barlow WE. Robust variance estimation for the case-cohort design. Biom. 1994;50:1064–1072. [PubMed] [Google Scholar]
16. Borgan Ø, Langholz B, Samuelson SO, Goldstein L, Pogoda J. Exposure stratified case-cohort designs. Lifetime Data Analy. 2000;6:39–58. [PubMed] [Google Scholar]
17. Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. J Amer Statist Assoc. 1952;47:663–685.[Google Scholar]
18. Therneau TM, Li H. Computing the Cox model for case cohort designs. Lifetime Data Analy. 1999;5:99–112. [PubMed] [Google Scholar]
19. Langholz B, Jiao J. Computational methods for case-cohort studies. Comp Stats Data Analysis. 2007;51:3737–3748.[Google Scholar]
20. Harrell FE, Jr, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests. JAMA. 1982;247:1543–1546. [PubMed] [Google Scholar]
21. Pencina MJ, D’Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004;23:2109–2123. [PubMed] [Google Scholar]
22. Janssen KJM, Vergouwe Y, Kalkman CJ, Grobbee DE, Moons KGM. A simple method to adjust clinical prediction models to local circumstances. Can J Anesth. 2009;56:194–201. [PubMed] [Google Scholar]
23. Steyerberg EW, Borsboom GJJM, van Houwelingen HC, Eijkemans MJC, Habbema JDF. Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Stat Med. 2004;23:2567–2586. [PubMed] [Google Scholar]
24. Pencina MJ, D’Agostino RBS, D’Agostino RBJ, Vasan RS. Evaluating the added predictive ability of a new biomarker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27:157–172. [PubMed] [Google Scholar]
25. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circ. 2007;115:928–935. [PubMed] [Google Scholar]
26. Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009;150:795–802.[PMC free article] [PubMed] [Google Scholar]
27. Cook NR, Buring JE, Ridker PM. The effect of including C-reactive protein in cardiovascular risk prediction models for women. Ann Intern Med. 2006;145:21–29. [PubMed] [Google Scholar]
28. Chambless LE, Cummiskey CP, Cui G. Several methods to assess improvement in risk prediction models: Extension to survival analysis. Stat Med. 2010;30:22–38. [PubMed] [Google Scholar]
29. Pencina MJ, D’Agostino RB, Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30:11–21.[PMC free article] [PubMed] [Google Scholar]
30. American Diabetes Association. Diagnosis and classification of Diabetes Mellitus. Diab Care. 2010;33:S62–S69.[PMC free article] [PubMed] [Google Scholar]
31. Chow CK, Islam S, Bautista L, Rumboldt Z, Yusufali A, Xie C, Anand SS, Engert JC, Rangarajan S, Yusuf S. Parental history and myocardial infarction risk across the world: the INTERHEART Study. J Am Coll Cardiol. 2011;57:619–627. [PubMed] [Google Scholar]
32. The Emerging Risk Factors Collaboration. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis. Lancet. 2010;375:132–140.[PMC free article] [PubMed] [Google Scholar]
33. Beer C, Alfonso H, Flicker L, Norman PE, Hankey GJ, Almeida OP. Traditional risk factors for incident cardiovascular events have limited importance in later life compared with the Health in Men Study Cardiovascular Risk Score. Stroke. 2011;42:952–959. [PubMed] [Google Scholar]
34. Koller MT, Steyerberg EW, Wolbers M, Stijnen T, Bucher HC, Hunink MGM, Witteman JCM. Validity of the Framingham point scores in the elderly: results from the Rotterdam study. Am Heart J. 2007;154:87–93. [PubMed] [Google Scholar]
35. Barroso LC, Muro EC, Herrera ND, Ochoa GF, Hueros JI, Buitrago F. Performance of the Framingham and SCORE cardiovascular risk prediction functions in a non-diabetic population of a Spanish health care centre: a validation study. Scand J Prim Health Care. 2010;28:242–248.[PMC free article] [PubMed] [Google Scholar]
36. Collins GS, Altman DG. An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study. BMJ. 2010;340:c2442.[PMC free article] [PubMed] [Google Scholar]
37. Mosca M, Benjamin EJ, Berra K, Bezanson JL, Dolor RJ, Lloyd-Jones DM, Newby LK, Piña IL, Roger VL, Shaw LJ, Zhao D. Effectiveness-based guidelines for the prevention of cardiovascular disease in women 2011 update: a guideline from the American Heart Association. Circ. 2011;123:1243–1262.[PMC free article] [PubMed] [Google Scholar]
38. Mora S, Glynn RJ, Hsia J, MacFadyen J, Genest J, Ridker PM. Statins for the primary prevention of cardiovascular events in women with elevated high-sensitivity C-reactive protein or dyslipidemia. Circ. 2010;121:1069–1077.[PMC free article] [PubMed] [Google Scholar]
39. Ridker PM, Macfadyen JG, Nordestgaard BG, Koenig W, Kastelein JJ, Genest J, Glynn RJ. Rosuvastatin for primary prevention among individuals with elevated high-sensitivity c-reactive protein and 5% to 10% and 10% to 20% 10-year risk. Implications of the Justification for Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) trial for “intermediate risk” Circ Cardiovasc Qual Outcomes. 2010;3:447–452. [PubMed] [Google Scholar]