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Record W103115216

Does screening for peripheral arterial disease improve risk stratification for patients at intermediate risk for coronary artery disease?

2007· article· en· W103115216 on OpenAlexaboutno aff
Michelle Greiver

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineCoronary artery diseaseFramingham Risk ScoreDyslipidemiaFamily historyMyocardial infarctionDiabetes mellitusDiseaseCardiologyEndocrinology
DOInot available

Abstract

fetched live from OpenAlex

Many patients at intermediate risk of developing coronary artery disease (CAD) are seen in primary care. Intermediate risk is classified as a calculated Framingham 10-year risk of 10% to 20% for patients without a history of arteriosclerotic disease or diabetes. A recent audit showed that 14% of patients aged 35 to 75 years in my family practice meet this profile. Recent Canadian dyslipidemia guidelines1 have been criticized for promoting the overuse of statins for patients at low risk of developing CAD.2 Statins decrease the relative risk of coronary events by approximately 30%.3 If a patient has a 5% risk of developing CAD in the next 10 years, using a statin for 10 years would only reduce his or her absolute risk by 1.5% (0.3 x 5%); 67 patients would have to be treated for 10 years to prevent 1 myocardial infarction and 66 of these patients would not benefit from the treatment. With statins costing about $1 per day, treating 67 patients for 10 years would translate to $245 000 in drug costs alone. Patients at high risk of developing CAD (Framingham 10-year risk >20%) derive greater benefit from statins, and there is more evidence to support treating them. Several ways to further stratify patients at intermediate risk have been proposed. A family history of early CAD (a first-degree relative who developed CAD at age 50 years or younger) doubles the calculated Framingham risk.4 A plethora of biomarkers, including C-reactive protein, B-type natriuretic peptide, aldosterone, renin, fibrinogen, D-dimer, plasminogen-activator inhibitor type 1, homocysteine and urinary albumin-to-creatinine ratio, 5 have been suggested as candidates to improve risk stratification. However, the accuracy of these biomarkers and the resulting risk adjustment is not clear, and a recent study found that even the use of multiple markers adds little to the Framingham score.5 Using at least two high-sensitivity C-reactive protein measurements has been found to be a reliable marker for a new risk calculator in women (the Reynolds score)6 but the additional complexity and expense of implementing this new score may limit its use in primary care. am not using these biomarkers in my practice. There are other tests that can be used for risk stratification. The US Preventive Services Task Force recently reviewed CAD screening; the Task Force gave electrocardiography, cardiac stress exercise testing, and electron beam computed tomography (EBCT) an I rating (insufficient evidence to make a recommendation for or against) for patients at intermediate Framingham risk.7 EBCT is expensive and as there are already long waiting lists for CT scans in Canada this is not an ideal test to use for screening at this time. Recommendations have been made that all patients aged 50 years and over with at least 1 cardiovascular risk factor and all patients aged 70 years and over should be screened for peripheral arterial disease (PAD) using the ankle-brachial index (ABI)(Figure 1).8, 9 PAD is a known risk factor for underlying cardiovascular disease, but neither history taking nor clinical examination is sensitive or specific enough for PAD screening.10 The US Preventive Services Task Force does not currently recommend PAD screening in the general population (grade D recommendation), because there is the potential for a small degree of harm resulting from false-positive results and unnecessary investigations for PAD.11 Perhaps we should think of ABI as a tool for risk stratification for cardiovascular disease rather than solely as a screening or diagnostic test for PAD. Figure 1 How to measure the ankle-brachial index (ABI). DP = dorsalis pedis. PT = posterior tibial. Reprinted with permission from Laine et al.9 The prevalence of abnormal ABIs (0.9 or less) is 18% to 29% in patients aged 50 years and over; 75% of those patients are asymptomatic.11 A systematic review found that with an ABI of 0.9 or less, the likelihood ratio of CAD is 2.5 and the likelihood ratio for death from coronary causes is 5.6.12 Using a nomogram, one can calculate that if a patient has a 15% Framingham risk and a positive ABI, their risk of developing CAD is approximately 30%. This puts him or her in the high-risk category and he or she should be offered treatment with a statin. The cost of testing ABI in Ontario at a vascular or ultrasound laboratory is $22.60 for the technical component and $13.70 for the professional component. However, the test can be performed easily and accurately in an office setting with a handheld Doppler probe and a blood pressure cuff. The fee per test is $10.05, but the cost of the Doppler probe is approximately $700. It is unclear whether it is practical to perform the test in the primary care setting, given the current reimbursement rates and the additional time required for providers to perform the test. A prospective randomized controlled trial enrolling patients at intermediate risk of developing CAD who are randomly assigned to be screened or not screened with ABI would provide the best assessment of this test’s usefulness. The trial registry clinicaltrials.gov does not currently list any studies addressing the use of ABI in patients at intermediate risk, although it includes a randomized controlled trial using the expensive and more invasive EBCT. Guidelines produced by specialty groups appear to recommend the use of ABI to screen for PAD in patients at intermediate risk of developing CAD,13, 14 but the producers of these guidelines are perhaps more likely to be biased toward intervention in their area of interest rather than routine screening in the general population. It would be helpful to practitioners if the US Preventive Services Task Force or the revitalized Canadian Task Force on Preventive Health Care reviewed the subject. Meanwhile, we are each left to weigh the evidence to decide if it is strong enough to start using ABI for risk stratification of patients at intermediate Framingham risk of developing CAD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.244
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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