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Evid Based Nurs 12:123 doi:10.1136/ebn.12.4.123
  • Clinical prediction guide

Absolute CVD risk, stratified by risk score, was 20% higher in primary care patients with CVD than in those without CVD

Question

How do risks of cardiovascular disease (CVD) events compare in primary care patients with and without a history of CVD after adjusting for traditional CVD risk factors?

Methods

Design:

prospective cohort study with a mean 2 years of follow-up.

Setting:

primary care practices in Auckland, New Zealand.

Patients:

35 760 patients 30–74 years of age (mean age 54 y, 57% men, 10% with a history of CVD) who had a CVD risk score calculated using the web-based PREDICT clinical decision support program.

Description of prediction guide:

based on the Framingham risk score, PREDICT uses traditional CVD risk factors (age, sex, diabetes, smoking, blood pressure, and cholesterol concentrations) to classify patients as having <5%, 5 to <10%, 10 to <15%, 15 to <20%, or ⩾20% 5-year risk of a CVD event.

Outcome:

first CVD event (acute coronary syndrome, ischaemic or haemorrhagic stroke, peripheral arterial disease, a procedure for these conditions, heart failure, or death from CVD cause) after the PREDICT risk score was recorded (obtained by linkage with government databases). Observed rates were extrapolated to 5 years, assuming constant incidence.

Main results

Over the follow-up period, 1216 patients had ⩾1 CVD event, 42% of whom had a history of CVD. Extrapolated mean 5-year event rates were 5.2% in patients without CVD at baseline and 29% in those with CVD. The table shows observed rates by predicted risk group.

PREDICT risk score to predict 5-year risk of cardiovascular disease (CVD) events in primary care patients with or without a history of CVD*

Conclusions

Risk of cardiovascular disease (CVD) events increased with predicted risk category in primary care patients both with and without a history of CVD. Absolute risks were about 20 percentage points higher for patients with CVD, after adjusting for traditional risk factors.

A modified version of this abstract appears in Evidence-Based Medicine and ACP Journal Club in Ann Intern Med.

Abstracted from

Kerr AJ, Broad J, Wells S, et al. Should the first priority in cardiovascular risk management be those with prior cardiovascular disease? Heart 2009;95:125–9.

Clinical impact ratings: Cardiology 5/7; Family/general practice 5/7; General/internal medicine 5/7

Footnotes

  • Source of funding Health Research Council.

Commentary

All people with a history of CVD are not at the same risk of a future CV event. As shown by Kerr et al, a risk score such as PREDICT can be used to estimate the 5-year risk of an event in primary care patients. Modification of CVD risk factors reduces risk of future CVD events and mortality in people with previous CVD. Evidence for benefit is strongest for statins, smoking cessation, antiplatelet agents, β-blockers, and angiotensin-converting enzyme inhibitors.1

There is a large body of published research on secondary prevention in patients with CVD, focusing on optimal combinations of therapy, suboptimal use of known therapies, and guideline concordance. Barriers to optimal therapy occur at patient, healthcare provider, and system levels; there is still much we do not know about reducing patient barriers. Even patients who have been adherent to medication regimens for a long time are at risk of discontinuing them.2

Most interventions to increase patient concordance with risk factor reduction regimens have been shown to have only modest effects.3 Health belief models suggest that patient understanding of the probability and severity of an event and the potential benefits of therapy might provide motivation to follow a risk reduction regimen. Moderate effect sizes have been reported in studies using educational interventions to improve patient adherence.3 Based on their findings, Kerr et al suggested this as a possible use of the PREDICT tool in patients with previous CVD: “patients may also find it useful and motivating to know their numerical risk.” Instead of assuming that all patients with previous CVD are at the same high risk, the PREDICT tool could be used on a regular basis to determine current risk, foster conversation between patients and healthcare providers about risk reduction, and monitor changes in risk, potentially increasing use of optimal risk-reducing therapies by both healthcare providers and patients.

References

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