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Cardiovascular diseases and the search for more evidence

Ami Banerjee
Last edited 20th March 2013

Daniel Day Lewis won an Oscar this year for his depiction of Abraham Lincoln’s role in the abolition of slavery in the USA. As I watched Lincoln on the plane crossing the Atlantic, I wondered how many inequalities still exist in health and whether laws are the best way to reduce or abolish these inequalities.

Looking at just cardiovascular diseases, inequalities have been highlighted at local, regional, national and international levels, whether on the basis of gender, age, socioeconomic status or race. We have known about the major risk factors which cause cardiovascular disease for over 50 years, and yet some of these inequalities still pose significant challenges in many parts of the globe. An example from the UK is the recent study showing regional variations in mortality from cardiovascular disease in each electoral ward.

So do we not have enough evidence to act? Do we need to keep producing more research to show that inequalities and variations still exist? Of course, the answer is that we need to keep producing evidence, not just to understand the causes, “the causes of the causes” and in order to plan the best strategies to tackle these inequalities. Moreover, the evidence needs to be presented in new ways to reach the hearts and minds of policymakers in order to enact change.

In Circulation this week, Ezatti and colleagues consider the effect of macroeconomic changes on cardiovascular risk factors over time at the global level for hypertension, diabetes, hypercholesterolaemia and obesity. At the country level, systolic blood pressure, total cholesterol and body-mass index were positively associated with gross domestic product (GDP) and Western diet in 1980, whereas only total cholesterol remained positively associated with GDP in 2008. In an accompanying editorial, I make the point that existing surveillance systems for cardiovascular disease and its risk factors at global level are inadequate. This week, I am at the American Heart Association Cardiovascular Epidemiology and Prevention Meeting in New Orleans, learning about new data and new ways of presenting the data regarding cardiovascular diseases. Relating changes in cardiovascular disease to economic and macroeconomic change seems a promising strategy to get the attention of policymakers.

A Risky Business

Kamal Mahtani
Last edited 17th August 2011

A great deal of General Practice is essentially about managing risk. Every time a patient walks through your consulting door you are basically thinking “what are the chances of this patient coming to harm from what they are about to tell me?” Some patients will fall into a category of “high risk” needing immediate or quick treatment. Others may have a degree of risk that necessitates either further investigations or monitoring. Or there may be low risk cases which can be assessed, reassured or treated. This is all supposed to happen within 10 minutes.

But how far should we go to convey that risk to our patients and what is the best way to do it? A recent scenario at my practice made me pause for thought. The cardiologists had advised us to have a discussion with a patient on the merits and risks of aspirin versus warfarin for their atrial fibrillation.

How could that conversation go?

“Right Mr X, the cardiologists have written to me and asked me to help you decide between warfarin or aspirin. They mention in the letter that the risk of you now having a stroke is about 6% per year, however aspirin reduces that risk by about 25% but with warfarin there is about a 45% risk reduction. However, the number needed to treat with warfarin is 37, but bear in mind that warfarin increases the annual absolute risk of major haemorrhage by 2%, so it’s up to you, which one would you prefer? ”

“umm…I’m sorry Doctor, I didn’t understand all of that”

“No neither did I”

So how should we explain risk to patients? The 2002 BMJ clinical review “Explaining risks: turning numerical data into meaningful pictures” by Edwards and colleagues is certainly worth a read. More recently, a study in the Annals of Family Medicine also tried to answer the question. The group surveyed 934 consecutive patients drawn from family practitioners’ waiting rooms in Auckland, New Zealand. Patients were asked to rate how much various modes of communicating the benefits of therapy, to their 5-year CVD risk score, would encourage them to take medication daily. The modes offered to them included: relative risk, absolute risk, odds, number needed to treat, and natural frequencies. The same information was presented in 2 pictorial forms (bar graphs and 10 × 10 people charts). Most patients (61.8%) preferred a doctor to give an opinion than to explain using either numbers or pictures. More than half also preferred a pictorial presentation to numbers; and of the numerical presentations patients found relative risk reduction most encouraging, with absolute risk reduction rated second overall and numbers needed to treat (NNT) the least likely to be persuasive to take their medication.

So should this mean to our practice? Remember EBM is the integration of the best clinical practice, personal expertise and individual patient preference. The latter component is dependent upon the patient fully understanding the risks and benefits of treatment so that a shared management plan can be reached. Having an idea of what those risks mean ourselves is the first step but finding the best way to convey it to our individual patients in as simplest way as possible is perhaps the bigger challenge.

Understanding clinical risk scores in 4 days. Day 2: Prediction

Ami Banerjee
Last edited 20th April 2011

As well as diagnosing disease, it may be important to predict which patients need more investigation or more treatment. The term, “clinical prediction rule”, is often used to describe the best combination of medical signs, symptoms, and other findings in predicting the probability of a specific disease or outcome. Therefore, they can be used in diagnosis as in the last article, or they can estimate the risk of a specific outcome, and therefore help in decision-making.

To form prediction rules, researchers look at a group of patients suspected of having a specific disease or outcome. They then compare the value of clinical findings available to the clinician versus the results of more intensive testing or the results of delayed clinical follow up.

Previous studies have shown that giving clinicians information in the form of risk scores in isolation may not influence clinical practice. GPs routinely underuse risk scores and have suggested that computerized risk prediction for multiple diseases simultaneously and the integration of lifestyle recommendations may improve their uptake.

Clinical prediction rules are most effective when implemented as part of a pathway within hospital policies and guidelines, as shown in the case of pneumonia and the more rigorously the rules are implemented, the more successful they are at improving outcomes.

The CHADS(2) score is an example of a clinical risk score to help in deciding whether to start warfarin in patients with atrial fibrillation to prevent stroke. Patients are classified by their clinical history, getting 1 point each for Congestive cardiac failure, Hypertension, Age>75 years, Diabetes and 2 points for Stroke. Patients with scores of 2 or more should get warfarin, as long as they have no contraindications. A recent improvement on this score, the CHA(2)DS(2)-VASc has added 3 additional factors (history of vascular disase, sex and age 65-74) to improve the prediction of risk so that only people who are really at negligible risk of stroke do not get warfarin.

Other examples of risk scores used in this way are Wells scores for deep vein thrombosis and pulmonary embolism and CURB-65 in pneumonia.

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